20210126_col and duf-5 RSA

Experimental procedures:

Seeds were sterilized with 50% bleach for 10 min, rinsed 5 times with MQ sterile water, and incubated at 4°C for 24 h. ½ ms media + sucrose (including vitamins) was made for both control and salt with 75 and 125 mM NaCl. Salt added after PH adjustment.The seeds were germinated for 4 complete days at control plate, and then at d5, they were transferred to +/- salt plates, and scanned for 5 times, every other day. and then the samples were harvested after 2 weeks on salt plate for root and shoot FW. The Arabidopsis growth chamber (#34 at BTI) had 21 °C and 16 h light/8 h dark cycle, with 60% humidity.

getwd()

## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype"

setwd("C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype/")
list.files(pattern = ".csv")

## [1] "20211011_duf-5_growth_factors.csv"                                         
## [2] "20211013_duf-5_growth_factors.csv"                                         
## [3] "8-19-21 FW for duf160-5 correct genotype at 0 75 125 salt_2weeksOnSalt.csv"
## [4] "d11_duf160-5_correct_Genotype.csv"                                         
## [5] "d13_duf160-5_correct_Genotype.csv"                                         
## [6] "d5_duf160-5_correct_Genotype.csv"                                          
## [7] "d7_duf160-5_correct_Genotype.csv"                                          
## [8] "d9_duf160-5_correct_Genotype.csv"                                          
## [9] "Eric10282021R1and R2_analyzed v3 for R analysis.csv"

my_data_d5 <-  read.csv("d5_duf160-5_correct_Genotype.csv")
my_data_d7 <- read.csv("d7_duf160-5_correct_Genotype.csv")
my_data_d9 <- read.csv("d9_duf160-5_correct_Genotype.csv")
my_data_d11 <- read.csv("d11_duf160-5_correct_Genotype.csv")
my_data_d13 <- read.csv("d13_duf160-5_correct_Genotype.csv")

head(my_data_d5)

Let’s add collumn with “day” to each file

my_data_d5$day <- 5
my_data_d7$day <- 7
my_data_d9$day <- 9
my_data_d11$day <- 11
my_data_d13$day <- 13

head(my_data_d13)

head(my_data_d13)

Let’s fuse all the datasets into one day:

all_data <- rbind(my_data_d5, my_data_d7)
all_data <- rbind(all_data, my_data_d9)
all_data <- rbind(all_data, my_data_d11)
all_data <- rbind(all_data, my_data_d13)
all_data

