Phenotyping mutants from GWAS study for identification of early responses to Salt Stress - Salt Stress Experiment
This notebook is constructed to examine the phenotypes of the mutant lines, in genes identified to be important for early responses to salt stress in Arabidopsis HapMap population. The output was generated by high-throughput phenotyping platform at KAUST.
The data is composed of Arabidopsis plants grown under control and salt stress conditions. The raw data used for this pipeline is available here
libraries used are:
library("ggplot2")
library("doBy")
library("reshape2")
library(ggbeeswarm)
library(ggpubr)## Loading required package: magrittrlibrary(cowplot)##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legendData import
Let’s start by making sure that our data is loaded correctly from the correct folder
RGB data:
# Where is your working directory (wd) at the moment?
getwd()## [1] "/Users/magdalena/Dropbox/DataAndAnalysis/PSI/BIG Salt 4/Analysis"# Change wd into the location you have your .csv files stored:
setwd("/Users/magdalena/Dropbox/DataAndAnalysis/PSI/BIG Salt 4/Analysis/")
list.files()## [1] "20190904_Analysis.R"
## [2] "20190904_Raw_analysis"
## [3] "20191015_Analysis.R"
## [4] "20191016_Analysis.R"
## [5] "20200119_Analysis_for_real.Rmd"
## [6] "20200120_Analysis_for_real.Rmd"
## [7] "20200125_Analysis_for_real.Rmd"
## [8] "20200318_Analysis_for_real.Rmd"
## [9] "20200422_Analysis_TDNA_lines_FvFm_QYmax.Rmd"
## [10] "all_growth_clean.pdf"
## [11] "all_growth_rate_int1_clean.pdf"
## [12] "Area_most_interesting_mutants.pdf"
## [13] "at1g64270-2 _Area_raw_data.pdf"
## [14] "at1g64280-1 _Area_raw_data.pdf"
## [15] "at1g64290-1 _Area_raw_data.pdf"
## [16] "at1g64290-2 _Area_raw_data.pdf"
## [17] "at1g64290-3 _Area_raw_data.pdf"
## [18] "at1g64290-4 _Area_raw_data.pdf"
## [19] "at1g64295-1 _Area_raw_data.pdf"
## [20] "at1g64295-2 _Area_raw_data.pdf"
## [21] "at1g64300-1 _Area_raw_data.pdf"
## [22] "at1g64300-2 _Area_raw_data.pdf"
## [23] "at1g64300-3 _Area_raw_data.pdf"
## [24] "at1g64300-4 _Area_raw_data.pdf"
## [25] "at1g64320-1 _Area_raw_data.pdf"
## [26] "at5g64920-1 _Area_raw_data.pdf"
## [27] "at5g64920-2 _Area_raw_data.pdf"
## [28] "Clean_Growth_factors_and_SIIT.csv"
## [29] "coding_Noha.csv"
## [30] "Col-0 _Area_raw_data.pdf"
## [31] "COP1 graphs"
## [32] "COP1 locus"
## [33] "COP1_ Fm _ 1 _das.pdf"
## [34] "COP1_ Fm _ 10 _das.pdf"
## [35] "COP1_ Fm _ 11 _das.pdf"
## [36] "COP1_ Fm _ 2 _das.pdf"
## [37] "COP1_ Fm _ 3 _das.pdf"
## [38] "COP1_ Fm _ 4 _das.pdf"
## [39] "COP1_ Fm _ 5 _das.pdf"
## [40] "COP1_ Fm _ 6 _das.pdf"
## [41] "COP1_ Fm _ 7 _das.pdf"
## [42] "COP1_ Fm _ 8 _das.pdf"
## [43] "COP1_ Fm _ 9 _das.pdf"
## [44] "COP1_ Fm_Lss1 _ 1 _das.pdf"
## [45] "COP1_ Fm_Lss1 _ 10 _das.pdf"
## [46] "COP1_ Fm_Lss1 _ 11 _das.pdf"
## [47] "COP1_ Fm_Lss1 _ 2 _das.pdf"
## [48] "COP1_ Fm_Lss1 _ 3 _das.pdf"
## [49] "COP1_ Fm_Lss1 _ 4 _das.pdf"
## [50] "COP1_ Fm_Lss1 _ 5 _das.pdf"
## [51] "COP1_ Fm_Lss1 _ 6 _das.pdf"
## [52] "COP1_ Fm_Lss1 _ 7 _das.pdf"
## [53] "COP1_ Fm_Lss1 _ 8 _das.pdf"
## [54] "COP1_ Fm_Lss1 _ 9 _das.pdf"
## [55] "COP1_ Fm_Lss2 _ 1 _das.pdf"
## [56] "COP1_ Fm_Lss2 _ 10 _das.pdf"
## [57] "COP1_ Fm_Lss2 _ 11 _das.pdf"
## [58] "COP1_ Fm_Lss2 _ 2 _das.pdf"
## [59] "COP1_ Fm_Lss2 _ 3 _das.pdf"
## [60] "COP1_ Fm_Lss2 _ 4 _das.pdf"
## [61] "COP1_ Fm_Lss2 _ 5 _das.pdf"
## [62] "COP1_ Fm_Lss2 _ 6 _das.pdf"
## [63] "COP1_ Fm_Lss2 _ 7 _das.pdf"
## [64] "COP1_ Fm_Lss2 _ 8 _das.pdf"
## [65] "COP1_ Fm_Lss2 _ 9 _das.pdf"
## [66] "COP1_ Fm_Lss3 _ 1 _das.pdf"
## [67] "COP1_ Fm_Lss3 _ 10 _das.pdf"
## [68] "COP1_ Fm_Lss3 _ 11 _das.pdf"
## [69] "COP1_ Fm_Lss3 _ 2 _das.pdf"
## [70] "COP1_ Fm_Lss3 _ 3 _das.pdf"
## [71] "COP1_ Fm_Lss3 _ 4 _das.pdf"
## [72] "COP1_ Fm_Lss3 _ 5 _das.pdf"
## [73] "COP1_ Fm_Lss3 _ 6 _das.pdf"
## [74] "COP1_ Fm_Lss3 _ 7 _das.pdf"
## [75] "COP1_ Fm_Lss3 _ 8 _das.pdf"
## [76] "COP1_ Fm_Lss3 _ 9 _das.pdf"
## [77] "COP1_ Fm_Lss4 _ 1 _das.pdf"
## [78] "COP1_ Fm_Lss4 _ 10 _das.pdf"
## [79] "COP1_ Fm_Lss4 _ 11 _das.pdf"
## [80] "COP1_ Fm_Lss4 _ 2 _das.pdf"
## [81] "COP1_ Fm_Lss4 _ 3 _das.pdf"
## [82] "COP1_ Fm_Lss4 _ 4 _das.pdf"
## [83] "COP1_ Fm_Lss4 _ 5 _das.pdf"
## [84] "COP1_ Fm_Lss4 _ 6 _das.pdf"
## [85] "COP1_ Fm_Lss4 _ 7 _das.pdf"
## [86] "COP1_ Fm_Lss4 _ 8 _das.pdf"
## [87] "COP1_ Fm_Lss4 _ 9 _das.pdf"
## [88] "COP1_ Fm_Lss5 _ 1 _das.pdf"
## [89] "COP1_ Fm_Lss5 _ 10 _das.pdf"
## [90] "COP1_ Fm_Lss5 _ 11 _das.pdf"
## [91] "COP1_ Fm_Lss5 _ 2 _das.pdf"
## [92] "COP1_ Fm_Lss5 _ 3 _das.pdf"
## [93] "COP1_ Fm_Lss5 _ 4 _das.pdf"
## [94] "COP1_ Fm_Lss5 _ 5 _das.pdf"
## [95] "COP1_ Fm_Lss5 _ 6 _das.pdf"
## [96] "COP1_ Fm_Lss5 _ 7 _das.pdf"
## [97] "COP1_ Fm_Lss5 _ 8 _das.pdf"
## [98] "COP1_ Fm_Lss5 _ 9 _das.pdf"
## [99] "COP1_ Fm_Lss6 _ 1 _das.pdf"
## [100] "COP1_ Fm_Lss6 _ 10 _das.pdf"
## [101] "COP1_ Fm_Lss6 _ 11 _das.pdf"
## [102] "COP1_ Fm_Lss6 _ 2 _das.pdf"
## [103] "COP1_ Fm_Lss6 _ 3 _das.pdf"
## [104] "COP1_ Fm_Lss6 _ 4 _das.pdf"
## [105] "COP1_ Fm_Lss6 _ 5 _das.pdf"
## [106] "COP1_ Fm_Lss6 _ 6 _das.pdf"
## [107] "COP1_ Fm_Lss6 _ 7 _das.pdf"
## [108] "COP1_ Fm_Lss6 _ 8 _das.pdf"
## [109] "COP1_ Fm_Lss6 _ 9 _das.pdf"
## [110] "COP1_ Fo _ 1 _das.pdf"
## [111] "COP1_ Fo _ 10 _das.pdf"
## [112] "COP1_ Fo _ 11 _das.pdf"
## [113] "COP1_ Fo _ 2 _das.pdf"
## [114] "COP1_ Fo _ 3 _das.pdf"
## [115] "COP1_ Fo _ 4 _das.pdf"
## [116] "COP1_ Fo _ 5 _das.pdf"
## [117] "COP1_ Fo _ 6 _das.pdf"
## [118] "COP1_ Fo _ 7 _das.pdf"
## [119] "COP1_ Fo _ 8 _das.pdf"
## [120] "COP1_ Fo _ 9 _das.pdf"
## [121] "COP1_ Fo_Lss1 _ 1 _das.pdf"
## [122] "COP1_ Fo_Lss1 _ 10 _das.pdf"
## [123] "COP1_ Fo_Lss1 _ 11 _das.pdf"
## [124] "COP1_ Fo_Lss1 _ 2 _das.pdf"
## [125] "COP1_ Fo_Lss1 _ 3 _das.pdf"
## [126] "COP1_ Fo_Lss1 _ 4 _das.pdf"
## [127] "COP1_ Fo_Lss1 _ 5 _das.pdf"
## [128] "COP1_ Fo_Lss1 _ 6 _das.pdf"
## [129] "COP1_ Fo_Lss1 _ 7 _das.pdf"
## [130] "COP1_ Fo_Lss1 _ 8 _das.pdf"
## [131] "COP1_ Fo_Lss1 _ 9 _das.pdf"
## [132] "COP1_ Fo_Lss2 _ 1 _das.pdf"
## [133] "COP1_ Fo_Lss2 _ 10 _das.pdf"
## [134] "COP1_ Fo_Lss2 _ 11 _das.pdf"
## [135] "COP1_ Fo_Lss2 _ 2 _das.pdf"
## [136] "COP1_ Fo_Lss2 _ 3 _das.pdf"
## [137] "COP1_ Fo_Lss2 _ 4 _das.pdf"
## [138] "COP1_ Fo_Lss2 _ 5 _das.pdf"
## [139] "COP1_ Fo_Lss2 _ 6 _das.pdf"
## [140] "COP1_ Fo_Lss2 _ 7 _das.pdf"
## [141] "COP1_ Fo_Lss2 _ 8 _das.pdf"
## [142] "COP1_ Fo_Lss2 _ 9 _das.pdf"
## [143] "COP1_ Fo_Lss3 _ 1 _das.pdf"
## [144] "COP1_ Fo_Lss3 _ 10 _das.pdf"
## [145] "COP1_ Fo_Lss3 _ 11 _das.pdf"
## [146] "COP1_ Fo_Lss3 _ 2 _das.pdf"
## [147] "COP1_ Fo_Lss3 _ 3 _das.pdf"
## [148] "COP1_ Fo_Lss3 _ 4 _das.pdf"
## [149] "COP1_ Fo_Lss3 _ 5 _das.pdf"
## [150] "COP1_ Fo_Lss3 _ 6 _das.pdf"
## [151] "COP1_ Fo_Lss3 _ 7 _das.pdf"
## [152] "COP1_ Fo_Lss3 _ 8 _das.pdf"
## [153] "COP1_ Fo_Lss3 _ 9 _das.pdf"
## [154] "COP1_ Fo_Lss4 _ 1 _das.pdf"
## [155] "COP1_ Fo_Lss4 _ 10 _das.pdf"
## [156] "COP1_ Fo_Lss4 _ 11 _das.pdf"
## [157] "COP1_ Fo_Lss4 _ 2 _das.pdf"
## [158] "COP1_ Fo_Lss4 _ 3 _das.pdf"
## [159] "COP1_ Fo_Lss4 _ 4 _das.pdf"
## [160] "COP1_ Fo_Lss4 _ 5 _das.pdf"
## [161] "COP1_ Fo_Lss4 _ 6 _das.pdf"
## [162] "COP1_ Fo_Lss4 _ 7 _das.pdf"
## [163] "COP1_ Fo_Lss4 _ 8 _das.pdf"
## [164] "COP1_ Fo_Lss4 _ 9 _das.pdf"
## [165] "COP1_ Fo_Lss5 _ 1 _das.pdf"
## [166] "COP1_ Fo_Lss5 _ 10 _das.pdf"
## [167] "COP1_ Fo_Lss5 _ 11 _das.pdf"
## [168] "COP1_ Fo_Lss5 _ 2 _das.pdf"
## [169] "COP1_ Fo_Lss5 _ 3 _das.pdf"
## [170] "COP1_ Fo_Lss5 _ 4 _das.pdf"
## [171] "COP1_ Fo_Lss5 _ 5 _das.pdf"
## [172] "COP1_ Fo_Lss5 _ 6 _das.pdf"
## [173] "COP1_ Fo_Lss5 _ 7 _das.pdf"
## [174] "COP1_ Fo_Lss5 _ 8 _das.pdf"
## [175] "COP1_ Fo_Lss5 _ 9 _das.pdf"
## [176] "COP1_ Fo_Lss6 _ 1 _das.pdf"
## [177] "COP1_ Fo_Lss6 _ 10 _das.pdf"
## [178] "COP1_ Fo_Lss6 _ 11 _das.pdf"
## [179] "COP1_ Fo_Lss6 _ 2 _das.pdf"
## [180] "COP1_ Fo_Lss6 _ 3 _das.pdf"
## [181] "COP1_ Fo_Lss6 _ 4 _das.pdf"
## [182] "COP1_ Fo_Lss6 _ 5 _das.pdf"
## [183] "COP1_ Fo_Lss6 _ 6 _das.pdf"
## [184] "COP1_ Fo_Lss6 _ 7 _das.pdf"
## [185] "COP1_ Fo_Lss6 _ 8 _das.pdf"
## [186] "COP1_ Fo_Lss6 _ 9 _das.pdf"
## [187] "COP1_ Ft_Lss1 _ 1 _das.pdf"
## [188] "COP1_ Ft_Lss1 _ 10 _das.pdf"
## [189] "COP1_ Ft_Lss1 _ 11 _das.pdf"
## [190] "COP1_ Ft_Lss1 _ 2 _das.pdf"
## [191] "COP1_ Ft_Lss1 _ 3 _das.pdf"
## [192] "COP1_ Ft_Lss1 _ 4 _das.pdf"
## [193] "COP1_ Ft_Lss1 _ 5 _das.pdf"
## [194] "COP1_ Ft_Lss1 _ 6 _das.pdf"
## [195] "COP1_ Ft_Lss1 _ 7 _das.pdf"
## [196] "COP1_ Ft_Lss1 _ 8 _das.pdf"
## [197] "COP1_ Ft_Lss1 _ 9 _das.pdf"
## [198] "COP1_ Ft_Lss2 _ 1 _das.pdf"
## [199] "COP1_ Ft_Lss2 _ 10 _das.pdf"
## [200] "COP1_ Ft_Lss2 _ 11 _das.pdf"
## [201] "COP1_ Ft_Lss2 _ 2 _das.pdf"
## [202] "COP1_ Ft_Lss2 _ 3 _das.pdf"
## [203] "COP1_ Ft_Lss2 _ 4 _das.pdf"
## [204] "COP1_ Ft_Lss2 _ 5 _das.pdf"
## [205] "COP1_ Ft_Lss2 _ 6 _das.pdf"
## [206] "COP1_ Ft_Lss2 _ 7 _das.pdf"
## [207] "COP1_ Ft_Lss2 _ 8 _das.pdf"
## [208] "COP1_ Ft_Lss2 _ 9 _das.pdf"
## [209] "COP1_ Ft_Lss3 _ 1 _das.pdf"
## [210] "COP1_ Ft_Lss3 _ 10 _das.pdf"
## [211] "COP1_ Ft_Lss3 _ 11 _das.pdf"
## [212] "COP1_ Ft_Lss3 _ 2 _das.pdf"
## [213] "COP1_ Ft_Lss3 _ 3 _das.pdf"
## [214] "COP1_ Ft_Lss3 _ 4 _das.pdf"
## [215] "COP1_ Ft_Lss3 _ 5 _das.pdf"
## [216] "COP1_ Ft_Lss3 _ 6 _das.pdf"
## [217] "COP1_ Ft_Lss3 _ 7 _das.pdf"
## [218] "COP1_ Ft_Lss3 _ 8 _das.pdf"
## [219] "COP1_ Ft_Lss3 _ 9 _das.pdf"
## [220] "COP1_ Ft_Lss4 _ 1 _das.pdf"
## [221] "COP1_ Ft_Lss4 _ 10 _das.pdf"
## [222] "COP1_ Ft_Lss4 _ 11 _das.pdf"
## [223] "COP1_ Ft_Lss4 _ 2 _das.pdf"
## [224] "COP1_ Ft_Lss4 _ 3 _das.pdf"
## [225] "COP1_ Ft_Lss4 _ 4 _das.pdf"
## [226] "COP1_ Ft_Lss4 _ 5 _das.pdf"
## [227] "COP1_ Ft_Lss4 _ 6 _das.pdf"
## [228] "COP1_ Ft_Lss4 _ 7 _das.pdf"
## [229] "COP1_ Ft_Lss4 _ 8 _das.pdf"
## [230] "COP1_ Ft_Lss4 _ 9 _das.pdf"
## [231] "COP1_ Ft_Lss5 _ 1 _das.pdf"
## [232] "COP1_ Ft_Lss5 _ 10 _das.pdf"
## [233] "COP1_ Ft_Lss5 _ 11 _das.pdf"
## [234] "COP1_ Ft_Lss5 _ 2 _das.pdf"
## [235] "COP1_ Ft_Lss5 _ 3 _das.pdf"
## [236] "COP1_ Ft_Lss5 _ 4 _das.pdf"
## [237] "COP1_ Ft_Lss5 _ 5 _das.pdf"
## [238] "COP1_ Ft_Lss5 _ 6 _das.pdf"
## [239] "COP1_ Ft_Lss5 _ 7 _das.pdf"
## [240] "COP1_ Ft_Lss5 _ 8 _das.pdf"
## [241] "COP1_ Ft_Lss5 _ 9 _das.pdf"
## [242] "COP1_ Ft_Lss6 _ 1 _das.pdf"
## [243] "COP1_ Ft_Lss6 _ 10 _das.pdf"
## [244] "COP1_ Ft_Lss6 _ 11 _das.pdf"
## [245] "COP1_ Ft_Lss6 _ 2 _das.pdf"
## [246] "COP1_ Ft_Lss6 _ 3 _das.pdf"
## [247] "COP1_ Ft_Lss6 _ 4 _das.pdf"
## [248] "COP1_ Ft_Lss6 _ 5 _das.pdf"
## [249] "COP1_ Ft_Lss6 _ 6 _das.pdf"
## [250] "COP1_ Ft_Lss6 _ 7 _das.pdf"
## [251] "COP1_ Ft_Lss6 _ 8 _das.pdf"
## [252] "COP1_ Ft_Lss6 _ 9 _das.pdf"
## [253] "COP1_ Fv _ 1 _das.pdf"
## [254] "COP1_ Fv _ 10 _das.pdf"
## [255] "COP1_ Fv _ 11 _das.pdf"
## [256] "COP1_ Fv _ 2 _das.pdf"
## [257] "COP1_ Fv _ 3 _das.pdf"
## [258] "COP1_ Fv _ 4 _das.pdf"
## [259] "COP1_ Fv _ 5 _das.pdf"
## [260] "COP1_ Fv _ 6 _das.pdf"
## [261] "COP1_ Fv _ 7 _das.pdf"
## [262] "COP1_ Fv _ 8 _das.pdf"
## [263] "COP1_ Fv _ 9 _das.pdf"
## [264] "COP1_ Fv_Lss1 _ 1 _das.pdf"
## [265] "COP1_ Fv_Lss1 _ 10 _das.pdf"
## [266] "COP1_ Fv_Lss1 _ 11 _das.pdf"
## [267] "COP1_ Fv_Lss1 _ 2 _das.pdf"
## [268] "COP1_ Fv_Lss1 _ 3 _das.pdf"
## [269] "COP1_ Fv_Lss1 _ 4 _das.pdf"
## [270] "COP1_ Fv_Lss1 _ 5 _das.pdf"
## [271] "COP1_ Fv_Lss1 _ 6 _das.pdf"
## [272] "COP1_ Fv_Lss1 _ 7 _das.pdf"
## [273] "COP1_ Fv_Lss1 _ 8 _das.pdf"
## [274] "COP1_ Fv_Lss1 _ 9 _das.pdf"
## [275] "COP1_ Fv_Lss2 _ 1 _das.pdf"
## [276] "COP1_ Fv_Lss2 _ 10 _das.pdf"
## [277] "COP1_ Fv_Lss2 _ 11 _das.pdf"
## [278] "COP1_ Fv_Lss2 _ 2 _das.pdf"
## [279] "COP1_ Fv_Lss2 _ 3 _das.pdf"
## [280] "COP1_ Fv_Lss2 _ 4 _das.pdf"
## [281] "COP1_ Fv_Lss2 _ 5 _das.pdf"
## [282] "COP1_ Fv_Lss2 _ 6 _das.pdf"
## [283] "COP1_ Fv_Lss2 _ 7 _das.pdf"
## [284] "COP1_ Fv_Lss2 _ 8 _das.pdf"
## [285] "COP1_ Fv_Lss2 _ 9 _das.pdf"
## [286] "COP1_ Fv_Lss3 _ 1 _das.pdf"
## [287] "COP1_ Fv_Lss3 _ 10 _das.pdf"
## [288] "COP1_ Fv_Lss3 _ 11 _das.pdf"
## [289] "COP1_ Fv_Lss3 _ 2 _das.pdf"
## [290] "COP1_ Fv_Lss3 _ 3 _das.pdf"
## [291] "COP1_ Fv_Lss3 _ 4 _das.pdf"
## [292] "COP1_ Fv_Lss3 _ 5 _das.pdf"
## [293] "COP1_ Fv_Lss3 _ 6 _das.pdf"
## [294] "COP1_ Fv_Lss3 _ 7 _das.pdf"
## [295] "COP1_ Fv_Lss3 _ 8 _das.pdf"
## [296] "COP1_ Fv_Lss3 _ 9 _das.pdf"
## [297] "COP1_ Fv_Lss4 _ 1 _das.pdf"
## [298] "COP1_ Fv_Lss4 _ 10 _das.pdf"
## [299] "COP1_ Fv_Lss4 _ 11 _das.pdf"
## [300] "COP1_ Fv_Lss4 _ 2 _das.pdf"
## [301] "COP1_ Fv_Lss4 _ 3 _das.pdf"
## [302] "COP1_ Fv_Lss4 _ 4 _das.pdf"
## [303] "COP1_ Fv_Lss4 _ 5 _das.pdf"
## [304] "COP1_ Fv_Lss4 _ 6 _das.pdf"
## [305] "COP1_ Fv_Lss4 _ 7 _das.pdf"
## [306] "COP1_ Fv_Lss4 _ 8 _das.pdf"
## [307] "COP1_ Fv_Lss4 _ 9 _das.pdf"
## [308] "COP1_ Fv_Lss5 _ 1 _das.pdf"
## [309] "COP1_ Fv_Lss5 _ 10 _das.pdf"
## [310] "COP1_ Fv_Lss5 _ 11 _das.pdf"
## [311] "COP1_ Fv_Lss5 _ 2 _das.pdf"
## [312] "COP1_ Fv_Lss5 _ 3 _das.pdf"
## [313] "COP1_ Fv_Lss5 _ 4 _das.pdf"
## [314] "COP1_ Fv_Lss5 _ 5 _das.pdf"
## [315] "COP1_ Fv_Lss5 _ 6 _das.pdf"
## [316] "COP1_ Fv_Lss5 _ 7 _das.pdf"
## [317] "COP1_ Fv_Lss5 _ 8 _das.pdf"
## [318] "COP1_ Fv_Lss5 _ 9 _das.pdf"
## [319] "COP1_ Fv_Lss6 _ 1 _das.pdf"
## [320] "COP1_ Fv_Lss6 _ 10 _das.pdf"
## [321] "COP1_ Fv_Lss6 _ 11 _das.pdf"
## [322] "COP1_ Fv_Lss6 _ 2 _das.pdf"
## [323] "COP1_ Fv_Lss6 _ 3 _das.pdf"
## [324] "COP1_ Fv_Lss6 _ 4 _das.pdf"
## [325] "COP1_ Fv_Lss6 _ 5 _das.pdf"
## [326] "COP1_ Fv_Lss6 _ 6 _das.pdf"
## [327] "COP1_ Fv_Lss6 _ 7 _das.pdf"
## [328] "COP1_ Fv_Lss6 _ 8 _das.pdf"
## [329] "COP1_ Fv_Lss6 _ 9 _das.pdf"
## [330] "COP1_ FvFm_Lss1 _ 1 _das.pdf"
## [331] "COP1_ FvFm_Lss1 _ 10 _das.pdf"
## [332] "COP1_ FvFm_Lss1 _ 11 _das.pdf"
## [333] "COP1_ FvFm_Lss1 _ 2 _das.pdf"
## [334] "COP1_ FvFm_Lss1 _ 3 _das.pdf"
## [335] "COP1_ FvFm_Lss1 _ 4 _das.pdf"
## [336] "COP1_ FvFm_Lss1 _ 5 _das.pdf"
## [337] "COP1_ FvFm_Lss1 _ 6 _das.pdf"
## [338] "COP1_ FvFm_Lss1 _ 7 _das.pdf"
## [339] "COP1_ FvFm_Lss1 _ 8 _das.pdf"
## [340] "COP1_ FvFm_Lss1 _ 9 _das.pdf"
## [341] "COP1_ FvFm_Lss2 _ 1 _das.pdf"
## [342] "COP1_ FvFm_Lss2 _ 10 _das.pdf"
## [343] "COP1_ FvFm_Lss2 _ 11 _das.pdf"
## [344] "COP1_ FvFm_Lss2 _ 2 _das.pdf"
## [345] "COP1_ FvFm_Lss2 _ 3 _das.pdf"
## [346] "COP1_ FvFm_Lss2 _ 4 _das.pdf"
## [347] "COP1_ FvFm_Lss2 _ 5 _das.pdf"
## [348] "COP1_ FvFm_Lss2 _ 6 _das.pdf"
## [349] "COP1_ FvFm_Lss2 _ 7 _das.pdf"
## [350] "COP1_ FvFm_Lss2 _ 8 _das.pdf"
## [351] "COP1_ FvFm_Lss2 _ 9 _das.pdf"
## [352] "COP1_ FvFm_Lss3 _ 1 _das.pdf"
## [353] "COP1_ FvFm_Lss3 _ 10 _das.pdf"
## [354] "COP1_ FvFm_Lss3 _ 11 _das.pdf"
## [355] "COP1_ FvFm_Lss3 _ 2 _das.pdf"
## [356] "COP1_ FvFm_Lss3 _ 3 _das.pdf"
## [357] "COP1_ FvFm_Lss3 _ 4 _das.pdf"
## [358] "COP1_ FvFm_Lss3 _ 5 _das.pdf"
## [359] "COP1_ FvFm_Lss3 _ 6 _das.pdf"
## [360] "COP1_ FvFm_Lss3 _ 7 _das.pdf"
## [361] "COP1_ FvFm_Lss3 _ 8 _das.pdf"
## [362] "COP1_ FvFm_Lss3 _ 9 _das.pdf"
## [363] "COP1_ FvFm_Lss4 _ 1 _das.pdf"
## [364] "COP1_ FvFm_Lss4 _ 10 _das.pdf"
## [365] "COP1_ FvFm_Lss4 _ 11 _das.pdf"
## [366] "COP1_ FvFm_Lss4 _ 2 _das.pdf"
## [367] "COP1_ FvFm_Lss4 _ 3 _das.pdf"
## [368] "COP1_ FvFm_Lss4 _ 4 _das.pdf"
## [369] "COP1_ FvFm_Lss4 _ 5 _das.pdf"
## [370] "COP1_ FvFm_Lss4 _ 6 _das.pdf"
## [371] "COP1_ FvFm_Lss4 _ 7 _das.pdf"
## [372] "COP1_ FvFm_Lss4 _ 8 _das.pdf"
## [373] "COP1_ FvFm_Lss4 _ 9 _das.pdf"
## [374] "COP1_ FvFm_Lss5 _ 1 _das.pdf"
## [375] "COP1_ FvFm_Lss5 _ 10 _das.pdf"
## [376] "COP1_ FvFm_Lss5 _ 11 _das.pdf"
## [377] "COP1_ FvFm_Lss5 _ 2 _das.pdf"
## [378] "COP1_ FvFm_Lss5 _ 3 _das.pdf"
## [379] "COP1_ FvFm_Lss5 _ 4 _das.pdf"
## [380] "COP1_ FvFm_Lss5 _ 5 _das.pdf"
## [381] "COP1_ FvFm_Lss5 _ 6 _das.pdf"
## [382] "COP1_ FvFm_Lss5 _ 7 _das.pdf"
## [383] "COP1_ FvFm_Lss5 _ 8 _das.pdf"
## [384] "COP1_ FvFm_Lss5 _ 9 _das.pdf"
## [385] "COP1_ FvFm_Lss6 _ 1 _das.pdf"
## [386] "COP1_ FvFm_Lss6 _ 10 _das.pdf"
## [387] "COP1_ FvFm_Lss6 _ 11 _das.pdf"
## [388] "COP1_ FvFm_Lss6 _ 2 _das.pdf"
## [389] "COP1_ FvFm_Lss6 _ 3 _das.pdf"
## [390] "COP1_ FvFm_Lss6 _ 4 _das.pdf"
## [391] "COP1_ FvFm_Lss6 _ 5 _das.pdf"
## [392] "COP1_ FvFm_Lss6 _ 6 _das.pdf"
## [393] "COP1_ FvFm_Lss6 _ 7 _das.pdf"
## [394] "COP1_ FvFm_Lss6 _ 8 _das.pdf"
## [395] "COP1_ FvFm_Lss6 _ 9 _das.pdf"
## [396] "COP1_ NPQ_Lss1 _ 1 _das.pdf"
## [397] "COP1_ NPQ_Lss1 _ 10 _das.pdf"
## [398] "COP1_ NPQ_Lss1 _ 11 _das.pdf"
## [399] "COP1_ NPQ_Lss1 _ 2 _das.pdf"
## [400] "COP1_ NPQ_Lss1 _ 3 _das.pdf"
## [401] "COP1_ NPQ_Lss1 _ 4 _das.pdf"
## [402] "COP1_ NPQ_Lss1 _ 5 _das.pdf"
## [403] "COP1_ NPQ_Lss1 _ 6 _das.pdf"
## [404] "COP1_ NPQ_Lss1 _ 7 _das.pdf"
## [405] "COP1_ NPQ_Lss1 _ 8 _das.pdf"
## [406] "COP1_ NPQ_Lss1 _ 9 _das.pdf"
## [407] "COP1_ NPQ_Lss2 _ 1 _das.pdf"
## [408] "COP1_ NPQ_Lss2 _ 10 _das.pdf"
## [409] "COP1_ NPQ_Lss2 _ 11 _das.pdf"
## [410] "COP1_ NPQ_Lss2 _ 2 _das.pdf"
## [411] "COP1_ NPQ_Lss2 _ 3 _das.pdf"
## [412] "COP1_ NPQ_Lss2 _ 4 _das.pdf"
## [413] "COP1_ NPQ_Lss2 _ 5 _das.pdf"
## [414] "COP1_ NPQ_Lss2 _ 6 _das.pdf"
## [415] "COP1_ NPQ_Lss2 _ 7 _das.pdf"
## [416] "COP1_ NPQ_Lss2 _ 8 _das.pdf"
## [417] "COP1_ NPQ_Lss2 _ 9 _das.pdf"
## [418] "COP1_ NPQ_Lss3 _ 1 _das.pdf"
## [419] "COP1_ NPQ_Lss3 _ 10 _das.pdf"
## [420] "COP1_ NPQ_Lss3 _ 11 _das.pdf"
## [421] "COP1_ NPQ_Lss3 _ 2 _das.pdf"
## [422] "COP1_ NPQ_Lss3 _ 3 _das.pdf"
## [423] "COP1_ NPQ_Lss3 _ 4 _das.pdf"
## [424] "COP1_ NPQ_Lss3 _ 5 _das.pdf"
## [425] "COP1_ NPQ_Lss3 _ 6 _das.pdf"
## [426] "COP1_ NPQ_Lss3 _ 7 _das.pdf"
## [427] "COP1_ NPQ_Lss3 _ 8 _das.pdf"
## [428] "COP1_ NPQ_Lss3 _ 9 _das.pdf"
## [429] "COP1_ NPQ_Lss4 _ 1 _das.pdf"
## [430] "COP1_ NPQ_Lss4 _ 10 _das.pdf"
## [431] "COP1_ NPQ_Lss4 _ 11 _das.pdf"
## [432] "COP1_ NPQ_Lss4 _ 2 _das.pdf"
## [433] "COP1_ NPQ_Lss4 _ 3 _das.pdf"
## [434] "COP1_ NPQ_Lss4 _ 4 _das.pdf"
## [435] "COP1_ NPQ_Lss4 _ 5 _das.pdf"
## [436] "COP1_ NPQ_Lss4 _ 6 _das.pdf"
## [437] "COP1_ NPQ_Lss4 _ 7 _das.pdf"
## [438] "COP1_ NPQ_Lss4 _ 8 _das.pdf"
## [439] "COP1_ NPQ_Lss4 _ 9 _das.pdf"
## [440] "COP1_ NPQ_Lss5 _ 1 _das.pdf"
## [441] "COP1_ NPQ_Lss5 _ 10 _das.pdf"
## [442] "COP1_ NPQ_Lss5 _ 11 _das.pdf"
## [443] "COP1_ NPQ_Lss5 _ 2 _das.pdf"
## [444] "COP1_ NPQ_Lss5 _ 3 _das.pdf"
## [445] "COP1_ NPQ_Lss5 _ 4 _das.pdf"
## [446] "COP1_ NPQ_Lss5 _ 5 _das.pdf"
## [447] "COP1_ NPQ_Lss5 _ 6 _das.pdf"
## [448] "COP1_ NPQ_Lss5 _ 7 _das.pdf"
## [449] "COP1_ NPQ_Lss5 _ 8 _das.pdf"
## [450] "COP1_ NPQ_Lss5 _ 9 _das.pdf"
## [451] "COP1_ NPQ_Lss6 _ 1 _das.pdf"
## [452] "COP1_ NPQ_Lss6 _ 10 _das.pdf"
## [453] "COP1_ NPQ_Lss6 _ 11 _das.pdf"
## [454] "COP1_ NPQ_Lss6 _ 2 _das.pdf"
## [455] "COP1_ NPQ_Lss6 _ 3 _das.pdf"
## [456] "COP1_ NPQ_Lss6 _ 4 _das.pdf"
## [457] "COP1_ NPQ_Lss6 _ 5 _das.pdf"
## [458] "COP1_ NPQ_Lss6 _ 6 _das.pdf"
## [459] "COP1_ NPQ_Lss6 _ 7 _das.pdf"
## [460] "COP1_ NPQ_Lss6 _ 8 _das.pdf"
## [461] "COP1_ NPQ_Lss6 _ 9 _das.pdf"
## [462] "COP1_ qP_Lss1 _ 1 _das.pdf"
## [463] "COP1_ qP_Lss1 _ 10 _das.pdf"
## [464] "COP1_ qP_Lss1 _ 11 _das.pdf"
## [465] "COP1_ qP_Lss1 _ 2 _das.pdf"
## [466] "COP1_ qP_Lss1 _ 3 _das.pdf"
## [467] "COP1_ qP_Lss1 _ 4 _das.pdf"
## [468] "COP1_ qP_Lss1 _ 5 _das.pdf"
## [469] "COP1_ qP_Lss1 _ 6 _das.pdf"
## [470] "COP1_ qP_Lss1 _ 7 _das.pdf"
## [471] "COP1_ qP_Lss1 _ 8 _das.pdf"
## [472] "COP1_ qP_Lss1 _ 9 _das.pdf"
## [473] "COP1_ qP_Lss2 _ 1 _das.pdf"
## [474] "COP1_ qP_Lss2 _ 10 _das.pdf"
## [475] "COP1_ qP_Lss2 _ 11 _das.pdf"
## [476] "COP1_ qP_Lss2 _ 2 _das.pdf"
## [477] "COP1_ qP_Lss2 _ 3 _das.pdf"
## [478] "COP1_ qP_Lss2 _ 4 _das.pdf"
## [479] "COP1_ qP_Lss2 _ 5 _das.pdf"
## [480] "COP1_ qP_Lss2 _ 6 _das.pdf"
## [481] "COP1_ qP_Lss2 _ 7 _das.pdf"
## [482] "COP1_ qP_Lss2 _ 8 _das.pdf"
## [483] "COP1_ qP_Lss2 _ 9 _das.pdf"
## [484] "COP1_ qP_Lss3 _ 1 _das.pdf"
## [485] "COP1_ qP_Lss3 _ 10 _das.pdf"
## [486] "COP1_ qP_Lss3 _ 11 _das.pdf"
## [487] "COP1_ qP_Lss3 _ 2 _das.pdf"
## [488] "COP1_ qP_Lss3 _ 3 _das.pdf"
## [489] "COP1_ qP_Lss3 _ 4 _das.pdf"
## [490] "COP1_ qP_Lss3 _ 5 _das.pdf"
## [491] "COP1_ qP_Lss3 _ 6 _das.pdf"
## [492] "COP1_ qP_Lss3 _ 7 _das.pdf"
## [493] "COP1_ qP_Lss3 _ 8 _das.pdf"
## [494] "COP1_ qP_Lss3 _ 9 _das.pdf"
## [495] "COP1_ qP_Lss4 _ 1 _das.pdf"
## [496] "COP1_ qP_Lss4 _ 10 _das.pdf"
## [497] "COP1_ qP_Lss4 _ 11 _das.pdf"
## [498] "COP1_ qP_Lss4 _ 2 _das.pdf"
## [499] "COP1_ qP_Lss4 _ 3 _das.pdf"
## [500] "COP1_ qP_Lss4 _ 4 _das.pdf"
## [501] "COP1_ qP_Lss4 _ 5 _das.pdf"
## [502] "COP1_ qP_Lss4 _ 6 _das.pdf"
## [503] "COP1_ qP_Lss4 _ 7 _das.pdf"
## [504] "COP1_ qP_Lss4 _ 8 _das.pdf"
## [505] "COP1_ qP_Lss4 _ 9 _das.pdf"
## [506] "COP1_ qP_Lss5 _ 1 _das.pdf"
## [507] "COP1_ qP_Lss5 _ 10 _das.pdf"
## [508] "COP1_ qP_Lss5 _ 11 _das.pdf"
## [509] "COP1_ qP_Lss5 _ 2 _das.pdf"
## [510] "COP1_ qP_Lss5 _ 3 _das.pdf"
## [511] "COP1_ qP_Lss5 _ 4 _das.pdf"
## [512] "COP1_ qP_Lss5 _ 5 _das.pdf"
## [513] "COP1_ qP_Lss5 _ 6 _das.pdf"
## [514] "COP1_ qP_Lss5 _ 7 _das.pdf"
## [515] "COP1_ qP_Lss5 _ 8 _das.pdf"
## [516] "COP1_ qP_Lss5 _ 9 _das.pdf"
## [517] "COP1_ qP_Lss6 _ 1 _das.pdf"
## [518] "COP1_ qP_Lss6 _ 10 _das.pdf"
## [519] "COP1_ qP_Lss6 _ 11 _das.pdf"
## [520] "COP1_ qP_Lss6 _ 2 _das.pdf"
## [521] "COP1_ qP_Lss6 _ 3 _das.pdf"
## [522] "COP1_ qP_Lss6 _ 4 _das.pdf"
## [523] "COP1_ qP_Lss6 _ 5 _das.pdf"
## [524] "COP1_ qP_Lss6 _ 6 _das.pdf"
## [525] "COP1_ qP_Lss6 _ 7 _das.pdf"
## [526] "COP1_ qP_Lss6 _ 8 _das.pdf"
## [527] "COP1_ qP_Lss6 _ 9 _das.pdf"
## [528] "COP1_ QY_Lss1 _ 1 _das.pdf"
## [529] "COP1_ QY_Lss1 _ 10 _das.pdf"
## [530] "COP1_ QY_Lss1 _ 11 _das.pdf"
## [531] "COP1_ QY_Lss1 _ 2 _das.pdf"
## [532] "COP1_ QY_Lss1 _ 3 _das.pdf"
## [533] "COP1_ QY_Lss1 _ 4 _das.pdf"
## [534] "COP1_ QY_Lss1 _ 5 _das.pdf"
## [535] "COP1_ QY_Lss1 _ 6 _das.pdf"
## [536] "COP1_ QY_Lss1 _ 7 _das.pdf"
## [537] "COP1_ QY_Lss1 _ 8 _das.pdf"
## [538] "COP1_ QY_Lss1 _ 9 _das.pdf"
## [539] "COP1_ QY_Lss2 _ 1 _das.pdf"
## [540] "COP1_ QY_Lss2 _ 10 _das.pdf"
## [541] "COP1_ QY_Lss2 _ 11 _das.pdf"
## [542] "COP1_ QY_Lss2 _ 2 _das.pdf"
## [543] "COP1_ QY_Lss2 _ 3 _das.pdf"
## [544] "COP1_ QY_Lss2 _ 4 _das.pdf"
## [545] "COP1_ QY_Lss2 _ 5 _das.pdf"
## [546] "COP1_ QY_Lss2 _ 6 _das.pdf"
## [547] "COP1_ QY_Lss2 _ 7 _das.pdf"
## [548] "COP1_ QY_Lss2 _ 8 _das.pdf"
## [549] "COP1_ QY_Lss2 _ 9 _das.pdf"
## [550] "COP1_ QY_Lss3 _ 1 _das.pdf"
## [551] "COP1_ QY_Lss3 _ 10 _das.pdf"
## [552] "COP1_ QY_Lss3 _ 11 _das.pdf"
## [553] "COP1_ QY_Lss3 _ 2 _das.pdf"
## [554] "COP1_ QY_Lss3 _ 3 _das.pdf"
## [555] "COP1_ QY_Lss3 _ 4 _das.pdf"
## [556] "COP1_ QY_Lss3 _ 5 _das.pdf"
## [557] "COP1_ QY_Lss3 _ 6 _das.pdf"
## [558] "COP1_ QY_Lss3 _ 7 _das.pdf"
## [559] "COP1_ QY_Lss3 _ 8 _das.pdf"
## [560] "COP1_ QY_Lss3 _ 9 _das.pdf"
## [561] "COP1_ QY_Lss4 _ 1 _das.pdf"
## [562] "COP1_ QY_Lss4 _ 10 _das.pdf"
## [563] "COP1_ QY_Lss4 _ 11 _das.pdf"
## [564] "COP1_ QY_Lss4 _ 2 _das.pdf"
## [565] "COP1_ QY_Lss4 _ 3 _das.pdf"
## [566] "COP1_ QY_Lss4 _ 4 _das.pdf"
## [567] "COP1_ QY_Lss4 _ 5 _das.pdf"
## [568] "COP1_ QY_Lss4 _ 6 _das.pdf"
## [569] "COP1_ QY_Lss4 _ 7 _das.pdf"
## [570] "COP1_ QY_Lss4 _ 8 _das.pdf"
## [571] "COP1_ QY_Lss4 _ 9 _das.pdf"
## [572] "COP1_ QY_Lss5 _ 1 _das.pdf"
## [573] "COP1_ QY_Lss5 _ 10 _das.pdf"
## [574] "COP1_ QY_Lss5 _ 11 _das.pdf"
## [575] "COP1_ QY_Lss5 _ 2 _das.pdf"
## [576] "COP1_ QY_Lss5 _ 3 _das.pdf"
## [577] "COP1_ QY_Lss5 _ 4 _das.pdf"
## [578] "COP1_ QY_Lss5 _ 5 _das.pdf"
## [579] "COP1_ QY_Lss5 _ 6 _das.pdf"
## [580] "COP1_ QY_Lss5 _ 7 _das.pdf"
## [581] "COP1_ QY_Lss5 _ 8 _das.pdf"
## [582] "COP1_ QY_Lss5 _ 9 _das.pdf"
## [583] "COP1_ QY_Lss6 _ 1 _das.pdf"
## [584] "COP1_ QY_Lss6 _ 10 _das.pdf"
## [585] "COP1_ QY_Lss6 _ 11 _das.pdf"
## [586] "COP1_ QY_Lss6 _ 2 _das.pdf"
## [587] "COP1_ QY_Lss6 _ 3 _das.pdf"
## [588] "COP1_ QY_Lss6 _ 4 _das.pdf"
## [589] "COP1_ QY_Lss6 _ 5 _das.pdf"
## [590] "COP1_ QY_Lss6 _ 6 _das.pdf"
## [591] "COP1_ QY_Lss6 _ 7 _das.pdf"
## [592] "COP1_ QY_Lss6 _ 8 _das.pdf"
## [593] "COP1_ QY_Lss6 _ 9 _das.pdf"
## [594] "COP1_ QY_max _ 1 _das.pdf"
## [595] "COP1_ QY_max _ 10 _das.pdf"
## [596] "COP1_ QY_max _ 11 _das.pdf"
## [597] "COP1_ QY_max _ 2 _das.pdf"
## [598] "COP1_ QY_max _ 3 _das.pdf"
## [599] "COP1_ QY_max _ 4 _das.pdf"
## [600] "COP1_ QY_max _ 5 _das.pdf"
## [601] "COP1_ QY_max _ 6 _das.pdf"
## [602] "COP1_ QY_max _ 7 _das.pdf"
## [603] "COP1_ QY_max _ 8 _das.pdf"
## [604] "COP1_ QY_max _ 9 _das.pdf"
## [605] "COP1_Area_ 1 _das.pdf"
## [606] "COP1_Area_ 10 _das.pdf"
## [607] "COP1_Area_ 11 _das.pdf"
## [608] "COP1_Area_ 2 _das.pdf"
## [609] "COP1_Area_ 3 _das.pdf"
## [610] "COP1_Area_ 4 _das.pdf"
## [611] "COP1_Area_ 5 _das.pdf"
## [612] "COP1_Area_ 6 _das.pdf"
## [613] "COP1_Area_ 7 _das.pdf"
## [614] "COP1_Area_ 8 _das.pdf"
## [615] "COP1_Area_ 9 _das.pdf"
## [616] "COP1_Compactness_ 1 _das.pdf"
## [617] "COP1_Compactness_ 10 _das.pdf"
## [618] "COP1_Compactness_ 11 _das.pdf"
## [619] "COP1_Compactness_ 2 _das.pdf"
## [620] "COP1_Compactness_ 3 _das.pdf"
## [621] "COP1_Compactness_ 4 _das.pdf"
## [622] "COP1_Compactness_ 5 _das.pdf"
## [623] "COP1_Compactness_ 6 _das.pdf"
## [624] "COP1_Compactness_ 7 _das.pdf"
## [625] "COP1_Compactness_ 8 _das.pdf"
## [626] "COP1_Compactness_ 9 _das.pdf"
## [627] "COP1_Eccentricity_ 1 _das.pdf"
## [628] "COP1_Eccentricity_ 10 _das.pdf"
## [629] "COP1_Eccentricity_ 11 _das.pdf"
## [630] "COP1_Eccentricity_ 2 _das.pdf"
## [631] "COP1_Eccentricity_ 3 _das.pdf"
## [632] "COP1_Eccentricity_ 4 _das.pdf"
## [633] "COP1_Eccentricity_ 5 _das.pdf"
## [634] "COP1_Eccentricity_ 6 _das.pdf"
## [635] "COP1_Eccentricity_ 7 _das.pdf"
## [636] "COP1_Eccentricity_ 8 _das.pdf"
## [637] "COP1_Eccentricity_ 9 _das.pdf"
## [638] "COP1_growth_rate_Interval1.pdf"
## [639] "COP1_growth_rate_Interval2.pdf"
## [640] "COP1_growth_rate_Interval3.pdf"
## [641] "COP1_growth_rate.pdf"
## [642] "COP1_growth_SIIT_Interval1.pdf"
## [643] "COP1_growth_SIIT_Interval2.pdf"
## [644] "COP1_growth_SIIT_Interval3.pdf"
## [645] "COP1_growth_SIIT.pdf"
## [646] "COP1_Perimeter_ 1 _das.pdf"
## [647] "COP1_Perimeter_ 10 _das.pdf"
## [648] "COP1_Perimeter_ 11 _das.pdf"
## [649] "COP1_Perimeter_ 2 _das.pdf"
## [650] "COP1_Perimeter_ 3 _das.pdf"
## [651] "COP1_Perimeter_ 4 _das.pdf"
## [652] "COP1_Perimeter_ 5 _das.pdf"
## [653] "COP1_Perimeter_ 6 _das.pdf"
## [654] "COP1_Perimeter_ 7 _das.pdf"
## [655] "COP1_Perimeter_ 8 _das.pdf"
## [656] "COP1_Perimeter_ 9 _das.pdf"
## [657] "COP1_RMS_ 1 _das.pdf"
## [658] "COP1_RMS_ 10 _das.pdf"
## [659] "COP1_RMS_ 11 _das.pdf"
## [660] "COP1_RMS_ 2 _das.pdf"
## [661] "COP1_RMS_ 3 _das.pdf"
## [662] "COP1_RMS_ 4 _das.pdf"
## [663] "COP1_RMS_ 5 _das.pdf"
## [664] "COP1_RMS_ 6 _das.pdf"
## [665] "COP1_RMS_ 7 _das.pdf"
## [666] "COP1_RMS_ 8 _das.pdf"
## [667] "COP1_RMS_ 9 _das.pdf"
## [668] "COP1_Roundness_ 1 _das.pdf"
## [669] "COP1_Roundness_ 10 _das.pdf"
## [670] "COP1_Roundness_ 11 _das.pdf"
## [671] "COP1_Roundness_ 2 _das.pdf"
## [672] "COP1_Roundness_ 3 _das.pdf"
## [673] "COP1_Roundness_ 4 _das.pdf"
## [674] "COP1_Roundness_ 5 _das.pdf"
## [675] "COP1_Roundness_ 6 _das.pdf"
## [676] "COP1_Roundness_ 7 _das.pdf"
## [677] "COP1_Roundness_ 8 _das.pdf"
## [678] "COP1_Roundness_ 9 _das.pdf"
## [679] "COP1_SOL_ 1 _das.pdf"
## [680] "COP1_SOL_ 10 _das.pdf"
## [681] "COP1_SOL_ 11 _das.pdf"
## [682] "COP1_SOL_ 2 _das.pdf"
## [683] "COP1_SOL_ 3 _das.pdf"
## [684] "COP1_SOL_ 4 _das.pdf"
## [685] "COP1_SOL_ 5 _das.pdf"
## [686] "COP1_SOL_ 6 _das.pdf"
## [687] "COP1_SOL_ 7 _das.pdf"
## [688] "COP1_SOL_ 8 _das.pdf"
## [689] "COP1_SOL_ 9 _das.pdf"
## [690] "DUF"
## [691] "Entire_exp_Area_clean_data.pdf"
## [692] "Entire_exp_Area_complete_data.pdf"
## [693] "Experiment_coding.csv"
## [694] "Figure_MAIN_mutants_COP_locus.pdf"
## [695] "Figure_MAIN_mutants_FvFm_locus.pdf"
## [696] "Figure_SUPPL_mutants_COP_locus.pdf"
## [697] "Figure_SUPPL_mutants_FvFm_locus.pdf"
## [698] "FvFm locus"
## [699] "FvFm_ Fm _ 1 _das.pdf"
## [700] "FvFm_ Fm _ 10 _das.pdf"
## [701] "FvFm_ Fm _ 11 _das.pdf"
## [702] "FvFm_ Fm _ 2 _das.pdf"
## [703] "FvFm_ Fm _ 3 _das.pdf"
## [704] "FvFm_ Fm _ 4 _das.pdf"
## [705] "FvFm_ Fm _ 5 _das.pdf"
## [706] "FvFm_ Fm _ 6 _das.pdf"
## [707] "FvFm_ Fm _ 7 _das.pdf"
## [708] "FvFm_ Fm _ 8 _das.pdf"
## [709] "FvFm_ Fm _ 9 _das.pdf"
## [710] "FvFm_ Fm_Lss1 _ 1 _das.pdf"
## [711] "FvFm_ Fm_Lss1 _ 10 _das.pdf"
## [712] "FvFm_ Fm_Lss1 _ 11 _das.pdf"
## [713] "FvFm_ Fm_Lss1 _ 2 _das.pdf"
## [714] "FvFm_ Fm_Lss1 _ 3 _das.pdf"
## [715] "FvFm_ Fm_Lss1 _ 4 _das.pdf"
## [716] "FvFm_ Fm_Lss1 _ 5 _das.pdf"
## [717] "FvFm_ Fm_Lss1 _ 6 _das.pdf"
## [718] "FvFm_ Fm_Lss1 _ 