not sure about volume column

all_data$volume

##    [1] 9.757187e-05 2.023650e-04 1.438000e-04 1.755444e-04 2.064508e-04
##    [6] 1.765218e-04 9.374577e-05 7.450450e-05 1.830703e-04 1.923150e-04
##   [11] 1.606648e-04 1.305041e-04 9.981600e-05 1.720967e-04 7.940373e-05
##   [16] 1.376774e-04 1.759550e-04 1.019220e-04 1.423480e-04 2.320353e-04
##   [21] 1.400037e-04 1.812327e-04 1.804584e-04 1.202397e-04 2.049711e-04
##   [26] 8.536267e-05 1.264245e-04 1.412855e-04 2.299503e-04 1.272371e-04
##   [31] 1.974961e-04 1.153182e-04 2.143371e-04 7.522177e-05 1.622333e-04
##   [36] 1.538703e-04 8.967490e-05 1.255405e-04 1.357484e-04 2.106462e-04
##   [41] 1.869395e-04 5.727559e-05 2.418873e-04 1.834263e-04 1.767178e-04
##   [46] 2.644990e-04 2.277732e-04 1.883566e-04 1.077252e-04 2.407867e-04
##   [51] 1.476659e-04 1.785541e-04 1.137637e-04 2.103030e-04 1.711279e-04
##   [56] 9.312112e-05 1.256246e-04 9.766885e-05 1.806066e-04 1.264996e-04
##   [61] 1.233396e-04 2.383901e-04 1.222241e-04 1.245329e-04 7.853537e-05
##   [66] 1.537319e-04 1.980204e-04 2.731020e-04 1.583624e-04 1.780714e-04
##   [71] 1.489569e-04 7.659701e-05 1.169085e-04 9.490533e-05 1.863052e-04
##   [76] 8.867576e-05 1.532231e-04 2.054825e-04 1.607869e-04 1.208111e-04
##   [81] 1.754314e-04 2.001989e-04 2.411097e-04 1.474010e-04 1.920829e-04
##   [86] 2.188218e-04 1.944366e-04 3.284304e-04 2.455221e-04 2.532808e-04
##   [91] 2.088573e-04 3.849337e-04 2.484897e-04 2.596349e-04 2.004633e-04
##   [96] 2.208709e-04 2.360114e-04 1.444193e-04 3.703367e-04 4.826237e-04
##  [101] 1.564793e-04 2.904967e-04 1.899899e-04 1.186592e-04 2.090785e-04
##  [106] 2.099565e-04 2.928106e-04 1.548332e-04 5.220217e-04 1.640176e-04
##  [111] 8.722520e-05 1.379332e-04 1.250652e-04 2.440958e-04 2.019842e-04
##  [116] 2.304835e-04 2.088859e-04 1.304615e-04 7.405933e-05 1.454187e-04
##  [121] 2.652408e-04 2.014629e-04 3.183657e-04 9.917068e-05 2.502511e-04
##  [126] 2.789775e-04 1.499615e-04 2.269621e-04 3.087931e-04 1.281305e-04
##  [131] 2.078926e-04 3.482695e-04 2.504992e-04 2.000644e-04 2.696717e-04
##  [136] 1.685382e-04 1.024173e-04 3.241956e-04 1.770014e-04 1.315722e-04
##  [141] 1.834174e-04 2.853536e-04 1.781890e-04 3.326060e-04 2.015242e-04
##  [146] 1.936388e-04 2.315432e-04 2.123550e-04 3.400456e-04 4.318253e-04
##  [151] 3.532774e-04 2.404851e-04 1.835203e-04 2.217218e-04 2.872059e-04
##  [156] 2.941592e-04 4.388150e-04 2.579832e-04 3.191605e-04 1.560890e-04
##  [161] 3.594656e-04 2.945844e-04 4.450700e-04 2.452178e-04 2.568444e-04
##  [166] 3.492114e-04 1.198657e-04 1.387509e-04 3.475597e-04 2.544951e-04
##  [171] 3.591226e-04 2.223670e-04 3.284863e-04 2.709593e-04 1.842775e-04
##  [176] 2.981191e-04 4.054314e-04 2.141025e-04 4.238581e-04 3.505872e-04
##  [181] 3.559895e-04 3.752877e-04 3.450470e-04 4.121310e-04 3.173700e-04
##  [186] 2.595203e-04 2.513145e-04 3.025897e-04 5.936564e-04 3.220714e-04
##  [191] 3.637692e-04 2.498468e-04 4.628286e-04 3.415106e-04 3.455597e-04
##  [196] 2.727937e-04 2.438914e-04 3.393235e-04 1.522192e-04 3.215932e-04
##  [201] 3.763886e-04 7.283411e-05 4.643029e-04 2.676768e-04 3.566412e-04
##  [206] 4.234594e-04 3.535557e-04 3.316650e-04 5.710781e-04 6.259991e-04
##  [211] 4.450156e-04 2.934034e-04 2.891565e-04 3.697878e-04 3.156153e-04
##  [216] 2.697796e-04 2.863432e-04 3.395472e-04 4.056026e-04 3.897182e-04
##  [221] 4.278538e-04 4.773438e-04 3.401567e-04 2.830748e-04 4.374466e-04
##  [226] 2.236786e-04 4.447382e-04 3.428957e-04 3.966654e-04 4.056813e-04
##  [231] 2.656458e-04 1.886845e-04 2.548916e-04 1.784707e-04 3.025739e-04
##  [236] 2.304293e-04 3.129840e-04 3.664955e-04 3.251935e-04 2.755409e-04
##  [241] 5.344377e-04 5.271930e-04 5.202424e-04 4.589039e-04 5.150403e-04
##  [246] 5.562073e-04 5.649372e-04 6.390096e-04 5.516810e-04 5.987581e-04
##  [251] 6.165474e-04 6.400820e-04 5.632870e-04 5.897801e-04 4.644870e-04
##  [256] 4.987524e-04 6.038590e-04 6.318138e-04 7.455230e-04 8.689984e-04
##  [261] 5.209952e-04 5.087976e-04 3.422052e-04 2.437867e-04 6.120439e-04
##  [266] 4.624423e-04 4.849324e-04 3.502401e-04 4.062299e-04 3.100269e-04
##  [271] 4.604266e-04 2.349865e-04 4.268468e-04 6.230560e-04 4.772115e-04
##  [276] 3.153766e-04 5.779539e-04 4.436983e-04 2.191629e-04 3.675327e-04
##  [281] 5.124084e-04 5.493463e-04 7.832596e-04 3.888257e-04 5.215041e-04
##  [286] 4.240707e-04 8.220308e-04 6.544414e-04 7.947800e-04 4.683950e-04
##  [291] 6.060534e-04 4.409942e-04 5.731185e-04 5.309519e-04 6.630310e-04
##  [296] 4.464482e-04 3.323554e-04 8.327838e-04 5.373205e-04 6.029232e-04
##  [301] 6.708117e-04 7.042931e-04 4.280680e-04 7.729826e-04 4.031038e-04
##  [306] 5.531586e-04 3.860116e-04 4.055632e-04 8.144309e-04 5.224986e-04
##  [311] 7.228071e-04 7.831895e-04 4.550083e-04 3.431930e-04 8.007865e-04
##  [316] 5.104898e-04 9.182931e-04 5.698361e-04 6.258995e-04 4.636286e-04
##  [321] 6.543954e-04 1.145788e-05 9.310067e-06 5.396419e-04 8.938368e-06
##  [326] 6.023935e-06 6.438397e-04 2.446387e-05 4.704523e-04 4.353337e-04
##  [331] 5.891506e-04 3.238933e-05 2.488460e-04 1.932602e-04 6.219612e-04
##  [336] 1.570247e-05 3.399164e-05 3.865681e-04 6.754660e-04 1.792330e-05
##  [341] 5.544598e-04 5.057130e-04 8.406862e-06 4.038218e-06 5.495818e-04
##  [346] 2.343277e-05 1.708076e-05 1.836988e-04 4.982606e-04 4.789702e-05
##  [351] 4.555616e-04 3.918819e-04 6.757654e-04 4.753470e-05 3.246765e-04
##  [356] 6.128836e-04 2.434252e-05 5.238371e-06 6.002774e-04 4.914740e-06
##  [361] 4.307979e-06 7.292958e-04 2.514142e-05 5.409922e-04 4.956669e-04
##  [366] 3.283547e-04 2.955365e-05 5.890080e-04 1.954374e-05 3.747900e-04
##  [371] 8.757475e-06 4.972355e-05 8.035306e-04 2.872305e-05 5.886802e-04
##  [376] 2.807822e-05 2.300338e-05 6.580176e-04 7.885918e-06 8.288583e-06
##  [381] 2.919736e-04 7.840985e-04 6.703297e-05 4.711527e-04 5.931948e-06
##  [386] 4.062779e-04 4.365929e-04 3.694008e-04 4.481273e-04 2.052479e-04
##  [391] 3.244069e-05 8.971572e-06 7.009201e-06 3.596219e-04 6.682676e-04
##  [396] 1.771554e-05 9.331310e-06 1.889404e-04 4.260737e-04 2.408216e-05
##  [401] 4.972060e-06 5.482944e-04 7.401646e-06 5.933513e-06 6.495889e-04
##  [406] 7.261611e-04 7.329378e-04 9.042555e-06 7.661116e-04 1.115074e-05
##  [411] 1.247429e-05 8.466743e-04 4.906911e-05 4.561231e-04 3.633081e-05
##  [416] 6.854371e-04 5.102868e-06 1.024713e-05 4.423822e-04 7.077033e-04
##  [421] 1.850059e-05 1.883563e-05 5.040568e-04 1.520433e-05 5.947329e-04
##  [426] 6.498185e-04 4.576568e-04 2.463169e-05 7.249930e-06 7.103451e-04
##  [431] 2.414193e-05 7.305361e-06 8.590714e-04 1.026086e-05 6.225064e-06
##  [436] 5.701916e-04 1.170881e-05 7.307157e-04 2.912936e-05 1.943139e-05
##  [441] 7.863563e-04 7.138629e-04 1.180397e-05 6.190776e-06 5.056185e-04
##  [446] 5.469815e-04 4.191084e-04 1.232165e-05 7.489189e-04 1.498254e-05
##  [451] 3.073732e-05 4.935491e-04 8.763422e-04 3.181045e-05 9.842468e-04
##  [456] 2.314845e-05 1.181032e-05 5.162543e-04 4.018683e-04 1.325002e-05
##  [461] 4.449426e-04 2.894383e-04 2.387237e-05 5.248325e-04 1.397439e-05
##  [466] 3.542392e-04 5.530650e-04 1.626512e-05 1.343306e-05 6.001052e-04
##  [471] 1.014025e-05 5.970646e-04 2.622208e-05 4.310967e-04 3.367582e-06
##  [476] 1.314632e-03 1.176301e-03 3.300117e-05 2.186539e-05 1.143849e-03
##  [481] 1.635976e-05 6.610080e-05 1.137115e-03 3.747009e-05 4.027124e-05
##  [486] 1.019896e-03 1.165598e-03 1.280221e-03 4.395144e-05 1.272465e-05
##  [491] 1.232886e-03 3.488073e-05 3.433035e-05 9.379481e-04 1.792810e-05
##  [496] 1.346385e-03 2.177026e-04 7.924096e-05 1.091250e-03 2.022034e-05
##  [501] 2.357670e-05 1.291459e-03 1.322101e-05 1.081820e-04 8.575574e-05
##  [506] 1.284860e-05 1.323158e-03 4.507412e-05 1.204085e-03 1.776127e-05
##  [511] 3.813027e-05 1.094721e-03 6.774213e-05 1.086721e-03 9.873028e-05
##  [516] 1.472453e-04 4.518995e-05 1.409177e-05 1.444498e-03 3.586775e-05
##  [521] 2.943293e-05 1.489163e-03 9.748930e-06 1.629892e-05 2.897842e-05
##  [526] 1.888726e-03 3.238852e-05 2.664300e-05 1.328427e-03 1.141023e-03
##  [531] 2.783177e-05 1.028899e-05 9.146679e-04 4.044225e-05 7.947810e-04
##  [536] 5.754252e-04 8.813568e-05 1.081800e-03 2.947672e-05 9.751206e-04
##  [541] 3.389301e-05 9.732833e-04 9.504992e-04 2.278963e-05 7.260359e-04
##  [546] 3.952567e-05 5.681312e-04 3.953932e-05 7.647485e-04 2.104882e-05
##  [551] 6.501758e-06 1.182093e-05 4.410811e-04 8.241182e-04 1.414890e-05
##  [556] 1.139794e-03 8.414214e-06 1.226934e-05 2.456829e-05 9.456021e-04
##  [561] 6.508101e-05 3.437146e-05 5.722619e-04 1.332749e-03 1.152294e-05
##  [566] 9.840545e-04 4.765011e-05 2.762629e-05 5.515328e-04 7.715945e-06
##  [571] 8.551028e-04 1.010353e-03 1.050865e-03 5.336613e-04 6.481996e-05
##  [576] 1.301022e-03 8.626780e-04 3.705791e-05 1.039991e-03 8.711822e-04
##  [581] 2.231030e-05 9.224963e-05 1.135320e-03 1.204818e-03 4.526831e-06
##  [586] 1.183342e-03 9.557556e-04 4.395288e-05 6.146953e-06 1.128993e-03
##  [591] 9.498662e-04 1.028365e-03 1.049164e-03 5.610001e-05 1.125703e-04
##  [596] 1.222094e-03 1.361693e-03 7.300834e-05 8.136274e-04 2.066166e-05
##  [601] 1.309635e-03 4.659156e-05 9.744610e-04 6.454850e-05 7.126578e-06
##  [606] 1.089336e-03 2.257401e-05 3.184273e-05 8.168527e-04 1.359961e-03
##  [611] 8.991076e-05 2.261965e-05 9.303106e-04 1.821197e-05 8.228441e-04
##  [616] 1.133628e-03 1.392535e-04 1.846422e-04 8.662216e-04 7.900352e-04
##  [621] 7.290354e-04 6.974151e-06 1.151464e-03 1.050565e-03 7.439858e-06
##  [626] 1.206164e-03 1.283193e-03 8.274691e-04 9.616172e-06 8.989719e-04
##  [631] 1.322289e-03 4.200169e-05 1.236975e-03 9.945058e-06 1.074483e-03
##  [636] 5.576378e-05 8.047100e-06 1.084536e-03 1.047231e-03 1.339151e-04
##  [641] 9.353787e-04 8.830189e-04 2.996481e-05 1.333478e-05 2.817084e-05
##  [646] 9.442945e-04 2.055346e-05 5.416423e-06 1.644636e-05 3.438702e-06
##  [651] 8.368691e-04 1.265809e-04 5.029306e-04 3.628388e-04 7.982152e-04
##  [656] 6.267192e-05 5.823791e-05 5.815406e-04 3.133309e-04 4.554757e-06
##  [661] 8.583629e-04 4.100473e-05 9.086670e-05 5.534623e-04 9.342367e-04
##  [666] 3.049609e-05 5.098359e-05 8.013739e-04 6.000325e-05 1.093647e-03
##  [671] 8.406862e-06 4.038218e-06 5.424799e-06 7.334989e-06 8.824389e-06
##  [676] 6.998095e-04 2.366583e-05 3.297174e-05 2.142473e-04 7.229004e-04
##  [681] 7.603665e-05 1.168385e-05 3.088632e-04 7.185209e-04 9.357053e-04
##  [686] 1.427110e-04 8.456294e-06 3.835208e-04 8.092667e-06 8.711788e-04
##  [691] 3.186829e-05 6.788929e-06 4.145218e-06 1.127105e-03 9.975679e-06
##  [696] 5.720909e-06 1.077584e-03 9.822783e-05 8.196708e-04 1.139286e-05
##  [701] 3.524415e-06 4.422150e-04 1.709030e-05 8.779903e-06 6.166407e-04
##  [706] 5.482843e-05 4.276971e-05 4.499042e-06 9.878122e-04 7.546124e-05
##  [711] 2.271667e-05 4.557949e-04 1.841763e-05 1.051823e-04 1.106859e-03
##  [716] 5.042680e-05 1.346026e-05 9.026202e-04 9.316504e-05 2.182937e-05
##  [721] 9.915683e-05 9.831820e-04 1.585396e-05 2.232860e-05 3.215482e-04
##  [726] 9.598402e-04 2.754235e-04 9.043896e-06 6.240538e-04 6.041489e-06
##  [731] 1.021390e-05 4.569812e-04 4.674154e-04 3.976248e-04 4.766976e-04
##  [736] 1.068017e-05 2.130505e-04 2.994717e-04 8.971572e-06 6.859008e-05
##  [741] 4.602217e-04 8.876769e-04 3.599880e-05 2.462778e-05 2.379265e-04
##  [746] 5.782502e-04 4.986447e-05 1.719027e-05 9.396474e-04 1.891789e-05
##  [751] 1.324169e-05 5.462591e-04 9.386696e-04 9.989277e-04 1.444129e-05
##  [756] 9.459988e-04 6.031971e-05 4.205599e-05 9.342200e-06 1.063685e-03
##  [761] 2.242103e-04 1.147909e-05 5.952466e-04 3.200792e-04 1.072901e-03
##  [766] 3.060505e-05 6.583188e-05 6.470286e-04 1.909960e-05 7.089940e-06
##  [771] 1.011550e-03 9.878749e-05 4.635109e-05 8.161031e-04 5.142891e-05
##  [776] 9.096366e-06 8.600766e-04 1.072916e-03 4.605707e-05 5.156944e-06
##  [781] 9.431437e-04 8.820864e-05 2.628013e-05 7.800183e-04 7.173396e-05
##  [786] 3.845024e-05 1.118440e-03 1.726708e-05 1.474658e-05 1.367033e-05
##  [791] 8.282011e-06 5.664604e-04 2.656623e-05 9.551395e-04 5.238140e-05
##  [796] 4.639325e-05 1.074016e-03 8.989738e-06 9.554657e-06 8.588891e-04
##  [801] 3.322134e-05 6.190776e-06 6.819617e-04 8.324615e-04 1.498044e-05
##  [806] 1.583066e-05 7.353924e-04 2.569952e-05 1.044612e-03 5.568974e-05
##  [811] 3.073732e-05 6.112034e-04 5.328042e-05 1.482740e-04 1.158171e-03
##  [816] 9.302122e-05 1.136711e-03 5.479271e-05 2.953877e-05 4.692507e-05
##  [821] 5.378379e-04 1.376630e-05 6.888536e-04 4.287515e-05 7.215416e-04
##  [826] 2.608718e-06 5.377984e-04 2.063571e-05 2.286183e-05 3.460107e-06
##  [831] 6.976210e-04 2.635603e-05 5.792518e-04 5.156895e-06 9.987906e-04
##  [836] 2.088843e-04 3.906998e-05 7.938500e-05 7.992131e-04 3.231096e-05
##  [841] 9.236046e-04 1.404089e-04 4.850946e-06 3.793760e-06 1.487348e-05
##  [846] 7.310046e-04 7.047245e-06 2.914407e-03 1.370106e-04 7.977718e-05
##  [851] 5.848774e-05 5.454208e-05 3.967754e-05 3.314681e-03 2.708663e-04
##  [856] 1.823525e-04 4.582053e-06 2.264902e-04 7.563630e-05 1.589224e-04
##  [861] 7.000491e-05 2.832344e-03 1.635976e-05 1.811479e-04 1.159464e-04
##  [866] 2.024952e-04 7.245714e-05 2.314107e-04 1.702122e-04 4.705587e-05
##  [871] 1.684643e-04 1.030491e-04 2.791859e-05 3.689628e-05 2.995147e-05
##  [876] 2.682170e-03 2.889994e-04 1.710006e-03 2.118609e-04 1.936200e-04
##  [881] 2.303980e-04 6.855668e-05 5.574504e-05 1.113180e-04 2.804105e-05
##  [886] 4.167958e-05 2.244913e-03 2.725916e-05 4.339907e-05 5.899093e-05
##  [891] 2.162864e-03 1.154328e-04 5.807625e-05 1.003951e-04 4.497427e-05
##  [896] 2.627664e-03 2.695011e-04 1.579934e-04 1.980988e-04 1.325230e-04
##  [901] 3.934385e-05 3.814883e-05 1.989209e-05 3.055171e-03 4.239489e-04
##  [906] 2.850527e-04 7.885731e-05 3.356001e-05 1.644594e-04 8.738765e-05
##  [911] 2.168034e-04 4.169058e-05 5.521295e-05 1.769391e-05 3.506583e-03
##  [916] 8.840292e-05 3.430080e-05 3.773859e-05 4.198263e-05 1.001702e-04
##  [921] 1.301196e-04 3.748422e-03 6.510406e-04 5.138717e-04 3.341521e-05
##  [926] 3.321646e-05 1.014211e-04 6.491673e-05 1.548182e-04 3.016469e-05
##  [931] 1.158900e-04 3.909095e-05 3.172826e-03 2.243213e-04 3.475157e-04
##  [936] 1.958640e-05 3.043327e-05 7.676704e-05 5.091085e-05 1.414674e-04
##  [941] 1.555225e-04 3.426167e-03 2.510771e-04 5.425750e-04 5.264340e-05
##  [946] 2.661605e-05 1.343963e-05 9.658450e-05 2.178119e-05 1.992845e-04
##  [951] 3.712902e-03 8.761040e-04 2.919659e-05 2.932157e-05 2.791063e-05
##  [956] 2.280483e-04 3.516327e-05 2.566245e-05 3.859862e-03 1.986317e-04
##  [961] 4.448110e-04 3.128724e-05 5.237045e-05 1.015552e-04 1.477129e-04
##  [966] 3.340408e-03 1.532568e-04 7.695649e-05 2.321142e-04 1.972726e-04
##  [971] 1.302218e-04 3.169215e-04 4.322709e-03 1.880514e-03 4.196724e-04
##  [976] 1.817790e-04 4.252370e-04 6.939270e-05 3.794239e-05 6.414313e-05
##  [981] 6.260809e-05 7.242493e-05 2.188673e-04 6.142180e-05 5.434628e-05
##  [986] 2.523524e-03 1.192591e-04 3.370843e-05 1.105450e-05 7.617524e-05
##  [991] 3.609209e-05 1.486575e-04 1.105164e-04 4.773152e-05 3.697763e-04
##  [996] 2.903952e-03 1.765316e-04 9.645712e-05 4.810557e-05 7.305414e-05
## [1001] 4.078268e-05 2.984722e-05 1.303547e-05 7.496974e-05 7.482619e-05
## [1006] 1.317442e-04 2.446152e-03 1.781531e-04 6.153143e-05 1.212652e-05
## [1011] 4.622181e-05 4.576217e-05 1.210220e-04 5.047920e-05 8.973245e-05
## [1016] 2.412819e-03 2.003149e-04 1.509098e-04 1.714881e-05 5.161297e-05
## [1021] 6.683067e-06 7.003609e-06 9.027970e-05 6.290332e-05 5.956709e-05
## [1026] 2.842818e-05 2.819648e-05 1.843349e-03 1.697810e-04 9.756148e-06
## [1031] 5.327245e-05 6.479627e-05 1.266860e-05 5.937826e-06 1.651361e-03
## [1036] 1.393562e-04 8.010966e-06 1.090999e-05 1.549728e-03 2.035800e-05
## [1041] 1.186336e-03 1.227101e-04 7.195084e-06 2.352625e-05 2.396208e-03
## [1046] 1.589092e-04 1.085377e-05 3.655935e-05 7.640500e-05 9.918780e-05
## [1051] 2.278297e-03 1.458201e-04 4.917341e-05 4.141871e-05 9.003554e-05
## [1056] 2.463299e-03 1.432107e-05 4.085677e-05 2.625932e-05 3.910565e-05
## [1061] 4.645122e-05 2.548027e-03 1.671712e-04 8.582007e-05 1.002365e-04
## [1066] 4.057158e-05 1.349811e-03 2.511404e-04 1.994889e-03 2.313057e-04
## [1071] 6.544174e-05 2.020863e-04 6.611540e-05 2.008454e-03 5.423898e-05
## [1076] 1.215703e-04 1.182093e-05 4.979657e-05 1.967341e-04 1.067731e-03
## [1081] 3.951691e-05 2.761066e-05 2.243285e-03 2.407840e-04 4.438424e-05
## [1086] 2.715997e-03 2.255309e-04 8.433511e-05 4.339629e-05 1.998949e-05
## [1091] 4.478927e-05 6.849974e-05 9.589250e-05 2.486325e-03 5.773094e-04
## [1096] 2.282064e-04 1.511472e-04 7.887505e-05 1.213056e-03 2.201358e-05
## [1101] 2.635204e-03 1.152294e-05 8.257887e-05 5.323001e-05 1.874994e-04
## [1106] 8.916985e-05 1.525562e-04 1.148265e-04 2.479626e-04 2.405872e-03
## [1111] 1.551144e-04 2.568164e-04 1.951555e-05 1.739965e-05 4.090146e-05
## [1116] 3.634553e-05 1.134109e-04 1.875309e-04 4.846484e-05 2.182257e-05
## [1121] 1.877458e-03 1.845782e-04 2.450257e-05 3.170864e-05 1.057321e-04
## [1126] 2.489546e-03 4.133216e-05 5.344652e-05 2.240777e-04 2.352381e-03
## [1131] 2.839875e-03 8.438667e-04 2.132527e-04 4.323777e-05 1.693448e-05
## [1136] 2.849909e-05 9.362362e-05 2.097933e-03 1.953379e-05 7.356449e-05
## [1141] 1.701686e-03 2.339330e-04 2.249299e-03 2.270868e-03 1.545511e-04
## [1146] 3.868765e-04 2.540868e-05 2.879937e-03 2.394985e-03 4.281324e-05
## [1151] 1.901774e-05 2.183214e-03 1.638186e-05 1.853454e-03 7.034619e-05
## [1156] 6.146953e-06 5.196709e-05 2.279701e-03 8.164291e-05 2.176432e-03
## [1161] 1.191465e-05 1.868184e-03 1.994477e-03 3.630741e-04 3.497114e-04
## [1166] 2.417230e-03 1.588680e-04 2.516851e-03 7.300834e-05 1.609913e-03
## [1171] 9.660727e-05 2.273579e-03 1.920482e-04 3.435506e-05 1.280076e-05