7 _das.pdf"
## [719] "FvFm_ Fm_Lss1 _ 8 _das.pdf"
## [720] "FvFm_ Fm_Lss1 _ 9 _das.pdf"
## [721] "FvFm_ Fm_Lss2 _ 1 _das.pdf"
## [722] "FvFm_ Fm_Lss2 _ 10 _das.pdf"
## [723] "FvFm_ Fm_Lss2 _ 11 _das.pdf"
## [724] "FvFm_ Fm_Lss2 _ 2 _das.pdf"
## [725] "FvFm_ Fm_Lss2 _ 3 _das.pdf"
## [726] "FvFm_ Fm_Lss2 _ 4 _das.pdf"
## [727] "FvFm_ Fm_Lss2 _ 5 _das.pdf"
## [728] "FvFm_ Fm_Lss2 _ 6 _das.pdf"
## [729] "FvFm_ Fm_Lss2 _ 7 _das.pdf"
## [730] "FvFm_ Fm_Lss2 _ 8 _das.pdf"
## [731] "FvFm_ Fm_Lss2 _ 9 _das.pdf"
## [732] "FvFm_ Fm_Lss3 _ 1 _das.pdf"
## [733] "FvFm_ Fm_Lss3 _ 10 _das.pdf"
## [734] "FvFm_ Fm_Lss3 _ 11 _das.pdf"
## [735] "FvFm_ Fm_Lss3 _ 2 _das.pdf"
## [736] "FvFm_ Fm_Lss3 _ 3 _das.pdf"
## [737] "FvFm_ Fm_Lss3 _ 4 _das.pdf"
## [738] "FvFm_ Fm_Lss3 _ 5 _das.pdf"
## [739] "FvFm_ Fm_Lss3 _ 6 _das.pdf"
## [740] "FvFm_ Fm_Lss3 _ 7 _das.pdf"
## [741] "FvFm_ Fm_Lss3 _ 8 _das.pdf"
## [742] "FvFm_ Fm_Lss3 _ 9 _das.pdf"
## [743] "FvFm_ Fm_Lss4 _ 1 _das.pdf"
## [744] "FvFm_ Fm_Lss4 _ 10 _das.pdf"
## [745] "FvFm_ Fm_Lss4 _ 11 _das.pdf"
## [746] "FvFm_ Fm_Lss4 _ 2 _das.pdf"
## [747] "FvFm_ Fm_Lss4 _ 3 _das.pdf"
## [748] "FvFm_ Fm_Lss4 _ 4 _das.pdf"
## [749] "FvFm_ Fm_Lss4 _ 5 _das.pdf"
## [750] "FvFm_ Fm_Lss4 _ 6 _das.pdf"
## [751] "FvFm_ Fm_Lss4 _ 7 _das.pdf"
## [752] "FvFm_ Fm_Lss4 _ 8 _das.pdf"
## [753] "FvFm_ Fm_Lss4 _ 9 _das.pdf"
## [754] "FvFm_ Fm_Lss5 _ 1 _das.pdf"
## [755] "FvFm_ Fm_Lss5 _ 10 _das.pdf"
## [756] "FvFm_ Fm_Lss5 _ 11 _das.pdf"
## [757] "FvFm_ Fm_Lss5 _ 2 _das.pdf"
## [758] "FvFm_ Fm_Lss5 _ 3 _das.pdf"
## [759] "FvFm_ Fm_Lss5 _ 4 _das.pdf"
## [760] "FvFm_ Fm_Lss5 _ 5 _das.pdf"
## [761] "FvFm_ Fm_Lss5 _ 6 _das.pdf"
## [762] "FvFm_ Fm_Lss5 _ 7 _das.pdf"
## [763] "FvFm_ Fm_Lss5 _ 8 _das.pdf"
## [764] "FvFm_ Fm_Lss5 _ 9 _das.pdf"
## [765] "FvFm_ Fm_Lss6 _ 1 _das.pdf"
## [766] "FvFm_ Fm_Lss6 _ 10 _das.pdf"
## [767] "FvFm_ Fm_Lss6 _ 11 _das.pdf"
## [768] "FvFm_ Fm_Lss6 _ 2 _das.pdf"
## [769] "FvFm_ Fm_Lss6 _ 3 _das.pdf"
## [770] "FvFm_ Fm_Lss6 _ 4 _das.pdf"
## [771] "FvFm_ Fm_Lss6 _ 5 _das.pdf"
## [772] "FvFm_ Fm_Lss6 _ 6 _das.pdf"
## [773] "FvFm_ Fm_Lss6 _ 7 _das.pdf"
## [774] "FvFm_ Fm_Lss6 _ 8 _das.pdf"
## [775] "FvFm_ Fm_Lss6 _ 9 _das.pdf"
## [776] "FvFm_ Fo _ 1 _das.pdf"
## [777] "FvFm_ Fo _ 10 _das.pdf"
## [778] "FvFm_ Fo _ 11 _das.pdf"
## [779] "FvFm_ Fo _ 2 _das.pdf"
## [780] "FvFm_ Fo _ 3 _das.pdf"
## [781] "FvFm_ Fo _ 4 _das.pdf"
## [782] "FvFm_ Fo _ 5 _das.pdf"
## [783] "FvFm_ Fo _ 6 _das.pdf"
## [784] "FvFm_ Fo _ 7 _das.pdf"
## [785] "FvFm_ Fo _ 8 _das.pdf"
## [786] "FvFm_ Fo _ 9 _das.pdf"
## [787] "FvFm_ Fo_Lss1 _ 1 _das.pdf"
## [788] "FvFm_ Fo_Lss1 _ 10 _das.pdf"
## [789] "FvFm_ Fo_Lss1 _ 11 _das.pdf"
## [790] "FvFm_ Fo_Lss1 _ 2 _das.pdf"
## [791] "FvFm_ Fo_Lss1 _ 3 _das.pdf"
## [792] "FvFm_ Fo_Lss1 _ 4 _das.pdf"
## [793] "FvFm_ Fo_Lss1 _ 5 _das.pdf"
## [794] "FvFm_ Fo_Lss1 _ 6 _das.pdf"
## [795] "FvFm_ Fo_Lss1 _ 7 _das.pdf"
## [796] "FvFm_ Fo_Lss1 _ 8 _das.pdf"
## [797] "FvFm_ Fo_Lss1 _ 9 _das.pdf"
## [798] "FvFm_ Fo_Lss2 _ 1 _das.pdf"
## [799] "FvFm_ Fo_Lss2 _ 10 _das.pdf"
## [800] "FvFm_ Fo_Lss2 _ 11 _das.pdf"
## [801] "FvFm_ Fo_Lss2 _ 2 _das.pdf"
## [802] "FvFm_ Fo_Lss2 _ 3 _das.pdf"
## [803] "FvFm_ Fo_Lss2 _ 4 _das.pdf"
## [804] "FvFm_ Fo_Lss2 _ 5 _das.pdf"
## [805] "FvFm_ Fo_Lss2 _ 6 _das.pdf"
## [806] "FvFm_ Fo_Lss2 _ 7 _das.pdf"
## [807] "FvFm_ Fo_Lss2 _ 8 _das.pdf"
## [808] "FvFm_ Fo_Lss2 _ 9 _das.pdf"
## [809] "FvFm_ Fo_Lss3 _ 1 _das.pdf"
## [810] "FvFm_ Fo_Lss3 _ 10 _das.pdf"
## [811] "FvFm_ Fo_Lss3 _ 11 _das.pdf"
## [812] "FvFm_ Fo_Lss3 _ 2 _das.pdf"
## [813] "FvFm_ Fo_Lss3 _ 3 _das.pdf"
## [814] "FvFm_ Fo_Lss3 _ 4 _das.pdf"
## [815] "FvFm_ Fo_Lss3 _ 5 _das.pdf"
## [816] "FvFm_ Fo_Lss3 _ 6 _das.pdf"
## [817] "FvFm_ Fo_Lss3 _ 7 _das.pdf"
## [818] "FvFm_ Fo_Lss3 _ 8 _das.pdf"
## [819] "FvFm_ Fo_Lss3 _ 9 _das.pdf"
## [820] "FvFm_ Fo_Lss4 _ 1 _das.pdf"
## [821] "FvFm_ Fo_Lss4 _ 10 _das.pdf"
## [822] "FvFm_ Fo_Lss4 _ 11 _das.pdf"
## [823] "FvFm_ Fo_Lss4 _ 2 _das.pdf"
## [824] "FvFm_ Fo_Lss4 _ 3 _das.pdf"
## [825] "FvFm_ Fo_Lss4 _ 4 _das.pdf"
## [826] "FvFm_ Fo_Lss4 _ 5 _das.pdf"
## [827] "FvFm_ Fo_Lss4 _ 6 _das.pdf"
## [828] "FvFm_ Fo_Lss4 _ 7 _das.pdf"
## [829] "FvFm_ Fo_Lss4 _ 8 _das.pdf"
## [830] "FvFm_ Fo_Lss4 _ 9 _das.pdf"
## [831] "FvFm_ Fo_Lss5 _ 1 _das.pdf"
## [832] "FvFm_ Fo_Lss5 _ 10 _das.pdf"
## [833] "FvFm_ Fo_Lss5 _ 11 _das.pdf"
## [834] "FvFm_ Fo_Lss5 _ 2 _das.pdf"
## [835] "FvFm_ Fo_Lss5 _ 3 _das.pdf"
## [836] "FvFm_ Fo_Lss5 _ 4 _das.pdf"
## [837] "FvFm_ Fo_Lss5 _ 5 _das.pdf"
## [838] "FvFm_ Fo_Lss5 _ 6 _das.pdf"
## [839] "FvFm_ Fo_Lss5 _ 7 _das.pdf"
## [840] "FvFm_ Fo_Lss5 _ 8 _das.pdf"
## [841] "FvFm_ Fo_Lss5 _ 9 _das.pdf"
## [842] "FvFm_ Fo_Lss6 _ 1 _das.pdf"
## [843] "FvFm_ Fo_Lss6 _ 10 _das.pdf"
## [844] "FvFm_ Fo_Lss6 _ 11 _das.pdf"
## [845] "FvFm_ Fo_Lss6 _ 2 _das.pdf"
## [846] "FvFm_ Fo_Lss6 _ 3 _das.pdf"
## [847] "FvFm_ Fo_Lss6 _ 4 _das.pdf"
## [848] "FvFm_ Fo_Lss6 _ 5 _das.pdf"
## [849] "FvFm_ Fo_Lss6 _ 6 _das.pdf"
## [850] "FvFm_ Fo_Lss6 _ 7 _das.pdf"
## [851] "FvFm_ Fo_Lss6 _ 8 _das.pdf"
## [852] "FvFm_ Fo_Lss6 _ 9 _das.pdf"
## [853] "FvFm_ Ft_Lss1 _ 1 _das.pdf"
## [854] "FvFm_ Ft_Lss1 _ 10 _das.pdf"
## [855] "FvFm_ Ft_Lss1 _ 11 _das.pdf"
## [856] "FvFm_ Ft_Lss1 _ 2 _das.pdf"
## [857] "FvFm_ Ft_Lss1 _ 3 _das.pdf"
## [858] "FvFm_ Ft_Lss1 _ 4 _das.pdf"
## [859] "FvFm_ Ft_Lss1 _ 5 _das.pdf"
## [860] "FvFm_ Ft_Lss1 _ 6 _das.pdf"
## [861] "FvFm_ Ft_Lss1 _ 7 _das.pdf"
## [862] "FvFm_ Ft_Lss1 _ 8 _das.pdf"
## [863] "FvFm_ Ft_Lss1 _ 9 _das.pdf"
## [864] "FvFm_ Ft_Lss2 _ 1 _das.pdf"
## [865] "FvFm_ Ft_Lss2 _ 10 _das.pdf"
## [866] "FvFm_ Ft_Lss2 _ 11 _das.pdf"
## [867] "FvFm_ Ft_Lss2 _ 2 _das.pdf"
## [868] "FvFm_ Ft_Lss2 _ 3 _das.pdf"
## [869] "FvFm_ Ft_Lss2 _ 4 _das.pdf"
## [870] "FvFm_ Ft_Lss2 _ 5 _das.pdf"
## [871] "FvFm_ Ft_Lss2 _ 6 _das.pdf"
## [872] "FvFm_ Ft_Lss2 _ 7 _das.pdf"
## [873] "FvFm_ Ft_Lss2 _ 8 _das.pdf"
## [874] "FvFm_ Ft_Lss2 _ 9 _das.pdf"
## [875] "FvFm_ Ft_Lss3 _ 1 _das.pdf"
## [876] "FvFm_ Ft_Lss3 _ 10 _das.pdf"
## [877] "FvFm_ Ft_Lss3 _ 11 _das.pdf"
## [878] "FvFm_ Ft_Lss3 _ 2 _das.pdf"
## [879] "FvFm_ Ft_Lss3 _ 3 _das.pdf"
## [880] "FvFm_ Ft_Lss3 _ 4 _das.pdf"
## [881] "FvFm_ Ft_Lss3 _ 5 _das.pdf"
## [882] "FvFm_ Ft_Lss3 _ 6 _das.pdf"
## [883] "FvFm_ Ft_Lss3 _ 7 _das.pdf"
## [884] "FvFm_ Ft_Lss3 _ 8 _das.pdf"
## [885] "FvFm_ Ft_Lss3 _ 9 _das.pdf"
## [886] "FvFm_ Ft_Lss4 _ 1 _das.pdf"
## [887] "FvFm_ Ft_Lss4 _ 10 _das.pdf"
## [888] "FvFm_ Ft_Lss4 _ 11 _das.pdf"
## [889] "FvFm_ Ft_Lss4 _ 2 _das.pdf"
## [890] "FvFm_ Ft_Lss4 _ 3 _das.pdf"
## [891] "FvFm_ Ft_Lss4 _ 4 _das.pdf"
## [892] "FvFm_ Ft_Lss4 _ 5 _das.pdf"
## [893] "FvFm_ Ft_Lss4 _ 6 _das.pdf"
## [894] "FvFm_ Ft_Lss4 _ 7 _das.pdf"
## [895] "FvFm_ Ft_Lss4 _ 8 _das.pdf"
## [896] "FvFm_ Ft_Lss4 _ 9 _das.pdf"
## [897] "FvFm_ Ft_Lss5 _ 1 _das.pdf"
## [898] "FvFm_ Ft_Lss5 _ 10 _das.pdf"
## [899] "FvFm_ Ft_Lss5 _ 11 _das.pdf"
## [900] "FvFm_ Ft_Lss5 _ 2 _das.pdf"
## [901] "FvFm_ Ft_Lss5 _ 3 _das.pdf"
## [902] "FvFm_ Ft_Lss5 _ 4 _das.pdf"
## [903] "FvFm_ Ft_Lss5 _ 5 _das.pdf"
## [904] "FvFm_ Ft_Lss5 _ 6 _das.pdf"
## [905] "FvFm_ Ft_Lss5 _ 7 _das.pdf"
## [906] "FvFm_ Ft_Lss5 _ 8 _das.pdf"
## [907] "FvFm_ Ft_Lss5 _ 9 _das.pdf"
## [908] "FvFm_ Ft_Lss6 _ 1 _das.pdf"
## [909] "FvFm_ Ft_Lss6 _ 10 _das.pdf"
## [910] "FvFm_ Ft_Lss6 _ 11 _das.pdf"
## [911] "FvFm_ Ft_Lss6 _ 2 _das.pdf"
## [912] "FvFm_ Ft_Lss6 _ 3 _das.pdf"
## [913] "FvFm_ Ft_Lss6 _ 4 _das.pdf"
## [914] "FvFm_ Ft_Lss6 _ 5 _das.pdf"
## [915] "FvFm_ Ft_Lss6 _ 6 _das.pdf"
## [916] "FvFm_ Ft_Lss6 _ 7 _das.pdf"
## [917] "FvFm_ Ft_Lss6 _ 8 _das.pdf"
## [918] "FvFm_ Ft_Lss6 _ 9 _das.pdf"
## [919] "FvFm_ Fv _ 1 _das.pdf"
## [920] "FvFm_ Fv _ 10 _das.pdf"
## [921] "FvFm_ Fv _ 11 _das.pdf"
## [922] "FvFm_ Fv _ 2 _das.pdf"
## [923] "FvFm_ Fv _ 3 _das.pdf"
## [924] "FvFm_ Fv _ 4 _das.pdf"
## [925] "FvFm_ Fv _ 5 _das.pdf"
## [926] "FvFm_ Fv _ 6 _das.pdf"
## [927] "FvFm_ Fv _ 7 _das.pdf"
## [928] "FvFm_ Fv _ 8 _das.pdf"
## [929] "FvFm_ Fv _ 9 _das.pdf"
## [930] "FvFm_ Fv_Lss1 _ 1 _das.pdf"
## [931] "FvFm_ Fv_Lss1 _ 10 _das.pdf"
## [932] "FvFm_ Fv_Lss1 _ 11 _das.pdf"
## [933] "FvFm_ Fv_Lss1 _ 2 _das.pdf"
## [934] "FvFm_ Fv_Lss1 _ 3 _das.pdf"
## [935] "FvFm_ Fv_Lss1 _ 4 _das.pdf"
## [936] "FvFm_ Fv_Lss1 _ 5 _das.pdf"
## [937] "FvFm_ Fv_Lss1 _ 6 _das.pdf"
## [938] "FvFm_ Fv_Lss1 _ 7 _das.pdf"
## [939] "FvFm_ Fv_Lss1 _ 8 _das.pdf"
## [940] "FvFm_ Fv_Lss1 _ 9 _das.pdf"
## [941] "FvFm_ Fv_Lss2 _ 1 _das.pdf"
## [942] "FvFm_ Fv_Lss2 _ 10 _das.pdf"
## [943] "FvFm_ Fv_Lss2 _ 11 _das.pdf"
## [944] "FvFm_ Fv_Lss2 _ 2 _das.pdf"
## [945] "FvFm_ Fv_Lss2 _ 3 _das.pdf"
## [946] "FvFm_ Fv_Lss2 _ 4 _das.pdf"
## [947] "FvFm_ Fv_Lss2 _ 5 _das.pdf"
## [948] "FvFm_ Fv_Lss2 _ 6 _das.pdf"
## [949] "FvFm_ Fv_Lss2 _ 7 _das.pdf"
## [950] "FvFm_ Fv_Lss2 _ 8 _das.pdf"
## [951] "FvFm_ Fv_Lss2 _ 9 _das.pdf"
## [952] "FvFm_ Fv_Lss3 _ 1 _das.pdf"
## [953] "FvFm_ Fv_Lss3 _ 10 _das.pdf"
## [954] "FvFm_ Fv_Lss3 _ 11 _das.pdf"
## [955] "FvFm_ Fv_Lss3 _ 2 _das.pdf"
## [956] "FvFm_ Fv_Lss3 _ 3 _das.pdf"
## [957] "FvFm_ Fv_Lss3 _ 4 _das.pdf"
## [958] "FvFm_ Fv_Lss3 _ 5 _das.pdf"
## [959] "FvFm_ Fv_Lss3 _ 6 _das.pdf"
## [960] "FvFm_ Fv_Lss3 _ 7 _das.pdf"
## [961] "FvFm_ Fv_Lss3 _ 8 _das.pdf"
## [962] "FvFm_ Fv_Lss3 _ 9 _das.pdf"
## [963] "FvFm_ Fv_Lss4 _ 1 _das.pdf"
## [964] "FvFm_ Fv_Lss4 _ 10 _das.pdf"
## [965] "FvFm_ Fv_Lss4 _ 11 _das.pdf"
## [966] "FvFm_ Fv_Lss4 _ 2 _das.pdf"
## [967] "FvFm_ Fv_Lss4 _ 3 _das.pdf"
## [968] "FvFm_ Fv_Lss4 _ 4 _das.pdf"
## [969] "FvFm_ Fv_Lss4 _ 5 _das.pdf"
## [970] "FvFm_ Fv_Lss4 _ 6 _das.pdf"
## [971] "FvFm_ Fv_Lss4 _ 7 _das.pdf"
## [972] "FvFm_ Fv_Lss4 _ 8 _das.pdf"
## [973] "FvFm_ Fv_Lss4 _ 9 _das.pdf"
## [974] "FvFm_ Fv_Lss5 _ 1 _das.pdf"
## [975] "FvFm_ Fv_Lss5 _ 10 _das.pdf"
## [976] "FvFm_ Fv_Lss5 _ 11 _das.pdf"
## [977] "FvFm_ Fv_Lss5 _ 2 _das.pdf"
## [978] "FvFm_ Fv_Lss5 _ 3 _das.pdf"
## [979] "FvFm_ Fv_Lss5 _ 4 _das.pdf"
## [980] "FvFm_ Fv_Lss5 _ 5 _das.pdf"
## [981] "FvFm_ Fv_Lss5 _ 6 _das.pdf"
## [982] "FvFm_ Fv_Lss5 _ 7 _das.pdf"
## [983] "FvFm_ Fv_Lss5 _ 8 _das.pdf"
## [984] "FvFm_ Fv_Lss5 _ 9 _das.pdf"
## [985] "FvFm_ Fv_Lss6 _ 1 _das.pdf"
## [986] "FvFm_ Fv_Lss6 _ 10 _das.pdf"
## [987] "FvFm_ Fv_Lss6 _ 11 _das.pdf"
## [988] "FvFm_ Fv_Lss6 _ 2 _das.pdf"
## [989] "FvFm_ Fv_Lss6 _ 3 _das.pdf"
## [990] "FvFm_ Fv_Lss6 _ 4 _das.pdf"
## [991] "FvFm_ Fv_Lss6 _ 5 _das.pdf"
## [992] "FvFm_ Fv_Lss6 _ 6 _das.pdf"
## [993] "FvFm_ Fv_Lss6 _ 7 _das.pdf"
## [994] "FvFm_ Fv_Lss6 _ 8 _das.pdf"
## [995] "FvFm_ Fv_Lss6 _ 9 _das.pdf"
## [996] "FvFm_ FvFm_Lss1 _ 1 _das.pdf"
## [997] "FvFm_ FvFm_Lss1 _ 10 _das.pdf"
## [998] "FvFm_ FvFm_Lss1 _ 11 _das.pdf"
## [999] "FvFm_ FvFm_Lss1 _ 2 _das.pdf"
## [1000] "FvFm_ FvFm_Lss1 _ 3 _das.pdf"
## [1001] "FvFm_ FvFm_Lss1 _ 4 _das.pdf"
## [1002] "FvFm_ FvFm_Lss1 _ 5 _das.pdf"
## [1003] "FvFm_ FvFm_Lss1 _ 6 _das.pdf"
## [1004] "FvFm_ FvFm_Lss1 _ 7 _das.pdf"
## [1005] "FvFm_ FvFm_Lss1 _ 8 _das.pdf"
## [1006] "FvFm_ FvFm_Lss1 _ 9 _das.pdf"
## [1007] "FvFm_ FvFm_Lss2 _ 1 _das.pdf"
## [1008] "FvFm_ FvFm_Lss2 _ 10 _das.pdf"
## [1009] "FvFm_ FvFm_Lss2 _ 11 _das.pdf"
## [1010] "FvFm_ FvFm_Lss2 _ 2 _das.pdf"
## [1011] "FvFm_ FvFm_Lss2 _ 3 _das.pdf"
## [1012] "FvFm_ FvFm_Lss2 _ 4 _das.pdf"
## [1013] "FvFm_ FvFm_Lss2 _ 5 _das.pdf"
## [1014] "FvFm_ FvFm_Lss2 _ 6 _das.pdf"
## [1015] "FvFm_ FvFm_Lss2 _ 7 _das.pdf"
## [1016] "FvFm_ FvFm_Lss2 _ 8 _das.pdf"
## [1017] "FvFm_ FvFm_Lss2 _ 9 _das.pdf"
## [1018] "FvFm_ FvFm_Lss3 _ 1 _das.pdf"
## [1019] "FvFm_ FvFm_Lss3 _ 10 _das.pdf"
## [1020] "FvFm_ FvFm_Lss3 _ 11 _das.pdf"
## [1021] "FvFm_ FvFm_Lss3 _ 2 _das.pdf"
## [1022] "FvFm_ FvFm_Lss3 _ 3 _das.pdf"
## [1023] "FvFm_ FvFm_Lss3 _ 4 _das.pdf"
## [1024] "FvFm_ FvFm_Lss3 _ 5 _das.pdf"
## [1025] "FvFm_ FvFm_Lss3 _ 6 _das.pdf"
## [1026] "FvFm_ FvFm_Lss3 _ 7 _das.pdf"
## [1027] "FvFm_ FvFm_Lss3 _ 8 _das.pdf"
## [1028] "FvFm_ FvFm_Lss3 _ 9 _das.pdf"
## [1029] "FvFm_ FvFm_Lss4 _ 1 _das.pdf"
## [1030] "FvFm_ FvFm_Lss4 _ 10 _das.pdf"
## [1031] "FvFm_ FvFm_Lss4 _ 11 _das.pdf"
## [1032] "FvFm_ FvFm_Lss4 _ 2 _das.pdf"
## [1033] "FvFm_ FvFm_Lss4 _ 3 _das.pdf"
## [1034] "FvFm_ FvFm_Lss4 _ 4 _das.pdf"
## [1035] "FvFm_ FvFm_Lss4 _ 5 _das.pdf"
## [1036] "FvFm_ FvFm_Lss4 _ 6 _das.pdf"
## [1037] "FvFm_ FvFm_Lss4 _ 7 _das.pdf"
## [1038] "FvFm_ FvFm_Lss4 _ 8 _das.pdf"
## [1039] "FvFm_ FvFm_Lss4 _ 9 _das.pdf"
## [1040] "FvFm_ FvFm_Lss5 _ 1 _das.pdf"
## [1041] "FvFm_ FvFm_Lss5 _ 10 _das.pdf"
## [1042] "FvFm_ FvFm_Lss5 _ 11 _das.pdf"
## [1043] "FvFm_ FvFm_Lss5 _ 2 _das.pdf"
## [1044] "FvFm_ FvFm_Lss5 _ 3 _das.pdf"
## [1045] "FvFm_ FvFm_Lss5 _ 4 _das.pdf"
## [1046] "FvFm_ FvFm_Lss5 _ 5 _das.pdf"
## [1047] "FvFm_ FvFm_Lss5 _ 6 _das.pdf"
## [1048] "FvFm_ FvFm_Lss5 _ 7 _das.pdf"
## [1049] "FvFm_ FvFm_Lss5 _ 8 _das.pdf"
## [1050] "FvFm_ FvFm_Lss5 _ 9 _das.pdf"
## [1051] "FvFm_ FvFm_Lss6 _ 1 _das.pdf"
## [1052] "FvFm_ FvFm_Lss6 _ 10 _das.pdf"
## [1053] "FvFm_ FvFm_Lss6 _ 11 _das.pdf"
## [1054] "FvFm_ FvFm_Lss6 _ 2 _das.pdf"
## [1055] "FvFm_ FvFm_Lss6 _ 3 _das.pdf"
## [1056] "FvFm_ FvFm_Lss6 _ 4 _das.pdf"
## [1057] "FvFm_ FvFm_Lss6 _ 5 _das.pdf"
## [1058] "FvFm_ FvFm_Lss6 _ 6 _das.pdf"
## [1059] "FvFm_ FvFm_Lss6 _ 7 _das.pdf"
## [1060] "FvFm_ FvFm_Lss6 _ 8 _das.pdf"
## [1061] "FvFm_ FvFm_Lss6 _ 9 _das.pdf"
## [1062] "FvFm_ NPQ_Lss1 _ 1 _das.pdf"
## [1063] "FvFm_ NPQ_Lss1 _ 10 _das.pdf"
## [1064] "FvFm_ NPQ_Lss1 _ 11 _das.pdf"
## [1065] "FvFm_ NPQ_Lss1 _ 2 _das.pdf"
## [1066] "FvFm_ NPQ_Lss1 _ 3 _das.pdf"
## [1067] "FvFm_ NPQ_Lss1 _ 4 _das.pdf"
## [1068] "FvFm_ NPQ_Lss1 _ 5 _das.pdf"
## [1069] "FvFm_ NPQ_Lss1 _ 6 _das.pdf"
## [1070] "FvFm_ NPQ_Lss1 _ 7 _das.pdf"
## [1071] "FvFm_ NPQ_Lss1 _ 8 _das.pdf"
## [1072] "FvFm_ NPQ_Lss1 _ 9 _das.pdf"
## [1073] "FvFm_ NPQ_Lss2 _ 1 _das.pdf"
## [1074] "FvFm_ NPQ_Lss2 _ 10 _das.pdf"
## [1075] "FvFm_ NPQ_Lss2 _ 11 _das.pdf"
## [1076] "FvFm_ NPQ_Lss2 _ 2 _das.pdf"
## [1077] "FvFm_ NPQ_Lss2 _ 3 _das.pdf"
## [1078] "FvFm_ NPQ_Lss2 _ 4 _das.pdf"
## [1079] "FvFm_ NPQ_Lss2 _ 5 _das.pdf"
## [1080] "FvFm_ NPQ_Lss2 _ 6 _das.pdf"
## [1081] "FvFm_ NPQ_Lss2 _ 7 _das.pdf"
## [1082] "FvFm_ NPQ_Lss2 _ 8 _das.pdf"
## [1083] "FvFm_ NPQ_Lss2 _ 9 _das.pdf"
## [1084] "FvFm_ NPQ_Lss3 _ 1 _das.pdf"
## [1085] "FvFm_ NPQ_Lss3 _ 10 _das.pdf"
## [1086] "FvFm_ NPQ_Lss3 _ 11 _das.pdf"
## [1087] "FvFm_ NPQ_Lss3 _ 2 _das.pdf"
## [1088] "FvFm_ NPQ_Lss3 _ 3 _das.pdf"
## [1089] "FvFm_ NPQ_Lss3 _ 4 _das.pdf"
## [1090] "FvFm_ NPQ_Lss3 _ 5 _das.pdf"
## [1091] "FvFm_ NPQ_Lss3 _ 6 _das.pdf"
## [1092] "FvFm_ NPQ_Lss3 _ 7 _das.pdf"
## [1093] "FvFm_ NPQ_Lss3 _ 8 _das.pdf"
## [1094] "FvFm_ NPQ_Lss3 _ 9 _das.pdf"
## [1095] "FvFm_ NPQ_Lss4 _ 1 _das.pdf"
## [1096] "FvFm_ NPQ_Lss4 _ 10 _das.pdf"
## [1097] "FvFm_ NPQ_Lss4 _ 11 _das.pdf"
## [1098] "FvFm_ NPQ_Lss4 _ 2 _das.pdf"
## [1099] "FvFm_ NPQ_Lss4 _ 3 _das.pdf"
## [1100] "FvFm_ NPQ_Lss4 _ 4 _das.pdf"
## [1101] "FvFm_ NPQ_Lss4 _ 5 _das.pdf"
## [1102] "FvFm_ NPQ_Lss4 _ 6 _das.pdf"
## [1103] "FvFm_ NPQ_Lss4 _ 7 _das.pdf"
## [1104] "FvFm_ NPQ_Lss4 _ 8 _das.pdf"
## [1105] "FvFm_ NPQ_Lss4 _ 9 _das.pdf"
## [1106] "FvFm_ NPQ_Lss5 _ 1 _das.pdf"
## [1107] "FvFm_ NPQ_Lss5 _ 10 _das.pdf"
## [1108] "FvFm_ NPQ_Lss5 _ 11 _das.pdf"
## [1109] "FvFm_ NPQ_Lss5 _ 2 _das.pdf"
## [1110] "FvFm_ NPQ_Lss5 _ 3 _das.pdf"
## [1111] "FvFm_ NPQ_Lss5 _ 4 _das.pdf"
## [1112] "FvFm_ NPQ_Lss5 _ 5 _das.pdf"
## [1113] "FvFm_ NPQ_Lss5 _ 6 _das.pdf"
## [1114] "FvFm_ NPQ_Lss5 _ 7 _das.pdf"
## [1115] "FvFm_ NPQ_Lss5 _ 8 _das.pdf"
## [1116] "FvFm_ NPQ_Lss5 _ 9 _das.pdf"
## [1117] "FvFm_ NPQ_Lss6 _ 1 _das.pdf"
## [1118] "FvFm_ NPQ_Lss6 _ 10 _das.pdf"
## [1119] "FvFm_ NPQ_Lss6 _ 11 _das.pdf"
## [1120] "FvFm_ NPQ_Lss6 _ 2 _das.pdf"
## [1121] "FvFm_ NPQ_Lss6 _ 3 _das.pdf"
## [1122] "FvFm_ NPQ_Lss6 _ 4 _das.pdf"
## [1123] "FvFm_ NPQ_Lss6 _ 5 _das.pdf"
## [1124] "FvFm_ NPQ_Lss6 _ 6 _das.pdf"
## [1125] "FvFm_ NPQ_Lss6 _ 7 _das.pdf"
## [1126] "FvFm_ NPQ_Lss6 _ 8 _das.pdf"
## [1127] "FvFm_ NPQ_Lss6 _ 9 _das.pdf"
## [1128] "FvFm_ qP_Lss1 _ 1 _das.pdf"
## [1129] "FvFm_ qP_Lss1 _ 10 _das.pdf"
## [1130] "FvFm_ qP_Lss1 _ 11 _das.pdf"
## [1131] "FvFm_ qP_Lss1 _ 2 _das.pdf"
## [1132] "FvFm_ qP_Lss1 _ 3 _das.pdf"
## [1133] "FvFm_ qP_Lss1 _ 4 _das.pdf"
## [1134] "FvFm_ qP_Lss1 _ 5 _das.pdf"
## [1135] "FvFm_ qP_Lss1 _ 6 _das.pdf"
## [1136] "FvFm_ qP_Lss1 _ 7 _das.pdf"
## [1137] "FvFm_ qP_Lss1 _ 8 _das.pdf"
## [1138] "FvFm_ qP_Lss1 _ 9 _das.pdf"
## [1139] "FvFm_ qP_Lss2 _ 1 _das.pdf"
## [1140] "FvFm_ qP_Lss2 _ 10 _das.pdf"
## [1141] "FvFm_ qP_Lss2 _ 11 _das.pdf"
## [1142] "FvFm_ qP_Lss2 _ 2 _das.pdf"
## [1143] "FvFm_ qP_Lss2 _ 3 _das.pdf"
## [1144] "FvFm_ qP_Lss2 _ 4 _das.pdf"
## [1145] "FvFm_ qP_Lss2 _ 5 _das.pdf"
## [1146] "FvFm_ qP_Lss2 _ 6 _das.pdf"
## [1147] "FvFm_ qP_Lss2 _ 7 _das.pdf"
## [1148] "FvFm_ qP_Lss2 _ 8 _das.pdf"
## [1149] "FvFm_ qP_Lss2 _ 9 _das.pdf"
## [1150] "FvFm_ qP_Lss3 _ 1 _das.pdf"
## [1151] "FvFm_ qP_Lss3 _ 10 _das.pdf"
## [1152] "FvFm_ qP_Lss3 _ 11 _das.pdf"
## [1153] "FvFm_ qP_Lss3 _ 2 _das.pdf"
## [1154] "FvFm_ qP_Lss3 _ 3 _das.pdf"
## [1155] "FvFm_ qP_Lss3 _ 4 _das.pdf"
## [1156] "FvFm_ qP_Lss3 _ 5 _das.pdf"
## [1157] "FvFm_ qP_Lss3 _ 6 _das.pdf"
## [1158] "FvFm_ qP_Lss3 _ 7 _das.pdf"
## [1159] "FvFm_ qP_Lss3 _ 8 _das.pdf"
## [1160] "FvFm_ qP_Lss3 _ 9 _das.pdf"
## [1161] "FvFm_ qP_Lss4 _ 1 _das.pdf"
## [1162] "FvFm_ qP_Lss4 _ 10 _das.pdf"
## [1163] "FvFm_ qP_Lss4 _ 11 _das.pdf"
## [1164] "FvFm_ qP_Lss4 _ 2 _das.pdf"
## [1165] "FvFm_ qP_Lss4 _ 3 _das.pdf"
## [1166] "FvFm_ qP_Lss4 _ 4 _das.pdf"
## [1167] "FvFm_ qP_Lss4 _ 5 _das.pdf"
## [1168] "FvFm_ qP_Lss4 _ 6 _das.pdf"
## [1169] "FvFm_ qP_Lss4 _ 7 _das.pdf"
## [1170] "FvFm_ qP_Lss4 _ 8 _das.pdf"
## [1171] "FvFm_ qP_Lss4 _ 9 _das.pdf"
## [1172] "FvFm_ qP_Lss5 _ 1 _das.pdf"
## [1173] "FvFm_ qP_Lss5 _ 10 _das.pdf"
## [1174] "FvFm_ qP_Lss5 _ 11 _das.pdf"
## [1175] "FvFm_ qP_Lss5 _ 2 _das.pdf"
## [1176] "FvFm_ qP_Lss5 _ 3 _das.pdf"
## [1177] "FvFm_ qP_Lss5 _ 4 _das.pdf"
## [1178] "FvFm_ qP_Lss5 _ 5 _das.pdf"
## [1179] "FvFm_ qP_Lss5 _ 6 _das.pdf"
## [1180] "FvFm_ qP_Lss5 _ 7 _das.pdf"
## [1181] "FvFm_ qP_Lss5 _ 8 _das.pdf"
## [1182] "FvFm_ qP_Lss5 _ 9 _das.pdf"
## [1183] "FvFm_ qP_Lss6 _ 1 _das.pdf"
## [1184] "FvFm_ qP_Lss6 _ 10 _das.pdf"
## [1185] "FvFm_ qP_Lss6 _ 11 _das.pdf"
## [1186] "FvFm_ qP_Lss6 _ 2 _das.pdf"
## [1187] "FvFm_ qP_Lss6 _ 3 _das.pdf"
## [1188] "FvFm_ qP_Lss6 _ 4 _das.pdf"
## [1189] "FvFm_ qP_Lss6 _ 5 _das.pdf"
## [1190] "FvFm_ qP_Lss6 _ 6 _das.pdf"
## [1191] "FvFm_ qP_Lss6 _ 7 _das.pdf"
## [1192] "FvFm_ qP_Lss6 _ 8 _das.pdf"
## [1193] "FvFm_ qP_Lss6 _ 9 _das.pdf"
## [1194] "FvFm_ QY_Lss1 _ 1 _das.pdf"
## [1195] "FvFm_ QY_Lss1 _ 10 _das.pdf"
## [1196] "FvFm_ QY_Lss1 _ 11 _das.pdf"
## [1197] "FvFm_ QY_Lss1 _ 2 _das.pdf"
## [1198] "FvFm_ QY_Lss1 _ 3 _das.pdf"
## [1199] "FvFm_ QY_Lss1 _ 4 _das.pdf"
## [1200] "FvFm_ QY_Lss1 _ 5 _das.pdf"
## [1201] "FvFm_ QY_Lss1 _ 6 _das.pdf"
## [1202] "FvFm_ QY_Lss1 _ 7 _das.pdf"
## [1203] "FvFm_ QY_Lss1 _ 8 _das.pdf"
## [1204] "FvFm_ QY_Lss1 _ 9 _das.pdf"
## [1205] "FvFm_ QY_Lss2 _ 1 _das.pdf"
## [1206] "FvFm_ QY_Lss2 _ 10 _das.pdf"
## [1207] "FvFm_ QY_Lss2 _ 11 _das.pdf"
## [1208] "FvFm_ QY_Lss2 _ 2 _das.pdf"
## [1209] "FvFm_ QY_Lss2 _ 3 _das.pdf"
## [1210] "FvFm_ QY_Lss2 _ 4 _das.pdf"
## [1211] "FvFm_ QY_Lss2 _ 5 _das.pdf"
## [1212] "FvFm_ QY_Lss2 _ 6 _das.pdf"
## [1213] "FvFm_ QY_Lss2 _ 7 _das.pdf"
## [1214] "FvFm_ QY_Lss2 _ 8 _das.pdf"
## [1215] "FvFm_ QY_Lss2 _ 9 _das.pdf"
## [1216] "FvFm_ QY_Lss3 _ 1 _das.pdf"
## [1217] "FvFm_ QY_Lss3 _ 10 _das.pdf"
## [1218] "FvFm_ QY_Lss3 _ 11 _das.pdf"
## [1219] "FvFm_ QY_Lss3 _ 2 _das.pdf"
## [1220] "FvFm_ QY_Lss3 _ 3 _das.pdf"
## [1221] "FvFm_ QY_Lss3 _ 4 _das.pdf"
## [1222] "FvFm_ QY_Lss3 _ 5 _das.pdf"
## [1223] "FvFm_ QY_Lss3 _ 6 _das.pdf"
## [1224] "FvFm_ QY_Lss3 _ 7 _das.pdf"
## [1225] "FvFm_ QY_Lss3 _ 8 _das.pdf"
## [1226] "FvFm_ QY_Lss3 _ 9 _das.pdf"
## [1227] "FvFm_ QY_Lss4 _ 1 _das.pdf"
## [1228] "FvFm_ QY_Lss4 _ 10 _das.pdf"
## [1229] "FvFm_ QY_Lss4 _ 11 _das.pdf"
## [1230] "FvFm_ QY_Lss4 _ 2 _das.pdf"
## [1231] "FvFm_ QY_Lss4 _ 3 _das.pdf"
## [1232] "FvFm_ QY_Lss4 _ 4 _das.pdf"
## [1233] "FvFm_ QY_Lss4 _ 5 _das.pdf"
## [1234] "FvFm_ QY_Lss4 _ 6 _das.pdf"
## [1235] "FvFm_ QY_Lss4 _ 7 _das.pdf"
## [1236] "FvFm_ QY_Lss4 _ 8 _das.pdf"
## [1237] "FvFm_ QY_Lss4 _ 9 _das.pdf"
## [1238] "FvFm_ QY_Lss5 _ 1 _das.pdf"
## [1239] "FvFm_ QY_Lss5 _ 10 _das.pdf"
## [1240] "FvFm_ QY_Lss5 _ 11 _das.pdf"
## [1241] "FvFm_ QY_Lss5 _ 2 _das.pdf"
## [1242] "FvFm_ QY_Lss5 _ 3 _das.pdf"
## [1243] "FvFm_ QY_Lss5 _ 4 _das.pdf"
## [1244] "FvFm_ QY_Lss5 _ 5 _das.pdf"
## [1245] "FvFm_ QY_Lss5 _ 6 _das.pdf"
## [1246] "FvFm_ QY_Lss5 _ 7 _das.pdf"
## [1247] "FvFm_ QY_Lss5 _ 8 _das.pdf"
## [1248] "FvFm_ QY_Lss5 _ 9 _das.pdf"
## [1249] "FvFm_ QY_Lss6 _ 1 _das.pdf"
## [1250] "FvFm_ QY_Lss6 _ 10 _das.pdf"
## [1251] "FvFm_ QY_Lss6 _ 11 _das.pdf"
## [1252] "FvFm_ QY_Lss6 _ 2 _das.pdf"
## [1253] "FvFm_ QY_Lss6 _ 3 _das.pdf"
## [1254] "FvFm_ QY_Lss6 _ 4 _das.pdf"
## [1255] "FvFm_ QY_Lss6 _ 5 _das.pdf"
## [1256] "FvFm_ QY_Lss6 _ 6 _das.pdf"
## [1257] "FvFm_ QY_Lss6 _ 7 _das.pdf"
## [1258] "FvFm_ QY_Lss6 _ 8 _das.pdf"
## [1259] "FvFm_ QY_Lss6 _ 9 _das.pdf"
## [1260] "FvFm_ QY_max _ 1 _das.pdf"
## [1261] "FvFm_ QY_max _ 10 _das.pdf"
## [1262] "FvFm_ QY_max _ 11 _das.pdf"
## [1263] "FvFm_ QY_max _ 2 _das.pdf"
## [1264] "FvFm_ QY_max _ 3 _das.pdf"
## [1265] "FvFm_ QY_max _ 4 _das.pdf"
## [1266] "FvFm_ QY_max _ 5 _das.pdf"
## [1267] "FvFm_ QY_max _ 6 _das.pdf"
## [1268] "FvFm_ QY_max _ 7 _das.pdf"
## [1269] "FvFm_ QY_max _ 8 _das.pdf"
## [1270] "FvFm_ QY_max _ 9 _das.pdf"
## [1271] "FvFm_Area_ 1 _das.pdf"
## [1272] "FvFm_Area_ 10 _das.pdf"
## [1273] "FvFm_Area_ 11 _das.pdf"
## [1274] "FvFm_Area_ 2 _das.pdf"
## [1275] "FvFm_Area_ 3 _das.pdf"
## [1276] "FvFm_Area_ 4 _das.pdf"
## [1277] "FvFm_Area_ 5 _das.pdf"
## [1278] "FvFm_Area_ 6 _das.pdf"
## [1279] "FvFm_Area_ 7 _das.pdf"
## [1280] "FvFm_Area_ 8 _das.pdf"
## [1281] "FvFm_Area_ 9 _das.pdf"
## [1282] "FvFm_Compactness_ 1 _das.pdf"
## [1283] "FvFm_Compactness_ 10 _das.pdf"
## [1284] "FvFm_Compactness_ 11 _das.pdf"
## [1285] "FvFm_Compactness_ 2 _das.pdf"
## [1286] "FvFm_Compactness_ 3 _das.pdf"
## [1287] "FvFm_Compactness_ 4 _das.pdf"
## [1288] "FvFm_Compactness_ 5 _das.pdf"
## [1289] "FvFm_Compactness_ 6 _das.pdf"
## [1290] "FvFm_Compactness_ 7 _das.pdf"
## [1291] "FvFm_Compactness_ 8 _das.pdf"
## [1292] "FvFm_Compactness_ 9 _das.pdf"
## [1293] "FvFm_Eccentricity_ 1 _das.pdf"
## [1294] "FvFm_Eccentricity_ 10 _das.pdf"
## [1295] "FvFm_Eccentricity_ 11 _das.pdf"
## [1296] "FvFm_Eccentricity_ 2 _das.pdf"
## [1297] "FvFm_Eccentricity_ 3 _das.pdf"
## [1298] "FvFm_Eccentricity_ 4 _das.pdf"
## [1299] "FvFm_Eccentricity_ 5 _das.pdf"
## [1300] "FvFm_Eccentricity_ 6 _das.pdf"
## [1301] "FvFm_Eccentricity_ 7 _das.pdf"
## [1302] "FvFm_Eccentricity_ 8 _das.pdf"
## [1303] "FvFm_Eccentricity_ 9 _das.pdf"
## [1304] "FvFm_graphs"
## [1305] "FvFm_growth_rate_Interval1.pdf"
## [1306] "FvFm_growth_rate_Interval2.pdf"
## [1307] "FvFm_growth_rate_Interval3.pdf"
## [1308] "FvFm_growth_rate.pdf"
## [1309] "FvFm_growth_SIIT_Interval1.pdf"
## [1310] "FvFm_growth_SIIT_Interval2.pdf"
## [1311] "FvFm_growth_SIIT_Interval3.pdf"
## [1312] "FvFm_growth_SIIT.pdf"
## [1313] "FvFm_Perimeter_ 1 _das.pdf"
## [1314] "FvFm_Perimeter_ 10 _das.pdf"
## [1315] "FvFm_Perimeter_ 11 _das.pdf"
## [1316] "FvFm_Perimeter_ 2 _das.pdf"
## [1317] "FvFm_Perimeter_ 3 _das.pdf"
## [1318] "FvFm_Perimeter_ 4 _das.pdf"
## [1319] "FvFm_Perimeter_ 5 _das.pdf"
## [1320] "FvFm_Perimeter_ 6 _das.pdf"
## [1321] "FvFm_Perimeter_ 7 _das.pdf"
## [1322] "FvFm_Perimeter_ 8 _das.pdf"
## [1323] "FvFm_Perimeter_ 9 _das.pdf"
## [1324] "FvFm_RMS_ 1 _das.pdf"
## [1325] "FvFm_RMS_ 10 _das.pdf"
## [1326] "FvFm_RMS_ 11 _das.pdf"
## [1327] "FvFm_RMS_ 2 _das.pdf"
## [1328] "FvFm_RMS_ 3 _das.pdf"
## [1329] "FvFm_RMS_ 4 _das.pdf"
## [1330] "FvFm_RMS_ 5 _das.pdf"
## [1331] "FvFm_RMS_ 6 _das.pdf"
## [1332] "FvFm_RMS_ 7 _das.pdf"
## [1333] "FvFm_RMS_ 8 _das.pdf"
## [1334] "FvFm_RMS_ 9 _das.pdf"
## [1335] "FvFm_Roundness_ 1 _das.pdf"
## [1336] "FvFm_Roundness_ 10 _das.pdf"
## [1337] "FvFm_Roundness_ 11 _das.pdf"
## [1338] "FvFm_Roundness_ 2 _das.pdf"
## [1339] "FvFm_Roundness_ 3 _das.pdf"
## [1340] "FvFm_Roundness_ 4 _das.pdf"
## [1341] "FvFm_Roundness_ 5 _das.pdf"
## [1342] "FvFm_Roundness_ 6 _das.pdf"
## [1343] "FvFm_Roundness_ 7 _das.pdf"
## [1344] "FvFm_Roundness_ 8 _das.pdf"
## [1345] "FvFm_Roundness_ 9 _das.pdf"
## [1346] "FvFm_SOL_ 1 _das.pdf"
## [1347] "FvFm_SOL_ 10 _das.pdf"
## [1348] "FvFm_SOL_ 11 _das.pdf"
## [1349] "FvFm_SOL_ 2 _das.pdf"
## [1350] "FvFm_SOL_ 3 _das.pdf"
## [1351] "FvFm_SOL_ 4 _das.pdf"
## [1352] "FvFm_SOL_ 5 _das.pdf"
## [1353] "FvFm_SOL_ 6 _das.pdf"
## [1354] "FvFm_SOL_ 7 _das.pdf"
## [1355] "FvFm_SOL_ 8 _das.pdf"
## [1356] "FvFm_SOL_ 9 _das.pdf"
## [1357] "Growth_Rates_3_intervals_and_SIIT.csv"
## [1358] "NPQ_Lss4_most_interesting_mutants.pdf"
## [1359] "pheno_data (kw-15034's conflicted copy 2019-09-04).csv"
## [1360] "pheno_data.csv"
## [1361] "PSI tray exp. 20180801 - Sheet1.csv"
## [1362] "QY_Lss4_most_interesting_mutants.pdf"
## [1363] "QY_max_most_interesting_mutants.pdf"
## [1364] "Sig_and_effect_graph_at1g64270-2.pdf"
## [1365] "Sig_and_effect_graph_at1g64280-1.pdf"
## [1366] "Sig_and_effect_graph_at1g64290-1.pdf"
## [1367] "Sig_and_effect_graph_at1g64290-2.pdf"
## [1368] "Sig_and_effect_graph_at1g64290-3.pdf"
## [1369] "Sig_and_effect_graph_at1g64290-4.pdf"
## [1370] "Sig_and_effect_graph_at1g64295-1.pdf"
## [1371] "Sig_and_effect_graph_at1g64295-2.pdf"
## [1372] "Sig_and_effect_graph_at1g64300-2.pdf"
## [1373] "Sig_and_effect_graph_at1g64300-3.pdf"
## [1374] "Sig_and_effect_graph_at1g64300-4.pdf"
## [1375] "Sig_and_effect_graph_at1g64320-1.pdf"
## [1376] "Sig_and_effect_graph_at5g64920-1.pdf"
## [1377] "Sig_and_effect_graph_at5g64920-2.pdf"
## [1378] "Sig_and_effect_graph_NA.pdf"
## [1379] "summary_diff.csv"
## [1380] "summary_pval.csv"
## [1381] "tip data"Let’s check if we load the data in correctly:
morpho_data <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/PSI/BIG Salt 4/RGB2/Analysis/Rgb_Morpho_Plant.csv")
color_data <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/PSI/BIG Salt 4/RGB2/Analysis/Rgb_Color_Plant.csv")Chlorophyll Fluorescence & IR data:
fc_data <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/PSI/BIG Salt 4/FC1/Analysis/Fc_Plant.csv")
ir_data <- read.csv("/Users/magdalena/Dropbox/DataAndAnalysis/PSI/BIG Salt 4/IR1/Analysis/Ir_Plant.csv")Check all the data:
head(color_data)head(morpho_data)head(ir_data)head(fc_data)Fix coding of the genotypes. the problem is that the coding of genotypes is not correct in the uploaded files. The correct code is in the file:
coding <- read.csv("PSI tray exp. 20180801 - Sheet1.csv")
head(coding)colnames(coding)[6] <- "Genotype"
colnames(color_data)[5] <- "TrayID"
colnames(color_data)[8] <- "Area"
head(color_data)colnames(morpho_data)## [1] "Measuring.Date" "Measuring.Time" "Experiment.ID" "Round.Order"
## [5] "Tray.ID" "Tray.Info" "Plant.ID" "Position"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "AREA_PX" "AREA_MM" "PERIMETER_PX" "PERIMETER_MM"
## [17] "ROUNDNESS" "ROUNDNESS2" "ISOTROPY" "COMPACTNESS"
## [21] "ECCENTRICITY" "RMS" "SOL"colnames(morpho_data)[5] <- "TrayID"
colnames(morpho_data)[8] <- "Area"
head(morpho_data)colnames(ir_data)## [1] "Measuring.Date" "Measuring.Time" "Experiment.ID" "Round.Order"
## [5] "Tray.ID" "Tray.Info" "Plant.ID" "Position"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "Temp.avg" "Temp.stddev" "Temp.median" "Temp.min"
## [17] "Temp.max"colnames(ir_data)[5] <- "TrayID"
colnames(ir_data)[8] <- "Area"
head(ir_data)colnames(fc_data)## [1] "Measuring.Date" "Measuring.Time" "Experiment.ID" "Round.Order"
## [5] "Tray.ID" "Tray.Info" "Plant.ID" "Position"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "Size" "Fo" "Fm" "Fv"
## [17] "QY_max" "Fm_Lss1" "Fm_Lss2" "Fm_Lss3"
## [21] "Fm_Lss4" "Fm_Lss5" "Fm_Lss6" "Ft_Lss1"
## [25] "Ft_Lss2" "Ft_Lss3" "Ft_Lss4" "Ft_Lss5"
## [29] "Ft_Lss6" "Fo_Lss1" "Fo_Lss2" "Fo_Lss3"
## [33] "Fo_Lss4" "Fo_Lss5" "Fo_Lss6" "Fv_Lss1"
## [37] "Fv_Lss2" "Fv_Lss3" "Fv_Lss4" "Fv_Lss5"
## [41] "Fv_Lss6" "Fq_Lss1" "Fq_Lss2" "Fq_Lss3"
## [45] "Fq_Lss4" "Fq_Lss5" "Fq_Lss6" "Fv.Fm_Lss1"
## [49] "Fv.Fm_Lss2" "Fv.Fm_Lss3" "Fv.Fm_Lss4" "Fv.Fm_Lss5"
## [53] "Fv.Fm_Lss6" "QY_Lss1" "QY_Lss2" "QY_Lss3"
## [57] "QY_Lss4" "QY_Lss5" "QY_Lss6" "NPQ_Lss1"
## [61] "NPQ_Lss2" "NPQ_Lss3" "NPQ_Lss4" "NPQ_Lss5"
## [65] "NPQ_Lss6" "qN_Lss1" "qN_Lss2" "qN_Lss3"
## [69] "qN_Lss4" "qN_Lss5" "qN_Lss6" "qP_Lss1"
## [73] "qP_Lss2" "qP_Lss3" "qP_Lss4" "qP_Lss5"
## [77] "qP_Lss6" "qL_Lss1" "qL_Lss2" "qL_Lss3"
## [81] "qL_Lss4" "qL_Lss5" "qL_Lss6" "PAR1"
## [85] "PAR2" "PAR3" "PAR4" "PAR5"
## [89] "PAR6" "ETR1" "ETR2" "ETR3"
## [93] "ETR4" "ETR5" "ETR6"colnames(fc_data)[5] <- "TrayID"
colnames(fc_data)[8] <- "Area"