## [1176] 3.149408e-05 1.943206e-03 2.882525e-04 3.738626e-05 5.771192e-05
## [1181] 2.137579e-03 1.024251e-04 1.959740e-04 4.124521e-05 2.276040e-03
## [1186] 2.626023e-03 3.365550e-04 2.932179e-04 2.127394e-03 8.680593e-05
## [1191] 3.716784e-05 1.790187e-03 4.981027e-05 2.252218e-03 1.059354e-04
## [1196] 2.715969e-04 2.116064e-03 2.194569e-03 4.344735e-05 1.996377e-03
## [1201] 1.305559e-05 1.821536e-05 2.505977e-03 2.989805e-05 2.497890e-05
## [1206] 2.499468e-03 6.639824e-05 2.186783e-05 2.278396e-03 2.092238e-03
## [1211] 1.774184e-03 9.616172e-06 2.116881e-03 2.180603e-05 7.162651e-05
## [1216] 2.307276e-03 2.256789e-04 2.779221e-03 2.788794e-04 1.698958e-05
## [1221] 3.303013e-05 3.009097e-03 5.097578e-04 1.578156e-05 3.086716e-03
## [1226] 1.057099e-04 7.040233e-05 2.353230e-05 2.219300e-05 2.109212e-03
## [1231] 1.339151e-04 1.916180e-03 8.702333e-05 1.269549e-03 3.806256e-05
## [1236] 1.711646e-04 1.316459e-04 1.498833e-03 2.728755e-05 8.405858e-06
## [1241] 5.821876e-05 1.757683e-05 1.285680e-05 1.183352e-03 1.633361e-04
## [1246] 5.029305e-04 6.795264e-04 1.492927e-03 1.010914e-04 1.205648e-04
## [1251] 7.376224e-04 4.890765e-04 1.704170e-05 1.609839e-03 1.345433e-04
## [1256] 4.407163e-04 4.039347e-05 1.025183e-03 1.248968e-03 8.054964e-05
## [1261] 5.098362e-05 1.266167e-03 1.292961e-04 2.126018e-03 8.406862e-06
## [1266] 4.038218e-06 2.537086e-05 4.113479e-05 2.361047e-05 1.582958e-05
## [1271] 1.084911e-03 9.151214e-05 3.621462e-05 2.691710e-04 1.007392e-03
## [1276] 2.319664e-04 5.957638e-05 3.984632e-04 1.064833e-03 2.236835e-05
## [1281] 1.383124e-03 1.951105e-04 7.920980e-05 4.367922e-04 8.092667e-06
## [1286] 1.371179e-03 4.142377e-05 6.788929e-06 4.145218e-06 1.790472e-03
## [1291] 3.505771e-05 6.989842e-06 2.145173e-05 2.850320e-05 1.628060e-03
## [1296] 1.610316e-04 1.135232e-03 1.139286e-05 3.524406e-06 6.791235e-04
## [1301] 2.366787e-05 1.723805e-05 1.055655e-03 8.162285e-05 9.370461e-05
## [1306] 4.499088e-06 1.683181e-03 2.965857e-04 1.575493e-04 1.310365e-05
## [1311] 5.278445e-04 1.841765e-05 1.551377e-04 2.209086e-03 3.149512e-04
## [1316] 1.115044e-05 1.276899e-04 4.707758e-05 1.967434e-03 9.259460e-05
## [1321] 3.865769e-05 1.985296e-04 1.578428e-03 6.751688e-05 1.271820e-04
## [1326] 2.610475e-05 3.379682e-04 1.492127e-03 5.504084e-04 6.669486e-05
## [1331] 8.172393e-04 6.041489e-06 1.021390e-05 5.786921e-04 5.012540e-04
## [1336] 4.330819e-04 5.236024e-04 1.068017e-05 2.413369e-04 1.054030e-03
## [1341] 4.076444e-04 6.675652e-05 5.296051e-04 1.375243e-03 1.648407e-04
## [1346] 6.937050e-05 3.762388e-04 8.988974e-04 4.986447e-05 5.484935e-05
## [1351] 1.322486e-03 4.429211e-05 3.916010e-05 5.459251e-05 5.396913e-04
## [1356] 1.117804e-03 1.730078e-03 1.292592e-04 4.833520e-04 2.080849e-05
## [1361] 1.236276e-03 1.770569e-04 1.618208e-04 9.342200e-06 1.921994e-03
## [1366] 7.850357e-04 5.770702e-05 1.235271e-03 6.203032e-04 3.934176e-04
## [1371] 1.525723e-03 2.592864e-04 2.227234e-04 2.227031e-05 1.333363e-05
## [1376] 1.859385e-05 7.379826e-04 1.909960e-05 7.089940e-06 2.306214e-03
## [1381] 4.089559e-04 2.028287e-04 1.981660e-03 1.291142e-04 2.505495e-05
## [1386] 2.032025e-03 1.668849e-03 9.448267e-05 5.156967e-06 3.999048e-04
## [1391] 1.739013e-03 1.707759e-04 5.685385e-05 1.879697e-03 2.220661e-04
## [1396] 3.845024e-05 1.914336e-03 5.940508e-05 4.963877e-05 6.528225e-05
## [1401] 2.122719e-05 6.350213e-04 2.656623e-05 2.137202e-03 2.975667e-04
## [1406] 2.441066e-03 7.288653e-05 4.625242e-05 1.157557e-04 2.848517e-05
## [1411] 2.377185e-05 1.815907e-05 1.027328e-03 3.322119e-05 6.190776e-06
## [1416] 1.057954e-03 1.537283e-03 1.498044e-05 1.043946e-04 1.284341e-03
## [1421] 2.569952e-05 1.330747e-03 1.647654e-04 3.073732e-05 6.965602e-04
## [1426] 5.328042e-05 1.482740e-04 3.515425e-05 1.697033e-03 9.302128e-05
## [1431] 2.017391e-03 9.028937e-05 1.334305e-04 1.864842e-04 5.378378e-04
## [1436] 1.376630e-05 1.654463e-04 1.538874e-03 6.479259e-05 4.489858e-05
## [1441] 1.031501e-03 2.608718e-06 7.387779e-04 2.063571e-05 2.286183e-05
## [1446] 3.460107e-06 1.047926e-03 2.635603e-05 9.479544e-04 1.020899e-05
## [1451] 1.673549e-03 2.856440e-04 2.517060e-04 4.263857e-04 1.811736e-03
## [1456] 4.240757e-05 1.186375e-03 5.120633e-04 4.850946e-06 3.793760e-06
## [1461] 9.282698e-05 2.903972e-05 1.090691e-03 7.047207e-06 5.374105e-03
## [1466] 3.363914e-04 3.421465e-04 1.144982e-04 3.682696e-04 2.734160e-04
## [1471] 2.038121e-04 6.398629e-05 7.087587e-05 3.880083e-05 6.102139e-05
## [1476] 1.094171e-04 1.654072e-04 3.814249e-05 1.644949e-04 5.354377e-03
## [1481] 4.869616e-04 1.410368e-04 2.827898e-04 5.567218e-04 3.435786e-04
## [1486] 7.848240e-04 6.997666e-05 5.386538e-05 1.594698e-04 1.591242e-04
## [1491] 2.680750e-04 1.782093e-04 2.273971e-04 4.138051e-05 1.090678e-04
## [1496] 1.708172e-04 6.181523e-05 5.544728e-05 2.796277e-05 6.130597e-03
## [1501] 1.501257e-04 3.754872e-04 3.362068e-04 5.697306e-04 4.548284e-04
## [1506] 4.210651e-04 7.568034e-04 3.293873e-04 2.918696e-04 1.122617e-04
## [1511] 2.646550e-05 1.777958e-04 1.694092e-04 7.693272e-05 2.497788e-04
## [1516] 2.513375e-04 2.462781e-04 1.509606e-04 6.128644e-05 3.687210e-05
## [1521] 5.667121e-03 1.210605e-03 9.073788e-04 6.675290e-04 4.874371e-04
## [1526] 3.715562e-04 1.860005e-04 1.383161e-04 2.851477e-04 1.301199e-04
## [1531] 5.161334e-05 1.698496e-05 7.615575e-05 5.472392e-05 2.275116e-05
## [1536] 2.165542e-04 3.719520e-05 1.112291e-05 1.787147e-05 1.824571e-04
## [1541] 2.692561e-04 3.276138e-04 5.498833e-05 1.533650e-04 3.888574e-03
## [1546] 2.725916e-05 1.067495e-04 2.150567e-04 4.314064e-05 8.122606e-05
## [1551] 9.792870e-05 1.785239e-05 3.415569e-03 4.716276e-04 2.168171e-04
## [1556] 3.374676e-04 2.082192e-04 2.699889e-05 2.776496e-05 1.325486e-04
## [1561] 4.899855e-03 7.155309e-04 7.146793e-04 5.192687e-04 4.226267e-04
## [1566] 3.514556e-04 4.225893e-04 1.371257e-04 1.705337e-04 7.110350e-05
## [1571] 2.285512e-04 1.105263e-04 1.421654e-04 1.138663e-04 2.606256e-05
## [1576] 1.704948e-05 2.301905e-05 4.015078e-05 8.629137e-05 5.888884e-03
## [1581] 9.403946e-04 5.839579e-04 6.247373e-04 3.396170e-04 8.052564e-04
## [1586] 4.490276e-04 4.647428e-04 1.857707e-04 3.905345e-04 2.605812e-04
## [1591] 6.914291e-05 7.442529e-05 1.705222e-04 6.462060e-05 8.571940e-05
## [1596] 8.004127e-05 2.407696e-05 5.407514e-05 5.058338e-03 8.890189e-05
## [1601] 1.191195e-04 1.417951e-04 2.788402e-04 2.148618e-04 4.731675e-04
## [1606] 2.537539e-05 5.055458e-05 6.200959e-05 1.208067e-04 6.104336e-05
## [1611] 1.041762e-04 9.384512e-05 5.033851e-03 8.693820e-04 8.245572e-04
## [1616] 4.421841e-05 1.434585e-04 2.411838e-04 9.639178e-05 2.783299e-04
## [1621] 2.262473e-04 2.304005e-04 1.442084e-04 2.972941e-05 4.617828e-05
## [1626] 9.760248e-05 1.507091e-04 4.796511e-03 4.925338e-04 7.358840e-04
## [1631] 1.450664e-04 5.671800e-05 4.079360e-04 3.724647e-04 3.031930e-04
## [1636] 1.555226e-04 3.581788e-05 2.143545e-05 6.385763e-06 9.755391e-05
## [1641] 1.521916e-04 5.976409e-05 4.814957e-03 5.825475e-04 9.806398e-04
## [1646] 1.484643e-04 1.767670e-04 1.343963e-05 3.105675e-04 1.359631e-04
## [1651] 8.088601e-04 2.618758e-05 1.096484e-04 1.158379e-04 1.698486e-04
## [1656] 2.187490e-04 5.000199e-03 9.117454e-04 9.822438e-05 8.931167e-05
## [1661] 7.818274e-05 4.053411e-04 3.516327e-05 2.566245e-05 2.863746e-05
## [1666] 2.679621e-05 2.835400e-05 5.776924e-05 5.825200e-05 3.882709e-05
## [1671] 1.766841e-04 8.698014e-05 1.466188e-04 9.701359e-05 5.774395e-03
## [1676] 1.986317e-04 4.448109e-04 3.128724e-05 5.237045e-05 1.015552e-04
## [1681] 1.477129e-04 3.750238e-05 7.168201e-05 6.885846e-05 1.402544e-04
## [1686] 6.372745e-05 1.566854e-04 6.335412e-05 5.213777e-03 3.150879e-04
## [1691] 1.227877e-04 7.245383e-04 5.938196e-04 4.791478e-04 6.900746e-04
## [1696] 5.180429e-05 1.373597e-04 1.224303e-04 1.179003e-04 1.572374e-04
## [1701] 1.615988e-04 2.369169e-04 8.726928e-05 1.340817e-04 1.286905e-04
## [1706] 8.725439e-05 6.615767e-03 5.728593e-04 4.057100e-04 2.148378e-04
## [1711] 5.631791e-04 6.939270e-05 1.779713e-04 8.604336e-04 6.338659e-04
## [1716] 4.425847e-04 2.188672e-04 9.176120e-05 3.719868e-04 2.138099e-05
## [1721] 2.396982e-05 9.837399e-05 9.340468e-05 2.015985e-05 2.169006e-04
## [1726] 1.549874e-04 1.526246e-04 1.483988e-04 5.660609e-03 2.464575e-04
## [1731] 3.370843e-05 6.675444e-04 5.300827e-04 1.091918e-04 8.578367e-04
## [1736] 5.466563e-04 3.921803e-04 5.520414e-04 1.308754e-04 8.625492e-05
## [1741] 7.873764e-05 2.195171e-04 1.047481e-04 1.858434e-04 3.236750e-04
## [1746] 1.475915e-04 3.204177e-04 1.611137e-04 3.034579e-05 1.563101e-05
## [1751] 4.294263e-04 7.725631e-03 2.865437e-04 2.792300e-04 4.810577e-05
## [1756] 2.982473e-04 2.419919e-04 2.776849e-04 2.224913e-04 2.661636e-04
## [1761] 4.379601e-04 1.370740e-04 3.037751e-05 6.358438e-05 1.656377e-04
## [1766] 5.600636e-05 7.881052e-05 1.356854e-04 1.275667e-04 2.250679e-04
## [1771] 5.721539e-04 4.995695e-03 6.177132e-04 6.153148e-05 2.377526e-04
## [1776] 2.042429e-04 2.861854e-04 3.477167e-04 1.362172e-04 2.171850e-04
## [1781] 2.235638e-05 8.828746e-05 1.932153e-05 8.975626e-05 1.279615e-04
## [1786] 1.634217e-04 1.016720e-04 1.713859e-04 2.143376e-04 5.248221e-03
## [1791] 8.897643e-04 3.867572e-04 1.714881e-05 5.161302e-05 6.683067e-06
## [1796] 2.745743e-05 1.842238e-04 1.863477e-04 2.312558e-04 1.821156e-04
## [1801] 2.186532e-04 8.644541e-05 1.235878e-04 1.056686e-04 1.666264e-04
## [1806] 1.282677e-04 6.077762e-05 1.108564e-04 8.229570e-05 2.023934e-05
## [1811] 3.853346e-03 6.042640e-04 9.756148e-06 2.981632e-04 2.164683e-04
## [1816] 1.266860e-05 2.969109e-04 3.982557e-05 5.617221e-05 8.237627e-05
## [1821] 8.798230e-05 1.107513e-04 7.793911e-05 4.548779e-05 1.262111e-04
## [1826] 7.378106e-04 3.320653e-03 4.954090e-04 1.248119e-04 2.831484e-04
## [1831] 6.864325e-05 1.418059e-04 6.290547e-05 1.154645e-04 1.827178e-04
## [1836] 6.599580e-05 1.291115e-04 2.088670e-04 2.860945e-03 1.140223e-04
## [1841] 6.051245e-05 8.510610e-05 5.942413e-05 2.030220e-05 2.633607e-03
## [1846] 2.128818e-04 1.350081e-04 2.822750e-04 5.285424e-05 6.579146e-05
## [1851] 4.340980e-05 7.828192e-05 5.052547e-03 4.006440e-04 2.085071e-04
## [1856] 3.614818e-04 8.832904e-04 2.155166e-05 2.964693e-05 8.698351e-05
## [1861] 1.503546e-04 8.579980e-05 3.663459e-05 1.900961e-04 5.091437e-05
## [1866] 2.326219e-04 1.174979e-04 4.440515e-03 5.302196e-04 3.197706e-04
## [1871] 2.643279e-04 1.960104e-04 4.626959e-05 5.863652e-05 9.435259e-05
## [1876] 2.385970e-04 1.771638e-04 1.539598e-05 9.679465e-05 2.707033e-04
## [1881] 4.338643e-03 1.745131e-04 3.793079e-04 2.625932e-05 2.924354e-04
## [1886] 2.290466e-04 2.281331e-05 3.496010e-05 1.269480e-05 7.165378e-05
## [1891] 9.113054e-05 1.154088e-04 6.121898e-05 1.385418e-04 9.276179e-05
## [1896] 1.409913e-04 1.257688e-04 4.207734e-03 1.671712e-04 3.634163e-04
## [1901] 2.012754e-04 3.345516e-04 1.645621e-05 3.782358e-05 6.175241e-05
## [1906] 2.506285e-05 1.394243e-04 8.930422e-05 1.644377e-04 8.768572e-05
## [1911] 2.835847e-03 1.019460e-03 1.515327e-04 1.309003e-04 3.635960e-05
## [1916] 1.151027e-04 4.499386e-05 3.062318e-03 3.296625e-04 3.430511e-04
## [1921] 6.055083e-04 4.713577e-04 1.512587e-05 2.524629e-05 3.449834e-05
## [1926] 1.445790e-04 4.559204e-05 1.111380e-04 4.065925e-03 6.242442e-05
## [1931] 3.742798e-04 1.182093e-05 2.071120e-04 4.258396e-04 2.606853e-05
## [1936] 5.108077e-05 4.916232e-05 1.122362e-05 5.110282e-05 2.316086e-04
## [1941] 2.183183e-05 2.037503e-04 2.122951e-03 9.670602e-05 2.761066e-05
## [1946] 6.206125e-05 5.256823e-05 5.961945e-05 3.727801e-03 5.625942e-04
## [1951] 1.925382e-04 1.410530e-05 4.213015e-05 1.051658e-04 9.567616e-05
## [1956] 1.851757e-05 1.237655e-04 9.993072e-05 3.489158e-05 4.206962e-03
## [1961] 5.671164e-04 8.433511e-05 1.285613e-04 1.720463e-04 1.583638e-04
## [1966] 2.265315e-04 5.314713e-04 2.490051e-05 3.937414e-05 8.609174e-05
## [1971] 8.261117e-05 9.250451e-05 1.402204e-04 2.368713e-04 4.151630e-03
## [1976] 1.061846e-03 4.496616e-04 5.957504e-04 4.956117e-04 3.020346e-05
## [1981] 5.313210e-05 4.358176e-05 1.353623e-04 8.150125e-05 1.930825e-04
## [1986] 3.044589e-05 3.102212e-05 2.285236e-03 2.023232e-04 3.614926e-05
## [1991] 8.456244e-05 6.306964e-05 1.250918e-04 6.057472e-03 2.157625e-04
## [1996] 3.465302e-04 2.433504e-04 8.913819e-04 4.452497e-04 5.714075e-04
## [2001] 3.679412e-04 1.540494e-03 2.752852e-05 3.782917e-05 7.671907e-05
## [2006] 9.670872e-05 3.403397e-05 1.350997e-04 2.985512e-04 1.646036e-04
## [2011] 9.786963e-05 1.082428e-05 4.882895e-03 2.280189e-04 5.059234e-04
## [2016] 2.572049e-04 6.956317e-05 1.129898e-04 1.983879e-04 2.314652e-04
## [2021] 4.198377e-04 3.422724e-04 2.575348e-04 4.552484e-05 3.839976e-05
## [2026] 4.418697e-05 3.391737e-03 4.761963e-04 7.803430e-05 1.665367e-04
## [2031] 4.466580e-04 1.752696e-05 3.578413e-05 4.046764e-05 2.458583e-05
## [2036] 1.245407e-04 1.072822e-04 1.355201e-04 9.270175e-05 1.031394e-04
## [2041] 3.838044e-03 1.244350e-04 1.801645e-04 4.128552e-04 1.057800e-05
## [2046] 1.692147e-05 9.940348e-05 6.748387e-05 3.780698e-03 1.571184e-05
## [2051] 3.204441e-05 6.890004e-05 2.207494e-05 1.993862e-03 4.006499e-03
## [2056] 1.527007e-03 4.061271e-04 1.636730e-04 1.192685e-04 9.977923e-05
## [2061] 2.979691e-04 1.139221e-04 6.425351e-05 6.048085e-05 2.286016e-05
## [2066] 3.641713e-03 1.334179e-04 3.365964e-04 2.786225e-05 7.107115e-05
## [2071] 2.750987e-05 2.756392e-05 2.979834e-03 6.377770e-04 3.999345e-05
## [2076] 1.645385e-05 2.169357e-05 4.207578e-03 7.973666e-05 4.611270e-05
## [2081] 3.404085e-05 6.947267e-03 2.613537e-04 4.666309e-04 1.366999e-04
## [2086] 2.854937e-05 6.475017e-05 1.187618e-04 1.228294e-04 6.618629e-05
## [2091] 8.565481e-04 4.287903e-03 1.810400e-05 1.785018e-05 4.454900e-05
## [2096] 9.614923e-05 9.089492e-06 2.701304e-05 2.410085e-05 5.232555e-05
## [2101] 2.089185e-04 3.390612e-04 1.365216e-04 3.781088e-03 1.291830e-04
## [2106] 1.007572e-04 2.094745e-05 2.415726e-05 9.672737e-05 3.768156e-05
## [2111] 6.233950e-05 2.321566e-04 2.278577e-05 6.441765e-05 1.948838e-05
## [2116] 6.421847e-05 3.480077e-03 1.256836e-04 1.859551e-05 3.616865e-05
## [2121] 4.305637e-05 2.382105e-05 8.569530e-05 5.928596e-05 3.762837e-05
## [2126] 3.578890e-05 3.075024e-03 2.003439e-04 4.843460e-05 1.939875e-04
## [2131] 2.040753e-05 7.661406e-05 1.043104e-04 2.483345e-04 3.647394e-03
## [2136] 3.167424e-04 1.912972e-05 4.299316e-05 6.867378e-05 1.196339e-04
## [2141] 9.089740e-05 7.457599e-05 2.611259e-05 2.526455e-05 1.044900e-04
## [2146] 6.414901e-05 6.392846e-05 7.448973e-05 4.346888e-04 3.428123e-03
## [2151] 4.333828e-05 1.657895e-05 4.099539e-05 8.810958e-05 2.310488e-05
## [2156] 1.095969e-04 1.090157e-03 3.026809e-03 1.902536e-05 4.848130e-05
## [2161] 3.514004e-05 1.002590e-04 4.013745e-05 3.047915e-03 6.887536e-04
## [2166] 1.195339e-03 4.196904e-05 2.344831e-05 4.233358e-05 1.356308e-05
## [2171] 4.510697e-03 6.716025e-04 5.365462e-05 9.485907e-05 1.932666e-04
## [2176] 3.221595e-05 3.317286e-03 7.300835e-05 8.221385e-05 1.525866e-05
## [2181] 2.442033e-03 3.699994e-04 2.014669e-05 3.545562e-05 3.293384e-03
## [2186] 5.034738e-04 2.274447e-04 1.501841e-04 2.491966e-05 3.833168e-05
## [2191] 1.298912e-04 7.854818e-05 5.500505e-05 1.829290e-05 3.918541e-03
## [2196] 6.295591e-04 2.370141e-04 3.123414e-04 1.719346e-05 5.503219e-05
## [2201] 5.147363e-05 3.936921e-05 6.152862e-05 5.354052e-05 5.690187e-05
## [2206] 4.316965e-05 6.282185e-05 3.717347e-03 2.406373e-04 3.409767e-04
## [2211] 2.911619e-04 3.942902e-05 7.007068e-05 3.476802e-05 3.369188e-05
## [2216] 3.089115e-03 3.043693e-05 3.041966e-05 3.076619e-05 3.506516e-03
## [2221] 1.083749e-03 5.019413e-04 2.678812e-05 6.053801e-05 8.123706e-05
## [2226] 2.885153e-03 9.409839e-05 2.580679e-04 9.358810e-05 3.197108e-05
## [2231] 4.597454e-05 3.927351e-05 3.144492e-03 2.628866e-04 3.840580e-05
## [2236] 2.180638e-05 7.667793e-05 4.965381e-05 3.537052e-05 1.865332e-05
## [2241] 3.312341e-03 1.059354e-04 6.300311e-04 1.934498e-05 3.318761e-06
## [2246] 2.572925e-05 1.867509e-05 6.110733e-05 1.891552e-04 2.989598e-03
## [2251] 6.158115e-05 6.619139e-05 3.320681e-03 2.469550e-04 3.971423e-05
## [2256] 2.032176e-05 2.247439e-05 3.947335e-05 1.046372e-04 3.141549e-05
## [2261] 2.747781e-03 4.678883e-05 1.115767e-04 1.706127e-05 2.085743e-05
## [2266] 1.291751e-04 1.922563e-05 4.592330e-03 1.528150e-04 6.412433e-04
## [2271] 5.561185e-05 2.847369e-05 1.079958e-04 4.018130e-03 2.672370e-04
## [2276] 2.145212e-04 1.757055e-05 2.359468e-05 6.773676e-05 5.747333e-05
## [2281] 1.495078e-05 3.835698e-05 1.919961e-05 4.005311e-03 4.605216e-05
## [2286] 2.608478e-05 2.407317e-05 6.296822e-05 5.596660e-05 2.252089e-05
## [2291] 3.426425e-03 2.928146e-03 8.952474e-05 2.742604e-05 8.107404e-05
## [2296] 5.436211e-05 2.193500e-05 7.465692e-05 3.406288e-03 1.787866e-04
## [2301] 3.208577e-04 2.205594e-05 2.442832e-05 3.498606e-05 3.451525e-05
## [2306] 9.460188e-05 7.904371e-05 3.383783e-05 1.188088e-05 1.005459e-04
## [2311] 5.954902e-05 3.502254e-03 4.253502e-04 3.624123e-05 3.818902e-03
## [2316] 8.414357e-04 5.761793e-05 4.991845e-05 1.097041e-04 1.971050e-05
## [2321] 6.556708e-05 7.983873e-05 9.970430e-05 4.466898e-03 8.565690e-04
## [2326] 4.458103e-05 2.715321e-05 3.455166e-05 6.345439e-05 4.466948e-03
## [2331] 4.339257e-04 3.308258e-04 6.428992e-05 1.399857e-04 6.756916e-05
## [2336] 4.497714e-05 1.215030e-04 1.414162e-04 7.454196e-05 5.658613e-05
## [2341] 3.021550e-03 1.339151e-04 1.033555e-03 3.914908e-03 1.988868e-04
## [2346] 4.550639e-05 3.868487e-05 9.330077e-05 1.311212e-04 1.880973e-04
## [2351] 8.335886e-05 6.059933e-05 1.462690e-04