head(fc_data)color_correct <- merge(color_data, coding, by=c("TrayID", "Area"))
colnames((color_correct))## [1] "TrayID" "Area" "Measuring.Date" "Measuring.Time"
## [5] "Experiment.ID" "Round.Order" "Tray.Info" "Plant.ID"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "Hue1" "Hue2" "Hue3" "Hue4"
## [17] "Hue5" "Hue6" "Hue7" "Hue8"
## [21] "Hue9" "TrayInfo" "TrayTypeName" "PlantID"
## [25] "Genotype" "PlantInfo"color_correct <- color_correct[,c(1:3,7,25,13:21)]
morpho_correct <- merge(morpho_data, coding, by=c("TrayID", "Area"))
colnames(morpho_correct)## [1] "TrayID" "Area" "Measuring.Date" "Measuring.Time"
## [5] "Experiment.ID" "Round.Order" "Tray.Info" "Plant.ID"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "AREA_PX" "AREA_MM" "PERIMETER_PX" "PERIMETER_MM"
## [17] "ROUNDNESS" "ROUNDNESS2" "ISOTROPY" "COMPACTNESS"
## [21] "ECCENTRICITY" "RMS" "SOL" "TrayInfo"
## [25] "TrayTypeName" "PlantID" "Genotype" "PlantInfo"morpho_correct <- morpho_correct[,c(1:3,7,27,13:23)]
ir_correct <- merge(ir_data, coding, by=c("TrayID", "Area"))
colnames(ir_correct)## [1] "TrayID" "Area" "Measuring.Date" "Measuring.Time"
## [5] "Experiment.ID" "Round.Order" "Tray.Info" "Plant.ID"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "Temp.avg" "Temp.stddev" "Temp.median" "Temp.min"
## [17] "Temp.max" "TrayInfo" "TrayTypeName" "PlantID"
## [21] "Genotype" "PlantInfo"ir_correct <- ir_correct[,c(1:3,7,21,13:17)]
fc_correct <- merge(fc_data, coding, by=c("TrayID", "Area"))
colnames(fc_correct)## [1] "TrayID" "Area" "Measuring.Date" "Measuring.Time"
## [5] "Experiment.ID" "Round.Order" "Tray.Info" "Plant.ID"
## [9] "Plant.Name" "Plant.Info" "PID" "Camera.Position"
## [13] "Size" "Fo" "Fm" "Fv"
## [17] "QY_max" "Fm_Lss1" "Fm_Lss2" "Fm_Lss3"
## [21] "Fm_Lss4" "Fm_Lss5" "Fm_Lss6" "Ft_Lss1"
## [25] "Ft_Lss2" "Ft_Lss3" "Ft_Lss4" "Ft_Lss5"
## [29] "Ft_Lss6" "Fo_Lss1" "Fo_Lss2" "Fo_Lss3"
## [33] "Fo_Lss4" "Fo_Lss5" "Fo_Lss6" "Fv_Lss1"
## [37] "Fv_Lss2" "Fv_Lss3" "Fv_Lss4" "Fv_Lss5"
## [41] "Fv_Lss6" "Fq_Lss1" "Fq_Lss2" "Fq_Lss3"
## [45] "Fq_Lss4" "Fq_Lss5" "Fq_Lss6" "Fv.Fm_Lss1"
## [49] "Fv.Fm_Lss2" "Fv.Fm_Lss3" "Fv.Fm_Lss4" "Fv.Fm_Lss5"
## [53] "Fv.Fm_Lss6" "QY_Lss1" "QY_Lss2" "QY_Lss3"
## [57] "QY_Lss4" "QY_Lss5" "QY_Lss6" "NPQ_Lss1"
## [61] "NPQ_Lss2" "NPQ_Lss3" "NPQ_Lss4" "NPQ_Lss5"
## [65] "NPQ_Lss6" "qN_Lss1" "qN_Lss2" "qN_Lss3"
## [69] "qN_Lss4" "qN_Lss5" "qN_Lss6" "qP_Lss1"
## [73] "qP_Lss2" "qP_Lss3" "qP_Lss4" "qP_Lss5"
## [77] "qP_Lss6" "qL_Lss1" "qL_Lss2" "qL_Lss3"
## [81] "qL_Lss4" "qL_Lss5" "qL_Lss6" "PAR1"
## [85] "PAR2" "PAR3" "PAR4" "PAR5"
## [89] "PAR6" "ETR1" "ETR2" "ETR3"
## [93] "ETR4" "ETR5" "ETR6" "TrayInfo"
## [97] "TrayTypeName" "PlantID" "Genotype" "PlantInfo"fc_correct <- fc_correct[,c(1:3,7,99,13:83)]
head(fc_correct)Re-calculate the FC traits
fc_reliable <- subset(fc_correct, select = c(TrayID, Area, Measuring.Date, Tray.Info, Genotype, Size,
Fo, Fm, Fm_Lss1, Fm_Lss2, Fm_Lss3, Fm_Lss4, Fm_Lss5, Fm_Lss6,
Ft_Lss1, Ft_Lss2, Ft_Lss3, Ft_Lss4, Ft_Lss5, Ft_Lss6,
Fo_Lss1, Fo_Lss2, Fo_Lss3, Fo_Lss4, Fo_Lss5, Fo_Lss6,
Fq_Lss1, Fq_Lss2, Fq_Lss3, Fq_Lss4, Fq_Lss5, Fq_Lss6))
fc_reliable <- na.omit(fc_reliable)
for(i in 1:nrow(fc_reliable)){
if(fc_reliable$Fm[i] > fc_reliable$Fo[i]){
fc_reliable$Fv[i] <- fc_reliable$Fm[i] - fc_reliable$Fo[i]
}
else{
fc_reliable$Fv[i] <- NaN
}
if(fc_reliable$Fo_Lss1[i] > 0){
if(fc_reliable$Fm_Lss1[i] > fc_reliable$Fo_Lss1[i]){
fc_reliable$Fv_Lss1[i] <- fc_reliable$Fm_Lss1[i] - fc_reliable$Fo_Lss1[i]
}
else{
fc_reliable$Fv_Lss1[i] <- NaN
}}
else{
fc_reliable$Fv_Lss1[i] <- NaN
}
if(fc_reliable$Fo_Lss2[i] > 0){
if(fc_reliable$Fm_Lss2[i] > fc_reliable$Fo_Lss2[i]){
fc_reliable$Fv_Lss2[i] <- fc_reliable$Fm_Lss2[i] - fc_reliable$Fo_Lss2[i]
}
else{
fc_reliable$Fv_Lss1[i] <- NaN
}}
else{
fc_reliable$Fv_Lss2[i] <- NaN
}
if(fc_reliable$Fo_Lss3[i] > 0){
if(fc_reliable$Fm_Lss3[i] > fc_reliable$Fo_Lss3[i]){
fc_reliable$Fv_Lss3[i] <- fc_reliable$Fm_Lss3[i] - fc_reliable$Fo_Lss3[i]
}
else{
fc_reliable$Fv_Lss3[i] <- NaN
}}
else{
fc_reliable$Fv_Lss3[i] <- NaN
}
if(fc_reliable$Fo_Lss4[i] > 0){
if(fc_reliable$Fm_Lss4[i] > fc_reliable$Fo_Lss4[i]){
fc_reliable$Fv_Lss4[i] <- fc_reliable$Fm_Lss4[i] - fc_reliable$Fo_Lss4[i]
}
else{
fc_reliable$Fv_Lss4[i] <- NaN
}}
else{
fc_reliable$Fv_Lss4[i] <- NaN
}
if(fc_reliable$Fo_Lss5[i] > 0){
if(fc_reliable$Fm_Lss5[i] > fc_reliable$Fo_Lss5[i]){
fc_reliable$Fv_Lss5[i] <- fc_reliable$Fm_Lss5[i] - fc_reliable$Fo_Lss5[i]
}
else{
fc_reliable$Fv_Lss5[i] <- NaN
}}
else{
fc_reliable$Fv_Lss5[i] <- NaN
}
if(fc_reliable$Fo_Lss6[i] > 0){
if(fc_reliable$Fm_Lss6[i] > fc_reliable$Fo_Lss6[i]){
fc_reliable$Fv_Lss6[i] <- fc_reliable$Fm_Lss6[i] - fc_reliable$Fo_Lss6[i]
}
else{
fc_reliable$Fv_Lss6[i] <- NaN
}}
else{
fc_reliable$Fv_Lss6[i] <- NaN
}
if(fc_reliable$Ft_Lss1[i] > fc_reliable$Fo_Lss1[i]){
fc_reliable$qP_Lss1[i] <- (fc_reliable$Fm_Lss1[i] - fc_reliable$Ft_Lss1[i])/(fc_reliable$Fm_Lss1[i]-fc_reliable$Fo_Lss1[i])
}
else{
fc_reliable$qP_Lss1[i] <- NaN
}
if(fc_reliable$Ft_Lss2[i] > fc_reliable$Fo_Lss2[i]){
fc_reliable$qP_Lss2[i] <- (fc_reliable$Fm_Lss2[i] - fc_reliable$Ft_Lss2[i])/(fc_reliable$Fm_Lss2[i]-fc_reliable$Fo_Lss2[i])
}
else{
fc_reliable$qP_Lss2[i] <- NaN
}
if(fc_reliable$Ft_Lss3[i] > fc_reliable$Fo_Lss3[i]){
fc_reliable$qP_Lss3[i] <- (fc_reliable$Fm_Lss3[i] - fc_reliable$Ft_Lss3[i])/(fc_reliable$Fm_Lss3[i]-fc_reliable$Fo_Lss3[i])
}
else{
fc_reliable$qP_Lss3[i] <- NaN
}
if(fc_reliable$Ft_Lss4[i] > fc_reliable$Fo_Lss4[i]){
fc_reliable$qP_Lss4[i] <- (fc_reliable$Fm_Lss4[i] - fc_reliable$Ft_Lss4[i])/(fc_reliable$Fm_Lss4[i]-fc_reliable$Fo_Lss4[i])
}
else{
fc_reliable$qP_Lss4[i] <- NaN
}
if(fc_reliable$Ft_Lss5[i] > fc_reliable$Fo_Lss5[i]){
fc_reliable$qP_Lss5[i] <- (fc_reliable$Fm_Lss5[i] - fc_reliable$Ft_Lss5[i])/(fc_reliable$Fm_Lss5[i]-fc_reliable$Fo_Lss5[i])
}
else{
fc_reliable$qP_Lss5[i] <- NaN
}
if(fc_reliable$Ft_Lss6[i] > fc_reliable$Fo_Lss6[i]){
fc_reliable$qP_Lss6[i] <- (fc_reliable$Fm_Lss6[i] - fc_reliable$Ft_Lss6[i])/(fc_reliable$Fm_Lss6[i]-fc_reliable$Fo_Lss6[i])
}
else{
fc_reliable$qP_Lss6[i] <- NaN
}
}
fc_reliable <- na.omit(fc_reliable)
fc_reliable$QY_max <- fc_reliable$Fv / fc_reliable$Fm
fc_reliable$QY_Lss1 <- (fc_reliable$Fm_Lss1 - fc_reliable$Ft_Lss1) / fc_reliable$Fm
fc_reliable$QY_Lss2 <- (fc_reliable$Fm_Lss2 - fc_reliable$Ft_Lss2) / fc_reliable$Fm
fc_reliable$QY_Lss3 <- (fc_reliable$Fm_Lss3 - fc_reliable$Ft_Lss3) / fc_reliable$Fm
fc_reliable$QY_Lss4 <- (fc_reliable$Fm_Lss4 - fc_reliable$Ft_Lss4) / fc_reliable$Fm
fc_reliable$QY_Lss5 <- (fc_reliable$Fm_Lss5 - fc_reliable$Ft_Lss5) / fc_reliable$Fm
fc_reliable$QY_Lss6 <- (fc_reliable$Fm_Lss6 - fc_reliable$Ft_Lss6) / fc_reliable$Fm
fc_reliable$FvFm_Lss1 <- (fc_reliable$Fm_Lss1 - fc_reliable$Fo_Lss1) / fc_reliable$Fm_Lss1
fc_reliable$FvFm_Lss2 <- (fc_reliable$Fm_Lss2 - fc_reliable$Fo_Lss2) / fc_reliable$Fm_Lss2
fc_reliable$FvFm_Lss3 <- (fc_reliable$Fm_Lss3 - fc_reliable$Fo_Lss3) / fc_reliable$Fm_Lss3
fc_reliable$FvFm_Lss4 <- (fc_reliable$Fm_Lss4 - fc_reliable$Fo_Lss4) / fc_reliable$Fm_Lss4
fc_reliable$FvFm_Lss5 <- (fc_reliable$Fm_Lss5 - fc_reliable$Fo_Lss5) / fc_reliable$Fm_Lss5
fc_reliable$FvFm_Lss6 <- (fc_reliable$Fm_Lss6 - fc_reliable$Fo_Lss6) / fc_reliable$Fm_Lss6
fc_reliable$NPQ_Lss1 <- (fc_reliable$Fm - fc_reliable$Fm_Lss1)/fc_reliable$Fm_Lss1
fc_reliable$NPQ_Lss2 <- (fc_reliable$Fm - fc_reliable$Fm_Lss2)/fc_reliable$Fm_Lss2
fc_reliable$NPQ_Lss3 <- (fc_reliable$Fm - fc_reliable$Fm_Lss3)/fc_reliable$Fm_Lss3
fc_reliable$NPQ_Lss4 <- (fc_reliable$Fm - fc_reliable$Fm_Lss4)/fc_reliable$Fm_Lss4
fc_reliable$NPQ_Lss5 <- (fc_reliable$Fm - fc_reliable$Fm_Lss5)/fc_reliable$Fm_Lss5
fc_reliable$NPQ_Lss6 <- (fc_reliable$Fm - fc_reliable$Fm_Lss6)/fc_reliable$Fm_Lss6Then - let’s merge individual data frames together:
all_data <- merge(color_correct, morpho_correct, by=c("TrayID", "Area", "Genotype", "Tray.Info", "Measuring.Date"))
all_data <- merge(all_data, fc_reliable, by=c("TrayID", "Area", "Genotype", "Tray.Info", "Measuring.Date"))
all_data <- merge(all_data, ir_correct, by=c("TrayID", "Area", "Genotype", "Tray.Info", "Measuring.Date"))
head(all_data)# Remove rows containing missing data:
all_data_nona <- na.omit(all_data)
dim(all_data_nona)## [1] 6951 89head(all_data_nona)Let’s check if there are any odd values - like negative chlorophyll fluorescent values:
max(all_data_nona$QY_max)## [1] 0.8471828max(all_data_nona$FvFm_Lss1)## [1] 0.9953035max(all_data_nona$qP_Lss1)## [1] 0.9999342max(all_data_nona$NPQ_Lss1)## [1] 0.1253265OK - then we need to change the date into an actual DAY of measurement after salt stress application. The salt stress was applied at 15th of August 2019
unique(all_data_nona$Measuring.Date)## [1] 8/15/19 8/16/19 8/17/19 8/18/19 8/19/19 8/20/19 8/22/19 8/23/19 8/24/19
## [10] 8/25/19 8/26/19 8/21/19
## 12 Levels: 8/15/19 8/16/19 8/17/19 8/18/19 8/19/19 8/20/19 8/21/19 ... 8/26/19all_data_nona$days <- as.character(all_data_nona$Measuring.Date)
head(all_data_nona)all_data_nona$days <- strsplit(all_data_nona$days, "/")
all_data_nona$delta_days <- sapply(all_data_nona$days, function(x){
x= as.numeric(x)
(x[1]-8)*30+x[2]-15
})
head(all_data_nona)unique(all_data_nona$delta_days)## [1] 0 1 2 3 4 5 7 8 9 10 11 6all_data_nona$delta_days <- as.numeric(all_data_nona$delta_days)
colnames(all_data_nona)## [1] "TrayID" "Area" "Genotype" "Tray.Info"
## [5] "Measuring.Date" "Hue1" "Hue2" "Hue3"
## [9] "Hue4" "Hue5" "Hue6" "Hue7"
## [13] "Hue8" "Hue9" "AREA_PX" "AREA_MM"
## [17] "PERIMETER_PX" "PERIMETER_MM" "ROUNDNESS" "ROUNDNESS2"
## [21] "ISOTROPY" "COMPACTNESS" "ECCENTRICITY" "RMS"
## [25] "SOL" "Size" "Fo" "Fm"
## [29] "Fm_Lss1" "Fm_Lss2" "Fm_Lss3" "Fm_Lss4"
## [33] "Fm_Lss5" "Fm_Lss6" "Ft_Lss1" "Ft_Lss2"
## [37] "Ft_Lss3" "Ft_Lss4" "Ft_Lss5" "Ft_Lss6"
## [41] "Fo_Lss1" "Fo_Lss2" "Fo_Lss3" "Fo_Lss4"
## [45] "Fo_Lss5" "Fo_Lss6" "Fq_Lss1" "Fq_Lss2"
## [49] "Fq_Lss3" "Fq_Lss4" "Fq_Lss5" "Fq_Lss6"
## [53] "Fv" "Fv_Lss1" "Fv_Lss2" "Fv_Lss3"
## [57] "Fv_Lss4" "Fv_Lss5" "Fv_Lss6" "qP_Lss1"
## [61] "qP_Lss2" "qP_Lss3" "qP_Lss4" "qP_Lss5"
## [65] "qP_Lss6" "QY_max" "QY_Lss1" "QY_Lss2"
## [69] "QY_Lss3" "QY_Lss4" "QY_Lss5" "QY_Lss6"
## [73] "FvFm_Lss1" "FvFm_Lss2" "FvFm_Lss3" "FvFm_Lss4"
## [77] "FvFm_Lss5" "FvFm_Lss6" "NPQ_Lss1" "NPQ_Lss2"
## [81] "NPQ_Lss3" "NPQ_Lss4" "NPQ_Lss5" "NPQ_Lss6"
## [85] "Temp.avg" "Temp.stddev" "Temp.median" "Temp.min"
## [89] "Temp.max" "days" "delta_days"all_data_nona$PlantID <- paste(all_data_nona$TrayID,"_",all_data_nona$Area)
all_data_correct <- all_data_nona[,c(1:4,91,92,6:89)]
colnames(all_data_correct)## [1] "TrayID" "Area" "Genotype" "Tray.Info" "delta_days"
## [6] "PlantID" "Hue1" "Hue2" "Hue3" "Hue4"
## [11] "Hue5" "Hue6" "Hue7" "Hue8" "Hue9"
## [16] "AREA_PX" "AREA_MM" "PERIMETER_PX" "PERIMETER_MM" "ROUNDNESS"
## [21] "ROUNDNESS2" "ISOTROPY" "COMPACTNESS" "ECCENTRICITY" "RMS"
## [26] "SOL" "Size" "Fo" "Fm" "Fm_Lss1"
## [31] "Fm_Lss2" "Fm_Lss3" "Fm_Lss4" "Fm_Lss5" "Fm_Lss6"
## [36] "Ft_Lss1" "Ft_Lss2" "Ft_Lss3" "Ft_Lss4" "Ft_Lss5"
## [41] "Ft_Lss6" "Fo_Lss1" "Fo_Lss2" "Fo_Lss3" "Fo_Lss4"
## [46] "Fo_Lss5" "Fo_Lss6" "Fq_Lss1" "Fq_Lss2" "Fq_Lss3"
## [51] "Fq_Lss4" "Fq_Lss5" "Fq_Lss6" "Fv" "Fv_Lss1"
## [56] "Fv_Lss2" "Fv_Lss3" "Fv_Lss4" "Fv_Lss5" "Fv_Lss6"
## [61] "qP_Lss1" "qP_Lss2" "qP_Lss3" "qP_Lss4" "qP_Lss5"
## [66] "qP_Lss6" "QY_max" "QY_Lss1" "QY_Lss2" "QY_Lss3"
## [71] "QY_Lss4" "QY_Lss5" "QY_Lss6" "FvFm_Lss1" "FvFm_Lss2"
## [76] "FvFm_Lss3" "FvFm_Lss4" "FvFm_Lss5" "FvFm_Lss6" "NPQ_Lss1"
## [81] "NPQ_Lss2" "NPQ_Lss3" "NPQ_Lss4" "NPQ_Lss5" "NPQ_Lss6"
## [86] "Temp.avg" "Temp.stddev" "Temp.median" "Temp.min" "Temp.max"colnames(all_data_correct)[5] <- "days"Then - let’s select only the samples for which we have the FULL data
all_plants <- unique(all_data_correct$PlantID)
length(unique(all_data_correct$days))## [1] 12i=4
temp <- subset(all_data_correct, all_data_correct$PlantID == all_plants[i])
dim(temp)## [1] 12 90if(dim(temp)[1] == 12){
all_data_complete <- temp
}
head(all_data_complete)for(i in 2:length(all_plants)){
temp <- subset(all_data_correct, all_data_correct$PlantID == all_plants[i])
if(dim(temp)[1] == 12){
all_data_complete <- rbind(all_data_complete,temp)}
}
length(all_plants)## [1] 663length(unique(all_data_complete$PlantID))## [1] 279length(unique(all_data_complete$PlantID))## [1] 279auch - this selection is making me cringe as we go from 663 unique plants to 279 plants
another idea is to select the lines where we have at least 8 days of measurement
i=1
temp <- subset(all_data_correct, all_data_correct$PlantID == all_plants[i])
dim(temp)## [1] 11 90if(dim(temp)[1] >8){
all_data_complete <- temp
}
head(all_data_complete)for(i in 2:length(all_plants)){
temp <- subset(all_data_correct, all_data_correct$PlantID == all_plants[i])
if(dim(temp)[1] > 8){
all_data_complete <- rbind(all_data_complete,temp)}
}
length(all_plants)## [1] 663length(unique(all_data_complete$PlantID))## [1] 564length(unique(all_data_complete$PlantID))## [1] 564write.csv(all_data_complete, "pheno_data.csv", row.names = F)De-coding the names of the plants
Now I need to de-code the names of the plants that I have
unique(all_data_complete$Genotype)## [1] Mariam_25_B Mariam_21_1 Mariam_35_B
## [4] Mariam_20_A Mariam_32_1 C Col-0
## [7] Mariam_24_A Mariam_40_A DUF_OX_7_duf_G008_541
## [10] duf247 mut DUF_OX_3_Col_G003_3D1 DUF_OX_1_Col_G003_1A1
## [13] DUF_OX_4_Col_G007_121 tip2:2 DUF_OX_2_Col_G003_1B1
## [16] DUF_OX6_Col_G007_4D2 DUF_OX_5_Col_G007_4B1 Mariam_16_A
## [19] DUF_OX_8_Blh_G008_7D1 Mariam_10_A Mariam_19_D
## [22] Mariam_3_A Mariam_15_D Mariam_2_A
## [25] Mariam_12_B Mariam_5_B
## 28 Levels: Col-0 DUF_OX_1_Col_G003_1A1 ... tip2:2The genotypes that are relevant for the study of early responses of salt stress are stored in “to_keep” list.