Main_root <- subset(all_data, all_data$root_order == 0)
Main_root

lets navigate columns

colnames(Main_root)

##  [1] "image"                 "root_name"             "root"                 
##  [4] "length"                "vector_length"         "surface"              
##  [7] "volume"                "direction"             "diameter"             
## [10] "root_order"            "root_ontology"         "parent_name"          
## [13] "parent"                "insertion_position"    "insertion_angle"      
## [16] "n_child"               "child_density"         "first_child"          
## [19] "insertion_first_child" "last_child"            "insertion_last_child" 
## [22] "day"

Main_root[,c(3:5,10)]

colnames(Main_root)

##  [1] "image"                 "root_name"             "root"                 
##  [4] "length"                "vector_length"         "surface"              
##  [7] "volume"                "direction"             "diameter"             
## [10] "root_order"            "root_ontology"         "parent_name"          
## [13] "parent"                "insertion_position"    "insertion_angle"      
## [16] "n_child"               "child_density"         "first_child"          
## [19] "insertion_first_child" "last_child"            "insertion_last_child" 
## [22] "day"

MR_data_2 <- Main_root[,c(1:3,4,16,17,19:22)]
MR_data_2

Lateral_root <- subset(all_data, all_data$root_order == 1)
Lateral_root

unique(Lateral_root$root_order)

## [1] 1

MR_data_2$root[1]

## [1] " 0ac830e5-e4ab-4e0b-8578-3bcdf2c1c082"

temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_2$root[1])
temporary

MR_data_3 <- subset(MR_data_2, MR_data_2$n_child >0)
temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_3$root[1])
temporary <- subset(temporary, temporary$day == MR_data_3$day[1])
temporary

dim(temporary)

## [1]  2 22

dim(temporary)[1]

## [1] 2

dim(temporary)[2]

## [1] 22

total_LRL <- sum(temporary$length)
LR_number <- dim(temporary)[1]
total_LRL

## [1] 0.09437875

LR_number

## [1] 2

add this info to MR file

MR_data_3$LRL <- 0
MR_data_3$LRno <- 0
MR_data_3

MR_data_3$LRL[1] <- total_LRL
MR_data_3$LRno[1] <- LR_number
MR_data_3

MR_data_noChild <- subset(MR_data_2, MR_data_2$n_child == 0)
MR_data_noChild

lets loop

dim(MR_data_3)

## [1] 379  12

for(i in 1:379){
  temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_3$root[i])
  temporary <- subset(temporary, temporary$day == MR_data_3$day[i])
  total_LRL <- sum(temporary$length)
  LR_number <- dim(temporary)[1]
  MR_data_3$LRL[i] <- total_LRL
  MR_data_3$LRno[i] <- LR_number
}

let have a look at data now

MR_data_3$check <- MR_data_3$n_child - MR_data_3$LRno
MR_data_3

unique(MR_data_3$check)

## [1] 0

#MR_all <- rbind(MR_data_3,MR_data_noChild)
head(MR_data_3)

#let's remove check collumn from Child:

MR_data_Child2 <- MR_data_3[,1:12]
MR_data_Child2

MR_data_noChild

MR_data_noChild$LRL <- 0
MR_data_noChild$LRno <- 0


MR_all <- rbind(MR_data_Child2, MR_data_noChild)
unique(MR_all$day)

## [1]  9 11 13  5  7

Now we are extracting information on genotype, replicate and condition from the root_name column

?strsplit()

## starting httpd help server ... done

MR_all$root_name[1]

## [1] " pl1_125_duf2"

text <- strsplit(x = MR_all$root_name[1], split = "_")
text

## [[1]]
## [1] " pl1" "125"  "duf2"

plate <- text[[1]][1]
plate

## [1] " pl1"

cond <- text[[1]][2]
cond

## [1] "125"

genotype <- text[[1]][3]
genotype

## [1] "duf2"

OK - now let’s loop it:

dim(MR_all)