However - I only want to keep Col-0 plants that are in the same TRAYS as my plants of interest - otherwise I have MUCH more Col-0 plants than neccessary:
mutants_only <- c("Mariam_25_B", "Mariam_21_1", "Mariam_35_B", "Mariam_20_A", "Mariam_32_1 C", "Mariam_24_A", "Mariam_40_A", "Mariam_16_A", "Mariam_10_A", "Mariam_19_D", "Mariam_3_A", "Mariam_15_D", "Mariam_2_A", "Mariam_12_B", "Mariam_5_B")
to_keep <- c("Mariam_25_B", "Mariam_21_1", "Mariam_35_B", "Mariam_20_A", "Mariam_32_1 C", "Mariam_24_A", "Mariam_40_A", "Mariam_16_A", "Mariam_10_A", "Mariam_19_D", "Mariam_3_A", "Mariam_15_D", "Mariam_2_A", "Mariam_12_B", "Mariam_5_B", "Col-0")
tray_number <- subset(all_data_complete, all_data_complete$Genotype %in% mutants_only)
tray_number2 <- unique(tray_number$TrayID)
all_data_rel <- subset(all_data_complete, all_data_complete$Genotype %in% to_keep)
all_data_rel2 <- subset(all_data_rel, all_data_rel$TrayID %in% tray_number2)
dim(all_data_rel2)## [1] 3632 90unique(all_data_rel2$TrayID)## [1] PS_Tray_000 PS_Tray_006 PS_Tray_008 PS_Tray_010 PS_Tray_022 PS_Tray_023
## [7] PS_Tray_033 PS_Tray_045 PS_Tray_053 PS_Tray_054 PS_Tray_066 PS_Tray_068
## [13] PS_Tray_092 PS_Tray_098 PS_Tray_100 PS_Tray_102 PS_Tray_104 PS_Tray_105
## [19] PS_Tray_106 PS_Tray_115 PS_Tray_122 PS_Tray_124 PS_Tray_129 PS_Tray_132
## 36 Levels: PS_Tray_000 PS_Tray_003 PS_Tray_006 PS_Tray_008 ... PS_Tray_132then - I made a coding of all genotypes in another file
coding <- read.csv("Experiment_coding.csv")
head(coding)codinghead(all_data_rel2)all_data_rel3 <- merge(all_data_rel2, coding, by="Genotype")
dim(all_data_rel2)## [1] 3632 90dim(all_data_rel3)## [1] 3632 91head(all_data_rel3)colnames(all_data_rel3)[4] <- "Treatment"Visual removal of samples based on Area
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutArea_lgraph <- ggplot(data=all_data_rel3, aes(x= days, y=AREA_MM, group = PlantID))
Area_lgraph <- Area_lgraph + geom_line()
#Area_lgraph <- Area_lgraph + ylim (0, 2000)
# geom_smooth(method="lm", aes(color="Exp Model"), formula= (y ~ exp(x)), se=FALSE, linetype = 1) +
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
Area_lgraph
ggplotly(Area_lgraph)All of the lines above look pretty good - yes - there are some lines that are growing ridiculously slowly - but it doesnt seem to be that bad. However, lets inspect it for each genotype separately:
all_genotypes <- unique(all_data_rel3$decoded)
length(all_genotypes)## [1] 16head(all_data_rel3)unique(all_data_rel3$Treatment)## [1] Control Salt
## Levels: Control Saltall_genotypes## [1] Col-0 at5g64920-1 at1g64270-2 at1g64290-1 at1g64290-2 at1g64290-3
## [7] at1g64280-1 at1g64295-1 at1g64295-2 at1g64300-3 at1g64300-4 at1g64300-1
## [13] at5g64920-2 at1g64290-4 at1g64320-1 at1g64300-2
## 26 Levels: at1g64270-2 at1g64280-1 at1g64290-1 at1g64290-2 ... tip2:2# There are 25 genotypes:
# because I am lazy - let's loop all the genotypes:
for(i in 1:15){
temp_data <- subset(all_data_rel3, all_data_rel3$decoded == all_genotypes[i])
Area_lgraph <- ggplot(data=temp_data, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")
Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + labs(title = as.character(all_genotypes[i])) + ylim(0,1700)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
Area_lgraph
}## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.I spotted some specific genotypes which have potential outliers. Let’s identify PlantID in these samples:
unique(all_genotypes)## [1] Col-0 at5g64920-1 at1g64270-2 at1g64290-1 at1g64290-2 at1g64290-3
## [7] at1g64280-1 at1g64295-1 at1g64295-2 at1g64300-3 at1g64300-4 at1g64300-1
## [13] at5g64920-2 at1g64290-4 at1g64320-1 at1g64300-2
## 26 Levels: at1g64270-2 at1g64280-1 at1g64290-1 at1g64290-2 ... tip2:2temp_data <- subset(all_data_rel3, all_data_rel3$decoded == all_genotypes[2])
Area_lgraph <- ggplot(data=temp_data, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + labs(title = as.character(all_genotypes[i])) + ylim(0,1700)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
ggplotly(Area_lgraph)unique(temp_data$PlantID) ## [1] "PS_Tray_092 _ C3" "PS_Tray_122 _ B3" "PS_Tray_022 _ B5" "PS_Tray_022 _ D2"
## [5] "PS_Tray_010 _ B2" "PS_Tray_045 _ C2" "PS_Tray_008 _ B2" "PS_Tray_010 _ D4"
## [9] "PS_Tray_100 _ D2" "PS_Tray_098 _ B3" "PS_Tray_102 _ A3" "PS_Tray_105 _ C2"
## [13] "PS_Tray_098 _ D5" "PS_Tray_068 _ C3" "PS_Tray_054 _ A3" "PS_Tray_054 _ C5"
## [17] "PS_Tray_068 _ A1" "PS_Tray_092 _ A1"temp_data <- subset(all_data_rel3, all_data_rel3$decoded == all_genotypes[7])
Area_lgraph <- ggplot(data=temp_data, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + labs(title = as.character(all_genotypes[i])) + ylim(0,1700)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
ggplotly(Area_lgraph)unique(temp_data$PlantID) ## [1] "PS_Tray_102 _ D4" "PS_Tray_045 _ D1" "PS_Tray_098 _ C2" "PS_Tray_102 _ B2"
## [5] "PS_Tray_105 _ D1" "PS_Tray_022 _ C4" "PS_Tray_045 _ B4" "PS_Tray_092 _ D2"
## [9] "PS_Tray_010 _ C1" "PS_Tray_122 _ C2" "PS_Tray_008 _ C1" "PS_Tray_100 _ C4"temp_data <- subset(all_data_rel3, all_data_rel3$decoded == all_genotypes[8])
Area_lgraph <- ggplot(data=temp_data, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + labs(title = as.character(all_genotypes[i])) + ylim(0,1700)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
ggplotly(Area_lgraph)unique(temp_data$PlantID) ## [1] "PS_Tray_066 _ A2" "PS_Tray_132 _ D4" "PS_Tray_006 _ C1" "PS_Tray_053 _ C4"
## [5] "PS_Tray_104 _ D3" "PS_Tray_033 _ C2" "PS_Tray_000 _ C2" "PS_Tray_129 _ A4"
## [9] "PS_Tray_000 _ A5" "PS_Tray_104 _ B1" "PS_Tray_124 _ D3" "PS_Tray_023 _ B4"
## [13] "PS_Tray_023 _ D1" "PS_Tray_115 _ B4" "PS_Tray_053 _ A2" "PS_Tray_106 _ D4"
## [17] "PS_Tray_066 _ C4" "PS_Tray_129 _ C1" "PS_Tray_132 _ B2" "PS_Tray_115 _ D1"
## [21] "PS_Tray_106 _ B2"temp_data <- subset(all_data_rel3, all_data_rel3$decoded == all_genotypes[12])
Area_lgraph <- ggplot(data=temp_data, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + labs(title = as.character(all_genotypes[i])) + ylim(0,1700)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
ggplotly(Area_lgraph)unique(temp_data$PlantID) ## [1] "PS_Tray_045 _ C4" "PS_Tray_045 _ A2" "PS_Tray_008 _ B4" "PS_Tray_105 _ A2"
## [5] "PS_Tray_068 _ A3" "PS_Tray_022 _ B2" "PS_Tray_105 _ C4" "PS_Tray_022 _ D4"
## [9] "PS_Tray_054 _ C2" "PS_Tray_010 _ B4" "PS_Tray_008 _ D1" "PS_Tray_098 _ D2"
## [13] "PS_Tray_092 _ C5" "PS_Tray_100 _ D4" "PS_Tray_010 _ D1" "PS_Tray_092 _ A3"
## [17] "PS_Tray_100 _ B2" "PS_Tray_102 _ C2" "PS_Tray_122 _ D2"temp_data <- subset(all_data_rel3, all_data_rel3$decoded == all_genotypes[13])
Area_lgraph <- ggplot(data=temp_data, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + labs(title = as.character(all_genotypes[i])) + ylim(0,1700)
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
ggplotly(Area_lgraph)unique(temp_data$PlantID) ## [1] "PS_Tray_106 _ A1" "PS_Tray_033 _ D3" "PS_Tray_000 _ D3" "PS_Tray_106 _ C3"
## [5] "PS_Tray_132 _ A1" "PS_Tray_129 _ D2" "PS_Tray_033 _ B1" "PS_Tray_115 _ A3"
## [9] "PS_Tray_066 _ B3" "PS_Tray_124 _ C2" "PS_Tray_132 _ C3" "PS_Tray_000 _ B1"
## [13] "PS_Tray_053 _ B3" "PS_Tray_104 _ C2" "PS_Tray_006 _ D2" "PS_Tray_023 _ A3"
## [17] "PS_Tray_053 _ D5"outliers <- c("PS_Tray_010 _ D4", "PS_Tray_115 _ D1", "PS_Tray_066 _ C4", "PS_Tray_066 _ A1", "PS_Tray_129 _ C5", "PS_Tray_106 _ B1", "PS_Tray_066 _ B3", "PS_Tray_132 _ A1", "PS_Tray_022 _ C4") Then - remove the outliers from the data
data_clean <- subset(all_data_rel3, !(all_data_rel3$PlantID %in% outliers))
dim(data_clean) ## [1] 3529 91 Area_lgraph <- ggplot(data=all_data_rel3, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + ylim(0,1700)
Area_lgraph <- Area_lgraph + labs(title = "Entire experiment") + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
Area_lgraph
pdf("Entire_exp_Area_complete_data.pdf", width=20, height = 10)
plot(Area_lgraph)
dev.off()## quartz_off_screen
## 2 Area_lgraph <- ggplot(data=data_clean, aes(x= days, y=AREA_MM, group = PlantID, color = Treatment))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")## Warning: `fun.y` is deprecated. Use `fun` instead. Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + ylim(0,1700)
Area_lgraph <- Area_lgraph + labs(title = "Entire experiment") + scale_color_manual(values=c("turquoise3", "maroon3"))
Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress") + theme(legend.position='none')
Area_lgraph
pdf("Entire_exp_Area_clean_data.pdf", width=20, height = 10)
plot(Area_lgraph)
dev.off() ## quartz_off_screen
## 2# Calculating the linear growth factors
Let’s calculate the exponential growth factors to fit into the observed increase in rosette area
Because we divided the timepoints into two intervals in the original manuscript - from 0-3 and 4-7 DAS, we want to do similar thing in here:
int_1 <- subset(data_clean, data_clean$days < 4)
int_2 <- subset(data_clean, data_clean$days > 3 & data_clean$days < 8)
int_3 <- subset(data_clean, data_clean$days > 7)
dim(int_1)## [1] 1063 91dim(int_2)## [1] 1212 91dim(int_3)## [1] 1254 91length(unique(int_1$PlantID))## [1] 321length(unique(int_2$PlantID))## [1] 321length(unique(int_3$PlantID))## [1] 321Let’s make an empty table that will contain all the values that we will calculate:
names <- c(text="PlantID", "decoded", "Treatment", "intercept", "delta")
growth_factors <- data.frame()
for (k in names) growth_factors[[k]] <- as.character()
i=1
uni <- subset(int_1, int_1$PlantID == unique(int_1$PlantID)[i])
uni# let's get also all the individual identifiers in the table:
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
# let's calculate the model:
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(Area_mm2~ time_d)
# add model parts into the main table with growth factors
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
# calculate predicted Area values for this specific sample:
timevalues <- unique(time_d)
timevalues## [1] 2 3Area.pred <- exp(predict(model_C,list(Time=timevalues)))
Area.pred## 1 2
## 3790116 316889966uni$Area_pred <- Area.pred
done <- uni
donegrowth_factorsSince in Interval_1 we have 321 individual plants:
for(i in 2:321){
uni <- subset(int_1, int_1$PlantID == unique(int_1$PlantID)[i])
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
# let's calculate the model:
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(Area_mm2~ time_d)
# add model parts into the main table with growth factors
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
# calculate predicted Area values for this specific sample:
timevalues <- unique(time_d)
timevalues
Area.pred <- exp(predict(model_C,list(Time=timevalues)))
uni$Area_pred <- Area.pred
done <- rbind(done, uni)
}## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleadinghead(growth_factors)dim(growth_factors)## [1] 321 5growth_int_1 <- growth_factorsThen we do the same for interval 2 and 3:
# Interval 2
names <- c(text="PlantID", "decoded", "Treatment", "intercept", "delta")
growth_factors <- data.frame()
for (k in names) growth_factors[[k]] <- as.character()
i=1
uni <- subset(int_2, int_2$PlantID == unique(int_2$PlantID)[i])
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(Area_mm2~ time_d)
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
timevalues <- unique(time_d)
Area.pred <- exp(predict(model_C,list(Time=timevalues)))
uni$Area_pred <- Area.pred
done <- uni
for(i in 2:321){
uni <- subset(int_2, int_2$PlantID == unique(int_2$PlantID)[i])
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(Area_mm2~ time_d)
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
timevalues <- unique(time_d)
Area.pred <- exp(predict(model_C,list(Time=timevalues)))
uni$Area_pred <- Area.pred
done <- rbind(done, uni)
}## Warning in predict.lm(model_C, list(Time = timevalues)): prediction from a rank-
## deficient fit may be misleadinggrowth_int_2 <- growth_factors
# Interval 3
names <- c(text="PlantID", "decoded", "Treatment", "intercept", "delta")
growth_factors <- data.frame()
for (k in names) growth_factors[[k]] <- as.character()
i=1
uni <- subset(int_3, int_3$PlantID == unique(int_3$PlantID)[i])
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(Area_mm2~ time_d)
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
timevalues <- unique(time_d)
Area.pred <- exp(predict(model_C,list(Time=timevalues)))
uni$Area_pred <- Area.pred
done <- uni
for(i in 2:321){
uni <- subset(int_3, int_3$PlantID == unique(int_3$PlantID)[i])
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(Area_mm2~ time_d)
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
timevalues <- unique(time_d)
Area.pred <- exp(predict(model_C,list(Time=timevalues)))
uni$Area_pred <- Area.pred
done <- rbind(done, uni)
}
growth_int_3 <- growth_factorsThen - let’s combine all the growth factors from individual intervals into one dataset:
head(growth_int_1)colnames(growth_int_1)[4] <- "Intercept_Int1"
colnames(growth_int_1)[5] <- "GR_Int1"
colnames(growth_int_2)[4] <- "Intercept_Int2"
colnames(growth_int_2)[5] <- "GR_Int2"
colnames(growth_int_3)[4] <- "Intercept_Int3"
colnames(growth_int_3)[5] <- "GR_Int3"
growth_all <- merge(growth_int_1, growth_int_2, by=c("PlantID", "decoded", "Treatment"))
growth_all <- merge(growth_all, growth_int_3, by=c("PlantID", "decoded", "Treatment"))
head(growth_all)summary(growth_all)## PlantID decoded Treatment Intercept_Int1
## Length:321 Length:321 Length:321 Length:321
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## GR_Int1 Intercept_Int2 GR_Int2 Intercept_Int3
## Length:321 Length:321 Length:321 Length:321
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## GR_Int3
## Length:321
## Class :character
## Mode :charactercalculating the SIIT and Visualizing growth factors:
Now - let’s calculate plant’s relative performance (SIIT) for individual intervals:
growth_all$GR_Int1 <- as.numeric(as.character(growth_all$GR_Int1))
growth_all$GR_Int2 <- as.numeric(as.character(growth_all$GR_Int2))
growth_all$GR_Int3 <- as.numeric(as.character(growth_all$GR_Int3))
head(growth_all)growth_imp <- growth_all[,c(2:3,5,7,9)]
tail(growth_imp)dim(growth_imp)## [1] 321 5growth_nona <- na.omit(growth_imp)
dim(growth_nona)## [1] 308 5growth_sum <- summaryBy(data = growth_nona, . ~ decoded + Treatment)
head(growth_sum)growth_C <- subset(growth_sum, growth_sum$Treatment == "Control")
head(growth_C)growth_C <- growth_C[,c(1,3:5)]
growth_factors2 <- merge(growth_all, growth_C, by="decoded", all=T)
unique(growth_factors2$Treatment)## [1] "Control" "Salt"head(growth_factors2)colnames(growth_factors2)[12] <- "GR_Int3_Control.mean"
colnames(growth_factors2)[11] <- "GR_Int2_Control.mean"
colnames(growth_factors2)[10] <- "GR_Int1_Control.mean"
growth_factors2$SIIT_Int1 <- growth_factors2$GR_Int1 / growth_factors2$GR_Int1_Control.mean
growth_factors2$SIIT_Int2 <- growth_factors2$GR_Int2 / growth_factors2$GR_Int2_Control.mean
growth_factors2$SIIT_Int3 <- growth_factors2$GR_Int3 / growth_factors2$GR_Int3_Control.mean
head(growth_factors2)write.csv(growth_factors2, "Growth_Rates_3_intervals_and_SIIT.csv", row.names = F)So now we can have a look at plant’s performance under salt stress. If we visualize everything all together…
library(ggsci)
library(ggpubr)
library(ggbeeswarm)
library(gapminder)
library(RColorBrewer)
library(ggridges)
library(cowplot)
growth_factors2 <- subset(growth_factors2, growth_factors2$Treatment == c("Control", "Salt"))## Warning in growth_factors2$Treatment == c("Control", "Salt"): longer object
## length is not a multiple of shorter object lengthdim(growth_factors2)## [1] 148 15my_box_plot <- ggplot(data = growth_factors2, mapping = aes(x = decoded, y = GR_Int1, colour = decoded))
#my_box_plot <- my_box_plot + geom_boxplot()
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = "")))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot## Warning: Removed 7 rows containing non-finite values (stat_summary).## Warning: Removed 7 rows containing non-finite values (stat_compare_means).## Warning: Removed 4 rows containing missing values (position_beeswarm).## Warning: Removed 3 rows containing missing values (position_beeswarm).
# Save the graph with these few commands
pdf("all_growth_rate_int1_clean.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Removed 7 rows containing non-finite values (stat_summary).## Warning: Removed 7 rows containing non-finite values (stat_compare_means).## Warning: Removed 4 rows containing missing values (position_beeswarm).## Warning: Removed 3 rows containing missing values (position_beeswarm).dev.off()## quartz_off_screen
## 2As you see - the above graph is a mess - so let’s have a look at the subset of the data per locus and make sure that the order of the mutants is logical
unique(growth_factors2$decoded)## [1] "at1g64270-2" "at1g64280-1" "at1g64290-1" "at1g64290-2" "at1g64290-3"
## [6] "at1g64290-4" "at1g64295-1" "at1g64295-2" "at1g64300-1" "at1g64300-2"
## [11] "at1g64300-3" "at1g64300-4" "at1g64320-1" "at5g64920-1" "at5g64920-2"
## [16] "Col-0"growth_factors2$decoded <- factor(growth_factors2$decoded, levels = c("Col-0", "at1g64270-2", "at1g64280-1", "at1g64290-1", "at1g64290-2", "at1g64290-3",
"at1g64290-4", "at1g64295-1", "at1g64295-2", "at1g64300-2", "at1g64300-3", "at1g64300-4",
"at1g64320-1","at5g64920-1", "at5g64920-2"))
FvFm <- c("Col-0", "at1g64270-2", "at1g64280-1", "at1g64290-1", "at1g64290-2", "at1g64290-3", "at1g64290-4", "at1g64295-1", "at1g64295-2", "at1g64300-2", "at1g64300-3", "at1g64300-4","at1g64320-1")
COP <- c("Col-0", "at5g64920-1", "at5g64920-2")
FvFm_locus <- subset(growth_factors2, growth_factors2$decoded %in% FvFm)
COP_locus <- subset(growth_factors2, growth_factors2$decoded %in% COP)Now - let’s make the graphs for each locus individually then :)
First - FvFm locus:
my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = GR_Int1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = "")))
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot## Warning: Removed 6 rows containing non-finite values (stat_summary).## Warning: Removed 6 rows containing non-finite values (stat_compare_means).## Warning: Removed 4 rows containing missing values (position_beeswarm).## Warning: Removed 2 rows containing missing values (position_beeswarm).
pdf("FvFm_growth_rate_Interval1.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Removed 6 rows containing non-finite values (stat_summary).## Warning: Removed 6 rows containing non-finite values (stat_compare_means).## Warning: Removed 4 rows containing missing values (position_beeswarm).## Warning: Removed 2 rows containing missing values (position_beeswarm).dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = SIIT_Int1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of GR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot## Warning: Removed 6 rows containing non-finite values (stat_summary).## Warning: Removed 6 rows containing non-finite values (stat_compare_means).## Warning: Removed 6 rows containing missing values (position_beeswarm).
pdf("FvFm_growth_SIIT_Interval1.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Removed 6 rows containing non-finite values (stat_summary).## Warning: Removed 6 rows containing non-finite values (stat_compare_means).## Warning: Removed 6 rows containing missing values (position_beeswarm).dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = GR_Int2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = "")))
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("FvFm_growth_rate_Interval2.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = SIIT_Int2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of GR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("FvFm_growth_SIIT_Interval2.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = GR_Int3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = "")))
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("FvFm_growth_rate_Interval3.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = SIIT_Int3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of GR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("FvFm_growth_SIIT_Interval3.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2And now we do the same but for COP locus
my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = GR_Int1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = ""))) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).## Warning: Removed 2 rows containing missing values (position_beeswarm).
pdf("COP1_growth_rate_Interval1.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).## Warning: Removed 2 rows containing missing values (position_beeswarm).dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = SIIT_Int1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of GR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).## Warning: Removed 2 rows containing missing values (position_beeswarm).
pdf("COP1_growth_SIIT_Interval1.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).## Warning: Removed 2 rows containing missing values (position_beeswarm).dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = GR_Int2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = ""))) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("COP1_growth_rate_Interval2.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = SIIT_Int2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of GR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("COP1_growth_SIIT_Interval2.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = GR_Int3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab(expression(paste("GR (", mm^2, " / day)", sep = ""))) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("COP1_growth_rate_Interval3.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = SIIT_Int3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of GR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("COP1_growth_SIIT_Interval3.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2# Calculating the exponential growth factors
For the ones that really want to have a look at eponential growth rate - see below:
Let’s make an empty table that will contain all the values that we will calculate:
names <- c(text="PlantID", "decoded", "Treatment", "intercept", "delta")
growth_factors <- data.frame()
for (k in names) growth_factors[[k]] <- as.character()
i=1
uni <- subset(data_clean, data_clean$PlantID == unique(data_clean$PlantID)[i])
uni# let's get also all the individual identifiers in the table:
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
# let's calculate the model:
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(log(Area_mm2)~ time_d)
# add model parts into the main table with growth factors
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
# calculate predicted Area values for this specific sample:
timevalues <- unique(time_d)
timevalues## [1] 6 8 7 11 9 10 5 4 2 3 1Area.pred <- exp(predict(model_C,list(Time=timevalues)))
Area.pred## 1 2 3 4 5 6 7 8
## 207.54766 363.44439 274.64892 842.20569 480.94790 636.44093 156.84034 118.52165
## 9 10 11
## 67.68268 89.56486 51.14669uni$Area_pred <- Area.pred
done <- uni
donegrowth_factors# We have in total 621 plants, so let's loop it for all the remaining ones:
for(i in 2:321){
uni <- subset(data_clean, data_clean$PlantID == unique(data_clean$PlantID)[i])
growth_factors[i,1] <- as.character(unique(uni$PlantID))
growth_factors[i,2] <- as.character(unique(uni$decoded))
growth_factors[i,3] <- as.character(unique(uni$Treatment))
# let's calculate the model:
Area_mm2 <- uni$AREA_MM
time_d <- uni$days
model_C <- lm(log(Area_mm2)~ time_d)
# add model parts into the main table with growth factors
growth_factors[i,4] <- as.numeric(model_C$coefficients[[1]])
growth_factors[i,5] <- as.numeric(model_C$coefficients[[2]])
# calculate predicted Area values for this specific sample:
timevalues <- unique(time_d)
timevalues
Area.pred <- exp(predict(model_C,list(Time=timevalues)))
uni$Area_pred <- Area.pred
done <- rbind(done, uni)
}
head(growth_factors)dim(growth_factors)## [1] 321 5calculating the SIIT and Visualizing exponential growth factors:
Very often - we would like to examine the relative performance of the plants under salt stress - relative how the genotype grows under control conditions. To do this, we can calculate the Salt Tolerance Index (or for that matter - ANY tolerance index).
We can easily do it automatically by calculating the average growth rate under control condition for each Genotype, match it with plants belonging to the same genotype under salt stress condition, and calculating the performance index:
growth_factors$intercept <- as.numeric(as.character(growth_factors$intercept))
growth_factors$delta <- as.numeric(as.character(growth_factors$delta))
# Calculate average growth rate for each genotype:
growth_sum <- summaryBy(data = growth_factors, delta ~ decoded + Treatment)
head(growth_sum)growth_C <- subset(growth_sum, growth_sum$Treatment == "Control")
head(growth_C)growth_C <- growth_C[,c(1,3)]
growth_factors2 <- merge(growth_factors, growth_C, by="decoded", all=T)
unique(growth_factors2$Treatment)## [1] "Control" "Salt"head(growth_factors2)colnames(growth_factors2)[6] <- "Delta_Control.mean"
growth_factors2$SIIT <- growth_factors2$delta / growth_factors2$Delta_Control.mean
head(growth_factors2)write.csv(growth_factors2, "Clean_Growth_factors_and_SIIT.csv", row.names = F)So now we can have a look at plant’s performance under salt stress. If we visualize everything all together…
growth_factors2 <- subset(growth_factors2, growth_factors2$Treatment == c("Control", "Salt"))## Warning in growth_factors2$Treatment == c("Control", "Salt"): longer object
## length is not a multiple of shorter object lengthmy_box_plot <- ggplot(data = growth_factors2, mapping = aes(x = decoded, y = delta, colour = decoded))
#my_box_plot <- my_box_plot + geom_boxplot()
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none") + ylim(0.2, 0.4)
my_box_plot <- my_box_plot + xlab("") + ylab("RGR") # + scale_colour_manual(values=c("steelblue", "sienna3", "slateblue3", "slateblue3","slateblue3","slateblue3","slateblue3","slateblue3", "firebrick3", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot## Warning: Computation failed in `stat_compare_means()`:
## not enough 'x' observations
# Save the graph with these few commands
pdf("all_growth_clean.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Computation failed in `stat_compare_means()`:
## not enough 'x' observationsdev.off()## quartz_off_screen
## 2As you see - the above graph is a mess - so let’s have a look at the subset of the data per locus and make sure that the order of the mutants is logical
FvFm_locus <- subset(growth_factors2, growth_factors2$decoded %in% FvFm)
COP_locus <- subset(growth_factors2, growth_factors2$decoded %in% COP)Now - let’s make the graphs for each locus individually then :)
my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = delta, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("RGR")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot## Warning: Computation failed in `stat_compare_means()`:
## not enough 'x' observations
pdf("FvFm_growth_rate.pdf", width=20, height = 10)
plot(my_box_plot)## Warning: Computation failed in `stat_compare_means()`:
## not enough 'x' observationsdev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = FvFm_locus, mapping = aes(x = decoded, y = SIIT, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of RGR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet", "darkviolet",
"cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("FvFm_growth_SIIT.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = delta, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment)
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("RGR") + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("COP1_growth_rate.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2my_box_plot <- ggplot(data = COP_locus, mapping = aes(x = decoded, y = SIIT, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: `fun.y` is deprecated. Use `fun` instead.my_box_plot <- my_box_plot + theme(legend.position = "none")
my_box_plot <- my_box_plot + xlab("") + ylab("faction of RGR at Control")
my_box_plot <- my_box_plot + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf("COP1_growth_SIIT.pdf", width=20, height = 10)
plot(my_box_plot)
dev.off()## quartz_off_screen
## 2visualizing timeseries of other parameters:
If you would like to visualize changes over time in other parameters - including Area, morphology, Fc, you can run the following timeseries graph, which will test e.g. the difference between your treatment vs. control across the genotypes of interest.
So first prepare your data
data_clean$decoded <- factor(data_clean$decoded, levels = c("Col-0", "at1g64270-2", "at1g64280-1", "at1g64290-1", "at1g64290-2", "at1g64290-3",
"at1g64290-4", "at1g64295-1", "at1g64295-2", "at1g64300-2", "at1g64300-3", "at1g64300-4",
"at1g64320-1","at5g64920-1", "at5g64920-2"))
FvFm_data <- subset(data_clean, data_clean$decoded %in% FvFm)
COP_data <- subset(data_clean, data_clean$decoded %in% COP)
head(FvFm_data)for single time point comparison:
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=AREA_MM, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_Area_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = AREA_MM, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_Area_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Then - let’s have a look at other traits that we find interesting:
colnames(FvFm_data)## [1] "Genotype" "TrayID" "Area" "Treatment" "days"
## [6] "PlantID" "Hue1" "Hue2" "Hue3" "Hue4"
## [11] "Hue5" "Hue6" "Hue7" "Hue8" "Hue9"
## [16] "AREA_PX" "AREA_MM" "PERIMETER_PX" "PERIMETER_MM" "ROUNDNESS"
## [21] "ROUNDNESS2" "ISOTROPY" "COMPACTNESS" "ECCENTRICITY" "RMS"
## [26] "SOL" "Size" "Fo" "Fm" "Fm_Lss1"
## [31] "Fm_Lss2" "Fm_Lss3" "Fm_Lss4" "Fm_Lss5" "Fm_Lss6"
## [36] "Ft_Lss1" "Ft_Lss2" "Ft_Lss3" "Ft_Lss4" "Ft_Lss5"
## [41] "Ft_Lss6" "Fo_Lss1" "Fo_Lss2" "Fo_Lss3" "Fo_Lss4"
## [46] "Fo_Lss5" "Fo_Lss6" "Fq_Lss1" "Fq_Lss2" "Fq_Lss3"
## [51] "Fq_Lss4" "Fq_Lss5" "Fq_Lss6" "Fv" "Fv_Lss1"
## [56] "Fv_Lss2" "Fv_Lss3" "Fv_Lss4" "Fv_Lss5" "Fv_Lss6"
## [61] "qP_Lss1" "qP_Lss2" "qP_Lss3" "qP_Lss4" "qP_Lss5"
## [66] "qP_Lss6" "QY_max" "QY_Lss1" "QY_Lss2" "QY_Lss3"
## [71] "QY_Lss4" "QY_Lss5" "QY_Lss6" "FvFm_Lss1" "FvFm_Lss2"
## [76] "FvFm_Lss3" "FvFm_Lss4" "FvFm_Lss5" "FvFm_Lss6" "NPQ_Lss1"
## [81] "NPQ_Lss2" "NPQ_Lss3" "NPQ_Lss4" "NPQ_Lss5" "NPQ_Lss6"
## [86] "Temp.avg" "Temp.stddev" "Temp.median" "Temp.min" "Temp.max"
## [91] "decoded"for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=PERIMETER_MM, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab("Perimeter (mm)") + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_Perimeter_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = PERIMETER_MM, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab("Perimeter (mm)") + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_Perimeter_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=ROUNDNESS, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab("Roundness (a.u.))") + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_Roundness_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = ROUNDNESS, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab("Roundness (a.u.))") + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_Roundness_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=COMPACTNESS, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab("Compactness (a.u.))") + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_Compactness_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = COMPACTNESS, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab("Compactness (a.u.))") + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_Compactness_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=ECCENTRICITY, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab("Eccentricity (a.u.))") + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_Eccentricity_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = ECCENTRICITY, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab("Eccentricity (a.u.))") + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_Eccentricity_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=RMS, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab("Rotational Mass Symmetry (a.u.))") + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_RMS_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = RMS, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab("Rotational Mass Symmetry (a.u.))") + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_RMS_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=SOL, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab("Slenderness of Leaves (a.u.))") + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_SOL_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = SOL, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab("Slenderness of Leaves (a.u.))") + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0", hide.ns = T)
my_box_plot
pdf(paste("COP1_SOL_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Then - let’s have a look at the Chlorophyll Fluorescence parameters too:
# Minimal Fluorescence
trait <- "Fo"
trait_description <- "Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
# Maximal Fluorescence
trait <- "Fm"
trait_description <- "Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
# Variable Fluorescence
trait <- "Fv"
trait_description <- "Variable Fluorescence (Fm - Fo)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
# Max Quantum Yield
trait <- "QY_max"
trait_description <- "Maximum Quantum Yield (Fv/Fm)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_max, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_max, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Then - the FC for light-adapted ones
# Minimal Fluorescence
trait <- "Fo_Lss1"
trait_description <- "Light Adapted Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fo_Lss2"
trait_description <- "Light Adapted Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fo_Lss3"
trait_description <- "Light Adapted Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fo_Lss4"
trait_description <- "Light Adapted Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fo_Lss5"
trait_description <- "Light Adapted Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fo_Lss6"
trait_description <- "Light Adapted Minimal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fo_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fo_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Maximal fluorescence
trait <- "Fm_Lss1"
trait_description <- "Light Adapted Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fm_Lss2"
trait_description <- "Light Adapted Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fm_Lss3"
trait_description <- "Light Adapted Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fm_Lss4"
trait_description <- "Light Adapted Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fm_Lss5"
trait_description <- "Light Adapted Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fm_Lss6"
trait_description <- "Light Adapted Maximal Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fm_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fm_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Steady-state Fluorescence
trait <- "Ft_Lss1"
trait_description <- "Light Adapted Steady-state Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Ft_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Ft_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Ft_Lss2"
trait_description <- "Light Adapted Steady-state Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Ft_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Ft_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Ft_Lss3"
trait_description <- "Light Adapted Steady-state Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Ft_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Ft_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Ft_Lss4"
trait_description <- "Light Adapted Steady-state Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Ft_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Ft_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Ft_Lss5"
trait_description <- "Light Adapted Steady-state Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Ft_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Ft_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Ft_Lss6"
trait_description <- "Light Adapted Steady-state Fluorescence (a.u.)"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Ft_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Ft_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Variable Fluorescence
trait <- "Fv_Lss1"
trait_description <- "Light Adapted Variable Fluorescence (Fm' - Fo')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fv_Lss2"
trait_description <- "Light Adapted Variable Fluorescence (Fm' - Fo')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fv_Lss3"
trait_description <- "Light Adapted Variable Fluorescence (Fm' - Fo')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fv_Lss4"
trait_description <- "Light Adapted Variable Fluorescence (Fm' - Fo')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fv_Lss5"
trait_description <- "Light Adapted Variable Fluorescence (Fm' - Fo')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "Fv_Lss6"
trait_description <- "Light Adapted Variable Fluorescence (Fm' - Fo')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=Fv_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = Fv_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Photochemical quenching in steady state
trait <- "qP_Lss1"
trait_description <- "Photochemical Quenching in steady-state ((Fm'-Ft)/Fv')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=qP_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = qP_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "qP_Lss2"
trait_description <- "Photochemical Quenching in steady-state ((Fm'-Ft)/Fv')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=qP_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = qP_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "qP_Lss3"
trait_description <- "Photochemical Quenching in steady-state ((Fm'-Ft)/Fv')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=qP_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = qP_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "qP_Lss4"
trait_description <- "Photochemical Quenching in steady-state ((Fm'-Ft)/Fv')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=qP_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = qP_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "qP_Lss5"
trait_description <- "Photochemical Quenching in steady-state ((Fm'-Ft)/Fv')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=qP_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = qP_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "qP_Lss6"
trait_description <- "Photochemical Quenching in steady-state ((Fm'-Ft)/Fv')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=qP_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = qP_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Non-photochemical quenching
trait <- "NPQ_Lss1"
trait_description <- "Non-Photochemical Quenching ((Fm - Fm')/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=NPQ_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = NPQ_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "NPQ_Lss2"
trait_description <- "Non-Photochemical Quenching ((Fm - Fm')/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=NPQ_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = NPQ_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "NPQ_Lss3"
trait_description <- "Non-Photochemical Quenching ((Fm - Fm')/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=NPQ_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = NPQ_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "NPQ_Lss4"
trait_description <- "Non-Photochemical Quenching ((Fm - Fm')/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=NPQ_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = NPQ_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "NPQ_Lss5"
trait_description <- "Non-Photochemical Quenching ((Fm - Fm')/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=NPQ_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = NPQ_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "NPQ_Lss6"
trait_description <- "Non-Photochemical Quenching ((Fm - Fm')/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=NPQ_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = NPQ_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Quantum yield in steady state
trait <- "FvFm_Lss1"
trait_description <- "Quantum Yield of PSII in Steady-State (Fv'/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=FvFm_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = FvFm_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "FvFm_Lss2"
trait_description <- "Quantum Yield of PSII in Steady-State (Fv'/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=FvFm_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = FvFm_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "FvFm_Lss3"
trait_description <- "Quantum Yield of PSII in Steady-State (Fv'/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=FvFm_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = FvFm_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "FvFm_Lss4"
trait_description <- "Quantum Yield of PSII in Steady-State (Fv'/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=FvFm_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = FvFm_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "FvFm_Lss5"
trait_description <- "Quantum Yield of PSII in Steady-State (Fv'/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=FvFm_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = FvFm_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "FvFm_Lss6"
trait_description <- "Quantum Yield of PSII in Steady-State (Fv'/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=FvFm_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = FvFm_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Steady-state PSII QY
trait <- "QY_Lss1"
trait_description <- "Steady-State Quantum Yield ((Fo'-Ft)/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_Lss1, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_Lss1, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "QY_Lss2"
trait_description <- "Steady-State Quantum Yield ((Fo'-Ft)/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_Lss2, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_Lss2, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "QY_Lss3"
trait_description <- "Steady-State Quantum Yield ((Fo'-Ft)/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_Lss3, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_Lss3, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "QY_Lss4"
trait_description <- "Steady-State Quantum Yield ((Fo'-Ft)/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_Lss4, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_Lss4, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "QY_Lss5"
trait_description <- "Steady-State Quantum Yield ((Fo'-Ft)/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_Lss5, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_Lss5, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}
trait <- "QY_Lss6"
trait_description <- "Steady-State Quantum Yield ((Fo'-Ft)/Fm')"
for(i in 1:11){
final_day <- subset(FvFm_data, FvFm_data$days == unique(FvFm_data$days)[i])
Area_graph <- ggplot(data=final_day, aes(x= decoded, y=QY_Lss6, colour = decoded))
Area_graph <- Area_graph +geom_beeswarm(alpha=0.6, priority = "density")
Area_graph <- Area_graph + stat_summary(fun=mean, size=2, geom = "point", color = "black")
Area_graph <- Area_graph + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "deeppink4", "darkviolet", "blueviolet", "darkviolet",
"darkviolet","cyan4", "cyan4", "maroon4", "maroon4", "maroon4", "plum4"))
Area_graph <- Area_graph + theme(legend.position="none")+ labs(color = "Treatment")
Area_graph <- Area_graph + ylab(trait_description) + xlab("")
Area_graph <- Area_graph + stat_compare_means(ref.group = "Col-0", label = "p.signif", method = "t.test", hide.ns = T)
Area_graph <- Area_graph + theme(axis.text.x = element_text(angle=90, vjust=0.5))
pdf(paste("FvFm_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(Area_graph)
dev.off()
final_day <- subset(COP_data, COP_data$days == unique(COP_data$days)[i])
my_box_plot <- ggplot(data = final_day, mapping = aes(x = decoded, y = QY_Lss6, colour = decoded))
my_box_plot <- my_box_plot + geom_beeswarm(alpha=0.6, priority = "density")
my_box_plot <- my_box_plot + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
my_box_plot <- my_box_plot + theme(legend.position = "none") + labs(color = "Treatment")
my_box_plot <- my_box_plot + ylab(trait_description) + xlab("")
my_box_plot <- my_box_plot + facet_wrap(~ Treatment) + scale_colour_manual(values=c("steelblue", "sienna3", "sienna3", "deeppink4", "deeppink4"))
my_box_plot <- my_box_plot + theme(axis.text.x = element_text(angle=90, vjust=0.5))
my_box_plot <- my_box_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0")
my_box_plot
pdf(paste("COP1_", trait, "_", unique(FvFm_data$days)[i], "_das.pdf"), width=20, height = 10)
plot(my_box_plot)
dev.off()
}Summary graphs
OK - it is still quite a lot of graphs to look at so why don’t we loop the t-test and see which of the studied mutants show MOST differences with Col-0?
head(data_clean)unique(data_clean$days)## [1] 6 8 7 2 11 3 9 10 5 1 4 0data_clean$days <- as.numeric(data_clean$days)
unique(data_clean$days)[1]## [1] 6names <- c(text="decoded", "Treatment", "phenotype", "day", "pval", "diff")
differences <- data.frame()
for (k in names) differences[[k]] <- as.character()
temp <- data.frame()
for (k in names) temp[[k]] <- as.character()
data_clean_c <- subset(data_clean, data_clean$Treatment == "Control")
for(i in 1:12){
final_day <- subset(data_clean_c, data_clean_c$days == unique(data_clean_c$days)[i])
for(m in 2:15){
Col <- subset(final_day, final_day$decoded == unique(final_day$decoded)[1])
mutant <- subset(final_day, final_day$decoded == unique(final_day$decoded)[m])
dim(mutant)
for(t in 16:86){
base <- Col[,t]
test <- mutant[,t]
test
if(length(test) > 1){
temp[1,1] <- as.character(unique(mutant$decoded))
temp[1,2] <- as.character(unique(mutant$Treatment))
temp[1,3] <- colnames(mutant)[t]
temp[1,4] <- as.character(unique(mutant$days))
temp[1,5] <- t.test(base, test)$p.value
temp[1,6] <- (mean(base) - mean(test))
differences <- rbind(differences, temp)
}}}}
Control <- differences
data_clean_s <- subset(data_clean, data_clean$Treatment == "Salt")
for(i in 1:12){
final_day <- subset(data_clean_s, data_clean_s$days == unique(data_clean_s$days)[i])
for(m in 2:15){
Col <- subset(final_day, final_day$decoded == unique(final_day$decoded)[1])
mutant <- subset(final_day, final_day$decoded == unique(final_day$decoded)[m])
dim(mutant)
for(t in 16:86){
base <- Col[,t]
test <- mutant[,t]
test
if(length(test) > 1){
temp[1,1] <- as.character(unique(mutant$decoded))
temp[1,2] <- as.character(unique(mutant$Treatment))
temp[1,3] <- colnames(mutant)[t]
temp[1,4] <- as.character(unique(mutant$days))
temp[1,5] <- t.test(base, test)$p.value
temp[1,6] <- (mean(base) - mean(test))
differences <- rbind(differences, temp)
}}}}
Salt <- differences
differences <- rbind(Control, Salt)
dim(differences)## [1] 33228 6head(differences)tail(differences)differencesSo - we are only interested in the p-values that are lower than 0.05 anyways and the effects that are consistent
differences_2 <- subset(differences, differences$pval < 0.05)
dim(differences)## [1] 33228 6dim(differences_2)## [1] 4108 6length(unique(differences_2$decoded))## [1] 13for(i in 1:14){
temp <- subset(differences_2, differences_2$decoded == unique(differences_2$decoded)[i])
temp_c <- subset(temp, temp$Treatment == "Control")
temp_c <- subset(temp_c, temp_c$decoded == unique(temp_c$decoded)[i])
temp_s <- subset(temp, temp$Treatment == "Salt")
temp_s <- subset(temp_s, temp_s$decoded == unique(temp_s$decoded)[i])
print(unique(differences_2$decoded)[i])
print("Control")
print(dim(temp_c))
print("Salt")
print(dim(temp_s))
}## [1] "at5g64920-1"
## [1] "Control"
## [1] 208 6
## [1] "Salt"
## [1] 174 6
## [1] "at1g64270-2"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64290-1"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64290-2"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64290-3"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64280-1"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64295-1"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64295-2"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64300-3"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64300-4"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64290-4"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at1g64320-1"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] "at5g64920-2"
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6
## [1] NA
## [1] "Control"
## [1] 0 6
## [1] "Salt"
## [1] 0 6The above gives us an idea which mutant is “winning” - but we still dont know whether the differences are found in the traits we find relevant and in different time points.