## [1] 801  12

for(i in 1:801){
  text <- strsplit(x = MR_all$root_name[i], split = "_")
  plate <- text[[1]][1]
  cond <- text[[1]][2]
  genotype <- text[[1]][3]
   
  MR_all$genotype[i] <- genotype
  MR_all$condition[i] <- cond
  MR_all$plate[i] <- plate
}

MR_all

unique(MR_all$genotype)

## [1] "duf2" "duf1" "c2"   "c1"   NA

MR_all.nona <- na.omit(MR_all)
unique(MR_all.nona$genotype)

## [1] "duf2" "duf1" "c2"   "c1"

#get genotype information as it should be:
MR_all.nona$genotype <- gsub("c1", "Col-0", MR_all.nona$genotype)
MR_all.nona$genotype <- gsub("c2", "Col-0", MR_all.nona$genotype)
MR_all.nona$genotype <- gsub("duf1", "duf-5", MR_all.nona$genotype)
MR_all.nona$genotype <- gsub("duf2", "duf-5", MR_all.nona$genotype)
unique(MR_all.nona$genotype)

## [1] "duf-5" "Col-0"

unique(MR_all.nona$condition)

## [1] "125" "c"   "75"

MR_all.nona$condition<- gsub("c","0", MR_all.nona$condition)
dim(MR_all.nona)

## [1] 800  15

unique(MR_all$day)

## [1]  9 11 13  5  7

######I decided to change character from here….to numeric for the condition

MR_all$condition <- as.numeric(MR_all$condition)

## Warning: NAs introduced by coercion

MR_all

MR_all.nona$TRS <- MR_all.nona$length + MR_all.nona$LRL
MR_all.nona$aLRL <- MR_all.nona$LRL/ MR_all.nona$LRno
MR_all.nona$MRpLRL <- MR_all.nona$length / MR_all.nona$LRL
MR_all.nona

MR_all.nona$condition <- as.numeric(MR_all.nona$condition)
MR_all.nona

I have 3 conditions, control, 75 mM and 125 mM salt…..I think I know what is the problem…..the condition is character but then for salt condition, I have 75 or 125……hmmmm…..not sure what to do with a mixed numeric and character values……..maybe using data.frame

#install.packages("ggplot")
#install.packages("ggpubr")
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.5

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.0.5

MR_all <- MR_all.nona
unique(MR_all.nona$condition)

## [1] 125   0  75

MR_all.nona$condition <- as.factor(MR_all.nona$condition)


histogram_TRS <- ggdensity(MR_all.nona, x = "TRS",
                           add = "mean", rug = TRUE,facet.by = "day",
                           color = "condition", fill = "condition",
                           palette = c("#00AFBB", "#E7B800", "#FF0000"))
histogram_TRS

histogram_LRL <- ggdensity(MR_all.nona, x = "LRL",
                           add = "mean", rug = TRUE,facet.by = "day",
                           color = "condition", fill = "condition",
                           palette = c("#00AFBB", "#E7B800", "#FF0000"))
histogram_LRL

pdf("histogram.TRS.pdf")
plot(histogram_TRS)
# if plotting multiple graphs - this command is extremely important 
dev.off()

## png 
##   2

library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggpubr':
## 
##     get_legend

pdf("Figure_MAIN_1.pdf", height = 15, width = 12)
plot_grid(histogram_TRS, histogram_LRL, ncol=2,
          align = "hv", labels=c("AUTO"), 
          label_size = 24)
dev.off()

## png 
##   2

unique(MR_all$genotype)

## [1] "duf-5" "Col-0"

TRS_lgraph <- ggplot(data=MR_all.nona, aes(x= genotype, y=TRS, color = condition)) 
TRS_lgraph <- TRS_lgraph + geom_boxplot()
TRS_lgraph <- TRS_lgraph + facet_grid(~ condition) + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange"))
TRS_lgraph <- TRS_lgraph + ylab("Total root size (cm)") + xlab("") + theme(legend.position='none')
TRS_lgraph

library(ggsci)
library(ggbeeswarm)
library(gapminder)
library(RColorBrewer)
library(ggridges)
better_TRS_graph <- ggplot(data=MR_all.nona, aes(x= genotype, y=TRS, color = condition))
better_TRS_graph <- better_TRS_graph + geom_beeswarm(alpha=0.6, priority = "density")
better_TRS_graph <- better_TRS_graph + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")

## Warning: `fun.y` is deprecated. Use `fun` instead.

better_TRS_graph <- better_TRS_graph + facet_grid(day~ condition) + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange"))
better_TRS_graph <- better_TRS_graph + ylab("Total root size (cm)") + xlab("Genotype") + theme(legend.position='none')
better_TRS_graph <- better_TRS_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
better_TRS_graph <- better_TRS_graph + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0") 
better_TRS_graph

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 5, 2

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 6, 3

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 5, 2

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 6, 3

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 5, 2

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 6, 3

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 5, 2

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 6, 3

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 5, 2

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 6, 3

head(MR_all)

unique(MR_all$condition)

## [1] 125   0  75

temp <- subset(MR_all, MR_all$condition == "duf-5")
temp

#install.packages("plotly")
library(ggplot2)
library(plotly)

## Warning: package 'plotly' was built under R version 4.0.5

## 
## 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':
## 
##     layout

head(MR_all)

my_graph <- ggplot(data=MR_all.nona, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("Main Root Length") + theme(legend.position='none')
my_graph

ggplotly(my_graph)

####now I want to inspect each particular genotype to see whether all replicates behaving well….basically curating the data!I start with col first.

geno_c <- subset(MR_all.nona, MR_all.nona$genotype == "Col-0")

my_graph <- ggplot(data=geno_c, aes(x= day, y=length, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("Main Root Length (cm)") + theme(legend.position='none')
my_graph

ggplotly(my_graph)

now calculate TRS and LRno as well

my_graph <- ggplot(data=geno_c, aes(x= day, y=TRS, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length (cm)") + xlab("time (day)") + ggtitle("Total Root Length") + theme(legend.position='none')
my_graph

my_graph <- ggplot(data=geno_c, aes(x= day, y=LRno, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("number") + xlab("time (day)") + ggtitle("Lateral Root number") + theme(legend.position='none')
my_graph

my_graph <- ggplot(data=geno_c, aes(x= day, y=LRL, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length (cm)") + xlab("time (day)") + ggtitle("Lateral Root Length") + theme(legend.position='none')
my_graph

####NOW do the same thing for the duf genotype

geno_duf <- subset(MR_all.nona, MR_all.nona$genotype == "duf-5")

my_graph <- ggplot(data=geno_duf, aes(x= day, y=length, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("Main Root Length (cm)") + theme(legend.position='none')
my_graph

ggplotly(my_graph)

my_graph <- ggplot(data=geno_duf, aes(x= day, y=TRS, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length (cm)") + xlab("time (day)") + ggtitle("Total Root Length") + theme(legend.position='none')
my_graph

my_graph <- ggplot(data=geno_duf, aes(x= day, y=LRno, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("number") + xlab("time (day)") + ggtitle("Lateral Root number") + theme(legend.position='none')
my_graph

my_graph <- ggplot(data=geno_duf, aes(x= day, y=LRL, group = root_name, color = genotype)) 
my_graph <- my_graph + geom_line(alpha = 0.2) 
my_graph <- my_graph + facet_grid(~ condition) 
my_graph <- my_graph + ylab("length (cm)") + xlab("time (day)") + ggtitle("Lateral Root Length") + theme(legend.position='none')
my_graph

####lets combine all curated data from both genotypes together in one graph

MR_both_geno <- rbind(geno_c, geno_duf)

MR_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=length, group = root_name, color = genotype)) 
MR_time_graph <- MR_time_graph + geom_line(alpha = 0.2) 
MR_time_graph <- MR_time_graph + facet_grid(~ condition)
MR_time_graph <- MR_time_graph + scale_color_manual(values= c("blue", "red"))
MR_time_graph <- MR_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Main Root Length")
MR_time_graph <- MR_time_graph + stat_summary(fun.y=mean, aes(group= genotype),  size=0.7, geom="line", linetype = "dashed")

## Warning: `fun.y` is deprecated. Use `fun` instead.

MR_time_graph

LRL_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=LRL, group = root_name, color = genotype)) 
LRL_time_graph <- LRL_time_graph + geom_line(alpha = 0.2) 
LRL_time_graph <- LRL_time_graph + facet_grid(~ condition) 
LRL_time_graph <- LRL_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Lateral Root Length")
LRL_time_graph <- LRL_time_graph + stat_summary(fun.y=mean, aes(group= genotype),  size=0.7, geom="line", linetype = "dashed")

## Warning: `fun.y` is deprecated. Use `fun` instead.

LRL_time_graph

LRno_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=LRno, group = root_name, color = genotype)) 
LRno_time_graph <- LRno_time_graph + geom_line(alpha = 0.2) 
LRno_time_graph <- LRno_time_graph + facet_grid(~ condition) 
LRno_time_graph <- LRno_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Lateral Root Number")
LRno_time_graph <- LRno_time_graph + stat_summary(fun.y=mean, aes(group= genotype),  size=0.7, geom="line", linetype = "dashed")

## Warning: `fun.y` is deprecated. Use `fun` instead.

LRno_time_graph

TRS_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=TRS, group = root_name, color = genotype)) 
TRS_time_graph <- TRS_time_graph + geom_line(alpha = 0.2) 
TRS_time_graph <- TRS_time_graph + facet_grid(~ condition) 
TRS_time_graph <- TRS_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Total Root Length")
TRS_time_graph <- TRS_time_graph + stat_summary(fun.y=mean, aes(group= genotype),  size=0.7, geom="line", linetype = "dashed")