For this - we need to re-shape the table - where we have the phenotypes in different collumns - just like in the original file:
head(differences)differences_3 <- differences[,c(1:5)]
differences_3differences_3$pval <- as.numeric(differences_3$pval)
library(reshape)##
## Attaching package: 'reshape'## The following object is masked from 'package:plotly':
##
## rename## The following object is masked from 'package:cowplot':
##
## stamp## The following objects are masked from 'package:reshape2':
##
## colsplit, melt, recastc_pval <- cast(differences_3, decoded + Treatment + day ~ phenotype, mean)## Using pval as value column. Use the value argument to cast to override this choicec_pvalwrite.csv(c_pval, "summary_pval.csv", row.names = F)Since it would also be nice to have the same table with DELTA - the difference between the Col-0 and the mutant line:
differences_4 <- differences[,c(1:4,6)]
head(differences_4)differences_4$diff <- as.numeric(differences_4$diff)
c_dif <- cast(differences_4, decoded + Treatment + day ~ phenotype, mean)## Using diff as value column. Use the value argument to cast to override this choicehead(c_dif)write.csv(c_dif, "summary_diff.csv", row.names = F)Let’s see if we can plot these in a nice way
differences$pval <- as.numeric(differences$pval)
differences$day <- as.numeric(differences$day)
head(differences)mutant_1 <- subset(differences, differences$decoded == unique(differences$decoded)[1])
head(mutant_1)mutant_1$LOD <- -log10(mutant_1$pval)
max(mutant_1$LOD)## [1] 5.899392-log10(0.01)## [1] 2 Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = phenotype))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
# Area_lgraph <- Area_lgraph + stat_summary(fun.y = mean, aes(group=Treatment), size=1.5, geom="line", linetype="dashed")
# Area_lgraph <- Area_lgraph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group=Treatment), alpha=0.3)
Area_lgraph <- Area_lgraph + facet_grid(~ Treatment) + theme(legend.position='none')
# Area_lgraph <- Area_lgraph + labs(title = "Entire experiment") + scale_color_manual(values=c("turquoise3", "maroon3"))
# Area_lgraph <- Area_lgraph + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days After Stress")
Area_lgraph
the above graph isnt too bad - but it is still too noisy to read anything from it - so let’s maybe separate the phenotypes into different categories:
unique(mutant_1$phenotype)## [1] "AREA_PX" "AREA_MM" "PERIMETER_PX" "PERIMETER_MM" "ROUNDNESS"
## [6] "ROUNDNESS2" "ISOTROPY" "COMPACTNESS" "ECCENTRICITY" "RMS"
## [11] "SOL" "Size" "Fo" "Fm" "Fm_Lss1"
## [16] "Fm_Lss2" "Fm_Lss3" "Fm_Lss4" "Fm_Lss5" "Fm_Lss6"
## [21] "Ft_Lss1" "Ft_Lss2" "Ft_Lss3" "Ft_Lss4" "Ft_Lss5"
## [26] "Ft_Lss6" "Fo_Lss1" "Fo_Lss2" "Fo_Lss3" "Fo_Lss4"
## [31] "Fo_Lss5" "Fo_Lss6" "Fq_Lss1" "Fq_Lss2" "Fq_Lss3"
## [36] "Fq_Lss4" "Fq_Lss5" "Fq_Lss6" "Fv" "Fv_Lss1"
## [41] "Fv_Lss2" "Fv_Lss3" "Fv_Lss4" "Fv_Lss5" "Fv_Lss6"
## [46] "qP_Lss1" "qP_Lss2" "qP_Lss3" "qP_Lss4" "qP_Lss5"
## [51] "qP_Lss6" "QY_max" "QY_Lss1" "QY_Lss2" "QY_Lss3"
## [56] "QY_Lss4" "QY_Lss5" "QY_Lss6" "FvFm_Lss1" "FvFm_Lss2"
## [61] "FvFm_Lss3" "FvFm_Lss4" "FvFm_Lss5" "FvFm_Lss6" "NPQ_Lss1"
## [66] "NPQ_Lss2" "NPQ_Lss3" "NPQ_Lss4" "NPQ_Lss5" "NPQ_Lss6"
## [71] "Temp.avg"morphometry <- c("AREA_PX", "AREA_MM", "PERIMETER_PX", "PERIMETER_MM", "ROUNDNESS", "ROUNDNESS2", "ISOTROPY", "COMPACTNESS", "ECCENTRICITY", "RMS", "SOL")
FC_dark <- c("Fo", "Fm", "Fv", "QY_max")
FC_light <- c("Fm_Lss1", "Fm_Lss2", "Fm_Lss3", "Fm_Lss4", "Fm_Lss5", "Fm_Lss6", "Ft_Lss1", "Ft_Lss2", "Ft_Lss3", "Ft_Lss4", "Ft_Lss5", "Ft_Lss6", "Fo_Lss1", "Fo_Lss2", "Fo_Lss3", "Fo_Lss4", "Fo_Lss5", "Fo_Lss6",
"Fv_Lss1", "Fv_Lss2", "Fv_Lss3", "Fv_Lss4", "Fv_Lss5", "Fv_Lss6", "QY_Lss1", "QY_Lss2", "QY_Lss3", "QY_Lss4", "QY_Lss5", "QY_Lss6", "FvFm_Lss1", "FvFm_Lss2", "FvFm_Lss3", "FvFm_Lss4", "FvFm_Lss5",
"FvFm_Lss6")
Quenching <- c("qP_Lss1", "qP_Lss2", "qP_Lss3", "qP_Lss4", "qP_Lss5", "qP_Lss6", "NPQ_Lss1", "NPQ_Lss2", "NPQ_Lss3", "NPQ_Lss4", "NPQ_Lss5", "NPQ_Lss6")
mutant_1$type <- "none"
for(a in 1:nrow(mutant_1)){
if(mutant_1$phenotype[a] %in% morphometry){
mutant_1$type[a] <- "morphometry"
}
if(mutant_1$phenotype[a] %in% FC_dark){
mutant_1$type[a] <- "FC_dark"
}
if(mutant_1$phenotype[a] %in% FC_light){
mutant_1$type[a] <- "FC_light"
}
if(mutant_1$phenotype[a] %in% Quenching){
mutant_1$type[a] <- "quenching"
}
}
Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = phenotype))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + facet_grid(type ~ Treatment) + theme(legend.position='none')
Area_lgraph <- Area_lgraph + ylab("-log10(p-value)") + xlab("Days After Stress")
Area_lgraph
the graph above looks pretty sweet - though I am not sure if I want to include “none” in my loop
curious <- subset(mutant_1, mutant_1$type == "none")
unique(curious$phenotype)## [1] "Size" "Fq_Lss1" "Fq_Lss2" "Fq_Lss3" "Fq_Lss4" "Fq_Lss5" "Fq_Lss6"
## [8] "Temp.avg"mutant_1 <- subset(mutant_1, mutant_1$type != "none" )
Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = phenotype))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + facet_grid(type ~ Treatment) + theme(legend.position='none')
Area_lgraph <- Area_lgraph + ylab("-log10(p-value)") + xlab("Days After Stress")
Area_lgraph
OK - that does look better - but what I still would like to include is the effect size
First I need to scale the effect (difference between Col-0 and my mutant) per individual phenotype - best to do it in c_diff file
Then I need to merge this file together with my p-values
head(c_dif)dim(c_dif)## [1] 312 74c_dif_new <- c_dif
c_dif_new[,4:74] <- scale(c_dif[,4:74])
dif_new <- melt(c_dif_new, id=c("decoded", "Treatment", "day"))
colnames(dif_new)[4] <- "norm_dif"
m_c_pval <- melt(c_pval, id=c("decoded", "Treatment", "day"))
colnames(m_c_pval)[4] <- "pval"
all <- merge(m_c_pval, dif_new, by=c("decoded", "Treatment", "day", "phenotype"))
head(all)morphometry <- c("AREA_PX", "AREA_MM", "PERIMETER_PX", "PERIMETER_MM", "ROUNDNESS", "ROUNDNESS2", "ISOTROPY", "COMPACTNESS", "ECCENTRICITY", "RMS", "SOL")
FC_dark <- c("Fo", "Fm", "Fv", "QY_max")
FC_light <- c("Fm_Lss1", "Fm_Lss2", "Fm_Lss3", "Fm_Lss4", "Fm_Lss5", "Fm_Lss6", "Ft_Lss1", "Ft_Lss2", "Ft_Lss3", "Ft_Lss4", "Ft_Lss5", "Ft_Lss6", "Fo_Lss1", "Fo_Lss2", "Fo_Lss3", "Fo_Lss4", "Fo_Lss5", "Fo_Lss6",
"Fv_Lss1", "Fv_Lss2", "Fv_Lss3", "Fv_Lss4", "Fv_Lss5", "Fv_Lss6", "QY_Lss1", "QY_Lss2", "QY_Lss3", "QY_Lss4", "QY_Lss5", "QY_Lss6", "FvFm_Lss1", "FvFm_Lss2", "FvFm_Lss3", "FvFm_Lss4", "FvFm_Lss5",
"FvFm_Lss6")
Quenching <- c("qP_Lss1", "qP_Lss2", "qP_Lss3", "qP_Lss4", "qP_Lss5", "qP_Lss6", "NPQ_Lss1", "NPQ_Lss2", "NPQ_Lss3", "NPQ_Lss4", "NPQ_Lss5", "NPQ_Lss6")
all$type <- "none"
for(a in 1:nrow(all)){
if(all$phenotype[a] %in% morphometry){
all$type[a] <- "morphometry"
}
if(all$phenotype[a] %in% FC_dark){
all$type[a] <- "FC_dark"
}
if(all$phenotype[a] %in% FC_light){
all$type[a] <- "FC_light"
}
if(all$phenotype[a] %in% Quenching){
all$type[a] <- "quenching"
}
}
all <- subset(all, all$type != "none" )
all$LOD <- -log10(all$pval)
head(all)mutant_1 <- subset(all, all$decoded == unique(all$decoded)[1])
Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = norm_dif))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + facet_grid(type ~ Treatment) + theme(legend.position='none')
Area_lgraph <- Area_lgraph + ylab("-log10(p-value)") + xlab("Days After Stress")
Area_lgraph
While the above graph is pretty sweet - we still dont see the LARGE difference between negative large effect and positive large effect
maybe I could use different scale?
And maybe let’s add horizontal lines indicating where the p-value threshold is for 0.05 and 0.01?
Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = norm_dif))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + facet_grid(type ~ Treatment) + scale_colour_gradient2()
Area_lgraph <- Area_lgraph + ylab("-log10(p-value)") + xlab("Days After Stress")
Area_lgraph <- Area_lgraph + geom_hline(yintercept=-log10(0.05), linetype="dashed", color = "black", size = 0.3)
Area_lgraph <- Area_lgraph + geom_hline(yintercept=-log10(0.01), linetype="dashed", color = "black", size = 0.5)
Area_lgraph
ok - that looks pretty good to me - lets add the mutant name in the graph title and loop it for all the mutants in this experiment:
Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = norm_dif))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + facet_grid(type ~ Treatment) + scale_colour_gradient2()
Area_lgraph <- Area_lgraph + ylab("-log10(p-value)") + xlab("Days After Stress") + ggtitle(unique(all$decoded)[1])
Area_lgraph <- Area_lgraph + geom_hline(yintercept=-log10(0.05), linetype="dashed", color = "black", size = 0.3)
Area_lgraph <- Area_lgraph + geom_hline(yintercept=-log10(0.01), linetype="dashed", color = "black", size = 0.5)
Area_lgraph
Loop it baby!
length(unique(all$decoded))## [1] 13all$day <- as.numeric(all$day)
head(all)unique(all$Treatment)## [1] "Control" "Salt"unique(all$type)## [1] "morphometry" "FC_dark" "FC_light" "quenching"for(m in 1:13){
mutant_1 <- subset(all, all$decoded == unique(all$decoded)[m])
Area_lgraph <- ggplot(data=mutant_1, aes(x= day, y=LOD, group = phenotype, color = norm_dif))
Area_lgraph <- Area_lgraph + geom_line(alpha=0.5)
Area_lgraph <- Area_lgraph + facet_grid(type ~ Treatment) + scale_colour_gradient2()
Area_lgraph <- Area_lgraph + ylab("-log10(p-value)") + xlab("Days After Stress") + ggtitle(unique(all$decoded)[m])
Area_lgraph <- Area_lgraph + geom_hline(yintercept=-log10(0.05), linetype="dashed", color = "black", size = 0.3)
Area_lgraph <- Area_lgraph + geom_hline(yintercept=-log10(0.01), linetype="dashed", color = "black", size = 0.5)
Area_lgraph
pdf(paste("Sig_and_effect_graph_",unique(all$decoded)[m], ".pdf", sep=""), width=10, height = 12)
plot(Area_lgraph)
dev.off()
}Visualizing only the mutants I want to - Fv’/Fm’ locus:
If you would like to visualize changes over time in other parameters - including Area, morphology, Fc, you can run the following timeseries graph, which will test e.g. the difference between your treatment vs. control across the genotypes of interest.
So first prepare your data
interesting_mutants <- c("Col-0", "at1g64300-3", "at1g64300-4")
FvFm_data <- subset(FvFm_data, FvFm_data$decoded %in% interesting_mutants)
head(FvFm_data)FvFm_data$decoded <- factor(FvFm_data$decoded, levels = c("Col-0", "at1g64300-3", "at1g64300-4"))mutant_300_3 <- c("Col-0", "at1g64300-3")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_3)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-3"))
mut_3003_Area <- ggline(temp_data, x = "days", y = "AREA_MM", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3003_Area <- mut_3003_Area + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3003_Area <- mut_3003_Area + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-3")
mut_3003_Area <- mut_3003_Area + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days after stress")
mut_3003_Area <- mut_3003_Area + theme(legend.position = "none") + ylim(0, 1300)
mut_3003_Area
mutant_300_4 <- c("Col-0", "at1g64300-4")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_4)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-4"))
mut_3004_Area <- ggline(temp_data, x = "days", y = "AREA_MM", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3004_Area <- mut_3004_Area + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3004_Area <- mut_3004_Area + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-4")
mut_3004_Area <- mut_3004_Area + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days after stress")
mut_3004_Area <- mut_3004_Area + theme(legend.position = "none") + ylim(0, 1300)
mut_3004_Area
# temp_dataLight adapted Fv/Fm @ Lss4
mutant_300_3 <- c("Col-0", "at1g64300-3")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_3)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-3"))
mut_3003_QY_Lss4 <- ggline(temp_data, x = "days", y = "QY_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3003_QY_Lss4 <- mut_3003_QY_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3003_QY_Lss4 <- mut_3003_QY_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-3")
mut_3003_QY_Lss4 <- mut_3003_QY_Lss4 + ylab(expression(paste("Fv'/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_3003_QY_Lss4 <- mut_3003_QY_Lss4 + theme(legend.position = "none") + ylim(0, 0.35)
mut_3003_QY_Lss4## Warning: Removed 1 rows containing non-finite values (stat_summary).## Warning: Removed 1 rows containing non-finite values (stat_compare_means).
mutant_300_4 <- c("Col-0", "at1g64300-4")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_4)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-4"))
mut_3004_QY_Lss4 <- ggline(temp_data, x = "days", y = "QY_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3004_QY_Lss4 <- mut_3004_QY_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3004_QY_Lss4 <- mut_3004_QY_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-4")
mut_3004_QY_Lss4 <- mut_3004_QY_Lss4 + ylab(expression(paste("Fv'/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_3004_QY_Lss4 <- mut_3004_QY_Lss4 + theme(legend.position = "none") + ylim(0, 0.35)
mut_3004_QY_Lss4
Dark adapted Fv/Fm
mutant_300_3 <- c("Col-0", "at1g64300-3")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_3)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-3"))
mut_3003_QY_max <- ggline(temp_data, x = "days", y = "QY_max", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3003_QY_max <- mut_3003_QY_max + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3003_QY_max <- mut_3003_QY_max + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-3")
mut_3003_QY_max <- mut_3003_QY_max + ylab("QY max (Fv / Fm)") + xlab("Days after stress")
mut_3003_QY_max <- mut_3003_QY_max + theme(legend.position = "none") + ylim(0.6, 0.85)
mut_3003_QY_max
mutant_300_4 <- c("Col-0", "at1g64300-4")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_4)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-4"))
mut_3004_QY_max <- ggline(temp_data, x = "days", y = "QY_max", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3004_QY_max <- mut_3004_QY_max + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3004_QY_max <- mut_3004_QY_max + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-4")
mut_3004_QY_max <- mut_3004_QY_max + ylab("QY max (Fv / Fm)") + xlab("Days after stress")
mut_3004_QY_max <- mut_3004_QY_max + theme(legend.position = "none") + ylim(0.6, 0.85)
mut_3004_QY_max
NPQ
mutant_300_3 <- c("Col-0", "at1g64300-3")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_3)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-3"))
mut_3003_NPQ_Lss4 <- ggline(temp_data, x = "days", y = "NPQ_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3003_NPQ_Lss4 <- mut_3003_NPQ_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3003_NPQ_Lss4 <- mut_3003_NPQ_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-3")
mut_3003_NPQ_Lss4 <- mut_3003_NPQ_Lss4 + ylab(expression(paste("NPQ (Fm - Fm')/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_3003_NPQ_Lss4 <- mut_3003_NPQ_Lss4 + theme(legend.position = "none") + ylim(0, 2)
mut_3003_NPQ_Lss4## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).
mutant_300_4 <- c("Col-0", "at1g64300-4")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_4)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-4"))
mut_3004_NPQ_Lss4 <- ggline(temp_data, x = "days", y = "NPQ_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3004_NPQ_Lss4 <- mut_3004_NPQ_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3004_NPQ_Lss4 <- mut_3004_NPQ_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-4")
mut_3004_NPQ_Lss4 <- mut_3004_NPQ_Lss4 + ylab(expression(paste("NPQ (Fm - Fm')/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_3004_NPQ_Lss4 <- mut_3004_NPQ_Lss4 + theme(legend.position = "none") + ylim(0, 2)
mut_3004_NPQ_Lss4## Warning: Removed 2 rows containing non-finite values (stat_summary).
## Warning: Removed 2 rows containing non-finite values (stat_compare_means).
qP
mutant_300_3 <- c("Col-0", "at1g64300-3")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_3)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-3"))
mut_3003_qP_Lss4 <- ggline(temp_data, x = "days", y = "qP_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3003_qP_Lss4 <- mut_3003_qP_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3003_qP_Lss4 <- mut_3003_qP_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-3")
mut_3003_qP_Lss4 <- mut_3003_qP_Lss4 + ylab(expression(paste("qP (Fm' - Ft)/(Fm' - Fo') (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_3003_qP_Lss4 <- mut_3003_qP_Lss4 + theme(legend.position = "none") + ylim(0.45, 0.8)
mut_3003_qP_Lss4## Warning: Removed 12 rows containing non-finite values (stat_summary).## Warning: Removed 12 rows containing non-finite values (stat_compare_means).
mutant_300_4 <- c("Col-0", "at1g64300-4")
temp_data <- subset(FvFm_data, FvFm_data$decoded %in% mutant_300_4)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at1g64300-4"))
mut_3004_qP_Lss4 <- ggline(temp_data, x = "days", y = "qP_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_3004_qP_Lss4 <- mut_3004_qP_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_3004_qP_Lss4 <- mut_3004_qP_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at1g64300-4")
mut_3004_qP_Lss4 <- mut_3004_qP_Lss4 + ylab(expression(paste("qP (Fm' - Ft)/(Fm' - Fo') (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_3004_qP_Lss4 <- mut_3004_qP_Lss4 + theme(legend.position = "none") + ylim(0.45, 0.8)
mut_3004_qP_Lss4## Warning: Removed 9 rows containing non-finite values (stat_summary).## Warning: Removed 9 rows containing non-finite values (stat_compare_means).
Super - so now let’s combine all of this into one figure:
pdf("Figure_MAIN_mutants_FvFm_locus.pdf", height = 15, width = 12)
plot_grid(mut_3003_Area, mut_3004_Area, mut_3003_QY_Lss4, mut_3004_QY_Lss4, mut_3003_NPQ_Lss4, mut_3004_NPQ_Lss4, ncol=2,
align = "hv", labels=c("AUTO"),
label_size = 24)## Warning: Removed 1 rows containing non-finite values (stat_summary).## Warning: Removed 1 rows containing non-finite values (stat_compare_means).## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).dev.off()## quartz_off_screen
## 2pdf("Figure_SUPPL_mutants_FvFm_locus.pdf", height = 10, width = 12)
plot_grid(mut_3003_QY_max, mut_3004_QY_max, mut_3003_qP_Lss4, mut_3004_qP_Lss4, ncol=2,
align = "hv", labels=c("AUTO"),
label_size = 24)## Warning: Removed 12 rows containing non-finite values (stat_summary).## Warning: Removed 12 rows containing non-finite values (stat_compare_means).## Warning: Removed 9 rows containing non-finite values (stat_summary).## Warning: Removed 9 rows containing non-finite values (stat_compare_means).dev.off()## quartz_off_screen
## 2Visualizing only the mutants I want to - QY max locus:
If you would like to visualize changes over time in other parameters - including Area, morphology, Fc, you can run the following timeseries graph, which will test e.g. the difference between your treatment vs. control across the genotypes of interest.
So first prepare your data
unique(all$decoded)## [1] "at1g64270-2" "at1g64280-1" "at1g64290-1" "at1g64290-2" "at1g64290-3"
## [6] "at1g64290-4" "at1g64295-1" "at1g64295-2" "at1g64300-3" "at1g64300-4"
## [11] "at1g64320-1" "at5g64920-1" "at5g64920-2"interesting_mutants <- c("Col-0", "at5g64920-1", "at5g64920-2")
COP_data <- subset(COP_data, COP_data$decoded %in% interesting_mutants)
head(COP_data)COP_data$decoded <- factor(COP_data$decoded, levels = c("Col-0", "at5g64920-1", "at5g64920-2"))mutant_920_1 <- c("Col-0", "at5g64920-1")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_1)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-1"))
mut_9201_Area <- ggline(temp_data, x = "days", y = "AREA_MM", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9201_Area <- mut_9201_Area + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9201_Area <- mut_9201_Area + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-1")
mut_9201_Area <- mut_9201_Area + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days after stress")
mut_9201_Area <- mut_9201_Area + theme(legend.position = "none") + ylim(0, 1300)
mut_9201_Area## Warning: Removed 1 rows containing non-finite values (stat_summary).## Warning: Removed 1 rows containing non-finite values (stat_compare_means).
mutant_920_2 <- c("Col-0", "at5g64920-2")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_2)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-2"))
mut_9202_Area <- ggline(temp_data, x = "days", y = "AREA_MM", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9202_Area <- mut_9202_Area + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9202_Area <- mut_9202_Area + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-2")
mut_9202_Area <- mut_9202_Area + ylab(expression(paste("Area (", mm^2, ")", sep = ""))) + xlab("Days after stress")
mut_9202_Area <- mut_9202_Area + theme(legend.position = "none") + ylim(0, 1300)
mut_9202_Area
# temp_dataLight adapted Fv/Fm @ Lss4
mutant_920_1 <- c("Col-0", "at5g64920-1")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_1)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-1"))
mut_9201_QY_Lss4 <- ggline(temp_data, x = "days", y = "QY_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9201_QY_Lss4 <- mut_9201_QY_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9201_QY_Lss4 <- mut_9201_QY_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-1")
mut_9201_QY_Lss4 <- mut_9201_QY_Lss4 + ylab(expression(paste("Fv'/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_9201_QY_Lss4 <- mut_9201_QY_Lss4 + theme(legend.position = "none") + ylim(0, 0.35)
mut_9201_QY_Lss4
mutant_920_2 <- c("Col-0", "at5g64920-2")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_2)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-2"))
mut_9202_QY_Lss4 <- ggline(temp_data, x = "days", y = "QY_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9202_QY_Lss4 <- mut_9202_QY_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9202_QY_Lss4 <- mut_9202_QY_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-2")
mut_9202_QY_Lss4 <- mut_9202_QY_Lss4 + ylab(expression(paste("Fv'/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_9202_QY_Lss4 <- mut_9202_QY_Lss4 + theme(legend.position = "none") + ylim(0, 0.35)
mut_9202_QY_Lss4
Dark adapted Fv/Fm
mutant_920_1 <- c("Col-0", "at5g64920-1")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_1)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-1"))
mut_9201_QY_max <- ggline(temp_data, x = "days", y = "QY_max", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9201_QY_max <- mut_9201_QY_max + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9201_QY_max <- mut_9201_QY_max + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-1")
mut_9201_QY_max <- mut_9201_QY_max + ylab("QY max (Fv / Fm)") + xlab("Days after stress")
mut_9201_QY_max <- mut_9201_QY_max + theme(legend.position = "none") + ylim(0.6, 0.85)
mut_9201_QY_max
mutant_920_2 <- c("Col-0", "at5g64920-2")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_2)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-2"))
mut_9202_QY_max <- ggline(temp_data, x = "days", y = "QY_max", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9202_QY_max <- mut_9202_QY_max + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9202_QY_max <- mut_9202_QY_max + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-2")
mut_9202_QY_max <- mut_9202_QY_max + ylab("QY max (Fv / Fm)") + xlab("Days after stress")
mut_9202_QY_max <- mut_9202_QY_max + theme(legend.position = "none") + ylim(0.6, 0.85)
mut_9202_QY_max
NPQ
mutant_920_1 <- c("Col-0", "at5g64920-1")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_1)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-1"))
mut_9201_NPQ_Lss4 <- ggline(temp_data, x = "days", y = "NPQ_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9201_NPQ_Lss4 <- mut_9201_NPQ_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9201_NPQ_Lss4 <- mut_9201_NPQ_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-1")
mut_9201_NPQ_Lss4 <- mut_9201_NPQ_Lss4 + ylab(expression(paste("NPQ (Fm - Fm')/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_9201_NPQ_Lss4 <- mut_9201_NPQ_Lss4 + theme(legend.position = "none") + ylim(0, 2)
mut_9201_NPQ_Lss4## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).
mutant_920_2 <- c("Col-0", "at5g64920-2")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_2)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-2"))
mut_9202_NPQ_Lss4 <- ggline(temp_data, x = "days", y = "NPQ_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9202_NPQ_Lss4 <- mut_9202_NPQ_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9202_NPQ_Lss4 <- mut_9202_NPQ_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-2")
mut_9202_NPQ_Lss4 <- mut_9202_NPQ_Lss4 + ylab(expression(paste("NPQ (Fm - Fm')/Fm' (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_9202_NPQ_Lss4 <- mut_9202_NPQ_Lss4 + theme(legend.position = "none") + ylim(0, 2)
mut_9202_NPQ_Lss4## Warning: Removed 3 rows containing non-finite values (stat_summary).## Warning: Removed 3 rows containing non-finite values (stat_compare_means).
qP
mutant_920_1 <- c("Col-0", "at5g64920-1")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_1)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-1"))
mut_9201_qP_Lss4 <- ggline(temp_data, x = "days", y = "qP_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9201_qP_Lss4 <- mut_9201_qP_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9201_qP_Lss4 <- mut_9201_qP_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-1")
mut_9201_qP_Lss4 <- mut_9201_qP_Lss4 + ylab(expression(paste("qp (Fm' - Ft)/(Fm' - Fo') (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_9201_qP_Lss4 <- mut_9201_qP_Lss4 + theme(legend.position = "none") + ylim(0.45, 0.8)
mut_9201_qP_Lss4## Warning: Removed 15 rows containing non-finite values (stat_summary).## Warning: Removed 15 rows containing non-finite values (stat_compare_means).
mutant_920_2 <- c("Col-0", "at5g64920-2")
temp_data <- subset(COP_data, COP_data$decoded %in% mutant_920_2)
temp_data$decoded <- factor(temp_data$decoded, levels = c("Col-0", "at5g64920-2"))
mut_9202_qP_Lss4 <- ggline(temp_data, x = "days", y = "qP_Lss4", add = "mean_se", color = "decoded", facet.by = "Treatment")
mut_9202_qP_Lss4 <- mut_9202_qP_Lss4 + stat_compare_means(aes(group = decoded), label = "p.signif", method = "t.test", hide.ns = T)
mut_9202_qP_Lss4 <- mut_9202_qP_Lss4 + scale_colour_manual(values = c("steelblue", "firebrick3")) + ggtitle("at5g64920-2")
mut_9202_qP_Lss4 <- mut_9202_qP_Lss4 + ylab(expression(paste("qp (Fm' - Ft)/(Fm' - Fo') (400 umol ", m^-1," ", s^-1, ")", sep = ""))) + xlab("Days after stress")
mut_9202_qP_Lss4 <- mut_9202_qP_Lss4 + theme(legend.position = "none") + ylim(0.45, 0.8)
mut_9202_qP_Lss4## Warning: Removed 12 rows containing non-finite values (stat_summary).## Warning: Removed 12 rows containing non-finite values (stat_compare_means).
Super - so now let’s combine all of this into one figure:
pdf("Figure_MAIN_mutants_COP_locus.pdf", height = 15, width = 12)
plot_grid(mut_9201_Area, mut_9202_Area, mut_9201_QY_max, mut_9202_QY_max, mut_9201_QY_Lss4, mut_9202_QY_Lss4, ncol=2,
align = "hv", labels=c("AUTO"),
label_size = 24)## Warning: Removed 1 rows containing non-finite values (stat_summary).## Warning: Removed 1 rows containing non-finite values (stat_compare_means).dev.off()## quartz_off_screen
## 2pdf("Figure_SUPPL_mutants_COP_locus.pdf", height = 10, width = 12)
plot_grid(mut_9201_NPQ_Lss4, mut_9202_NPQ_Lss4, mut_9201_qP_Lss4, mut_9202_qP_Lss4, ncol=2,
align = "hv", labels=c("AUTO"),
label_size = 24)## Warning: Removed 2 rows containing non-finite values (stat_summary).## Warning: Removed 2 rows containing non-finite values (stat_compare_means).## Warning: Removed 3 rows containing non-finite values (stat_summary).## Warning: Removed 3 rows containing non-finite values (stat_compare_means).## Warning: Removed 15 rows containing non-finite values (stat_summary).## Warning: Removed 15 rows containing non-finite values (stat_compare_means).## Warning: Removed 12 rows containing non-finite values (stat_summary).## Warning: Removed 12 rows containing non-finite values (stat_compare_means).dev.off()## quartz_off_screen
## 2