## Warning: `fun.y` is deprecated. Use `fun` instead.

TRS_time_graph

aLRL_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=aLRL, group = root_name, color = genotype)) 
aLRL_time_graph <- aLRL_time_graph + geom_line(alpha = 0.2) 
aLRL_time_graph <- aLRL_time_graph + facet_grid(~ condition) 
aLRL_time_graph <- aLRL_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Total Average Lateral Root Length")
aLRL_time_graph <- aLRL_time_graph + stat_summary(fun.y=mean, aes(group= genotype),  size=0.7, geom="line", linetype = "dashed")

## Warning: `fun.y` is deprecated. Use `fun` instead.

aLRL_time_graph

## Warning: Removed 421 rows containing non-finite values (stat_summary).

## Warning: Removed 421 row(s) containing missing values (geom_path).

pdf("20220608_Arabidopsis_duf-5_RSA_Time_Graphs.pdf", width = 13, height = 13)
plot_grid(MR_time_graph , LRL_time_graph, LRno_time_graph, TRS_time_graph, labels = c("AUTO"), ncol = 2)
#pdf("20220608_Arabidopsis_duf-5_RSA_Time_Graphs.pdf", width = 13, height = 13)
dev.off()

## png 
##   2

Growth calculations

So for calculating growth rate - lets first establish calculations on one plant. Let’s take the first plant that is within the experiment:

temp1 <- subset(MR_both_geno, MR_both_geno$root_name == unique(MR_both_geno$root_name)[1])
temp1

temp2 <- temp1[order(temp1$day),]
temp2

temp2$condition[1]

## [1] 125
## Levels: 0 75 125

# For Main Root Growth Rate - we want to remove all the datapoints that are repeating, because that indicates root hitting the plate edge:
temp_MR <- temp2[,c("day", "length")]
plot(temp_MR$length~ temp_MR$day)

so in this case - we should remove day 8 and day 9 MR, but we won;t have time to look at individual pictures, and thus let’s make it into a logical removal loop:

temp_MR$MRdouble <- "no"
  for(i in 2:nrow(temp_MR)){
    # we want the root to be at least 1 mm larger than the previous day - all the other ones will just indicate noise:
   if(temp_MR$length[i] <= temp_MR$length[i-1]+0.09){
    temp_MR$MRdouble[i] <- "yes"   
   } 
    else{
    temp_MR$MRdouble[i] <- "no"   
   } 
  }
temp_MR

plot(temp_MR$length~ temp_MR$day)
# let's add the regression line to this graph
abline(lm(temp_MR$length~ temp_MR$day))

MR_model <- lm(temp_MR$length~ temp_MR$day)
MR_model

## 
## Call:
## lm(formula = temp_MR$length ~ temp_MR$day)
## 
## Coefficients:
## (Intercept)  temp_MR$day  
##     -1.0348       0.2922

summary(MR_model)

## 
## Call:
## lm(formula = temp_MR$length ~ temp_MR$day)
## 
## Residuals:
##         1         2         3         4         5 
##  0.158735 -0.121902 -0.131394 -0.006445  0.101006 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.03485    0.22385  -4.623  0.01906 * 
## temp_MR$day  0.29223    0.02373  12.316  0.00115 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1501 on 3 degrees of freedom
## Multiple R-squared:  0.9806, Adjusted R-squared:  0.9741 
## F-statistic: 151.7 on 1 and 3 DF,  p-value: 0.001153

MR_model$coefficients[[2]]

## [1] 0.2922305

MR_growth_rate <- MR_model$coefficients[[2]]

Cool - now we have to do this for LRno and aLRL, which are much easier, because we just need to remove all the ‘n.a.’

# Let's remove all NA for LRno and aLRL in temp2
temp2

LR_temp <- temp2[,c("day", "LRno", "aLRL")]
LR_temp2 <- na.omit(LR_temp)
LR_temp2

# Let's start with LRno
plot(LR_temp2$LRno ~ LR_temp2$day)
abline(lm(LR_temp2$LRno ~ LR_temp2$day))

LRno_model <- lm(LR_temp2$LRno ~ LR_temp2$day)
LRno_model

## 
## Call:
## lm(formula = LR_temp2$LRno ~ LR_temp2$day)
## 
## Coefficients:
##  (Intercept)  LR_temp2$day  
##            1             0

summary(LRno_model)

## Warning in summary.lm(LRno_model): essentially perfect fit: summary may be
## unreliable

## 
## Call:
## lm(formula = LR_temp2$LRno ~ LR_temp2$day)
## 
## Residuals:
## 1 2 3 
## 0 0 0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         1          0     Inf   <2e-16 ***
## LR_temp2$day        0          0      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0 on 1 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 1 DF,  p-value: NA

LRno_increase <- as.numeric(as.character(LRno_model$coefficients[[2]]))
LRno_increase

## [1] 0

Now let’s move on to aLRL

# Let's start with LRno
plot(LR_temp2$aLRL ~ LR_temp2$day)
abline(lm(LR_temp2$aLRL ~ LR_temp2$day))

aLRL_model <- lm(LR_temp2$aLRL ~ LR_temp2$day)
aLRL_model

## 
## Call:
## lm(formula = LR_temp2$aLRL ~ LR_temp2$day)
## 
## Coefficients:
##  (Intercept)  LR_temp2$day  
##      -0.9236        0.1181

summary(aLRL_model)

## 
## Call:
## lm(formula = LR_temp2$aLRL ~ LR_temp2$day)
## 
## Residuals:
##         1         2         3 
## -0.005914  0.011828 -0.005914 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.923611   0.056955  -16.22   0.0392 *
## LR_temp2$day  0.118135   0.005122   23.07   0.0276 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01449 on 1 degrees of freedom
## Multiple R-squared:  0.9981, Adjusted R-squared:  0.9962 
## F-statistic:   532 on 1 and 1 DF,  p-value: 0.02758

aLRL_growth <- as.numeric(as.character(aLRL_model$coefficients[[2]]))
aLRL_growth

## [1] 0.1181354

OK - so now let’s make a new empty table to save all of the data from these calculations

temp2$condition[1]

## [1] 125
## Levels: 0 75 125

temp2$condition[1][1]

## [1] 125
## Levels: 0 75 125

names <- c(text="root_name", "genotype", "condition", "MR.delta", "LRno.delta", "aLRL.delta")
growth_factors <- data.frame()
growth_factors

for (k in names) growth_factors[[k]] <- as.character()

growth_factors[1,1] <- temp2$root_name[1]
growth_factors[1,2] <- temp2$genotype[1]
growth_factors[1,3] <- temp2$condition[[1]]
growth_factors[1,4] <- as.numeric(as.character(MR_growth_rate))
growth_factors[1,5] <- as.numeric(as.character(LRno_increase))
growth_factors[1,6] <- as.numeric(as.character(aLRL_growth))

growth_factors

Ok - before we loop it - lets see how many plants we have in our dataset:

length(unique(MR_both_geno$root_name))

## [1] 160

Now - lets loop this loop

for(e in 1:160){
  temp1 <- subset(MR_both_geno, MR_both_geno$root_name == unique(MR_both_geno$root_name)[e])
  temp2 <- temp1[order(temp1$day),]
  temp2
  
  if(dim(temp2)[1] > 2){
  ############ MR calculations
  temp_MR <- temp2[,c("day", "length")]
  temp_MR$MRdouble <- "no"
  for(i in 2:nrow(temp_MR)){
    # we want the root to be at least 1 mm larger than the previous day - all the other ones will just indicate noise:
   if(temp_MR$length[i] <= temp_MR$length[i-1]+0.09){
    temp_MR$MRdouble[i] <- "yes"   
   } else{temp_MR$MRdouble[i] <- "no"}}
  temp_MR2 <- subset(temp_MR, temp_MR$MRdouble == "no")
  temp_MR2
  MR_model <- lm(temp_MR2$length~ temp_MR2$day)
  MR_growth_rate <- MR_model$coefficients[[2]]
  MR_growth_rate }else{
  MR_growth_rate <- 0  
  }
  ############ LRno calculations
  LR_temp <- temp2[,c("day", "LRno", "aLRL")]
  LR_temp2 <- na.omit(LR_temp)
  LR_temp2
  dim(LR_temp2)
  
  ####################### safety precaution to calculate LR growth rate only for the plants that have LR at least for two days: 
  if(dim(LR_temp2)[1] > 1){
  LRno_model <- lm(LR_temp2$LRno ~ LR_temp2$day)
  LRno_increase <- as.numeric(as.character(LRno_model$coefficients[[2]]))

  ############ aLRL calculations
  aLRL_model <- lm(LR_temp2$aLRL ~ LR_temp2$day)
  aLRL_growth <- as.numeric(as.character(aLRL_model$coefficients[[2]]))
  } else{
  ####################### safety precaution continued:
  ####################### so if you only have one day where LR are there - this wont be good enough to calculate LRno or LRL rate
  ####################### and thus:
  LRno_increase <- 0
  aLRL_growth <- 0
  }
  LRno_increase
  aLRL_growth
  ############ adding the information to the table:
  growth_factors[e,1] <- temp2$root_name[1]
  growth_factors[e,2] <- temp2$genotype[1]
  growth_factors[e,3] <- temp2$condition[1]
  growth_factors[e,4] <- as.numeric(as.character(MR_growth_rate))
  growth_factors[e,5] <- as.numeric(as.character(LRno_increase))
  growth_factors[e,6] <- as.numeric(as.character(aLRL_growth))
}


growth_factors

Now - let’s subset for all plants that actually have growth:

growth_factors <- subset(growth_factors, growth_factors$MR.delta > 0)
growth_factors

unique(growth_factors$condition)

## [1] "3" "1" "2"

growth_factors$condition <- gsub("1", "0", growth_factors$condition)
growth_factors$condition <- gsub("2", "75", growth_factors$condition)
growth_factors$condition <- gsub("3", "125", growth_factors$condition)
growth_factors$condition <- as.factor(growth_factors$condition)
growth_factors

write.csv(growth_factors, "20211013_duf-5_growth_factors.csv", row.names = FALSE)

Visualizing the growth factors:

growth_factors2 <- subset(growth_factors, growth_factors$MR.delta >= 0)

growth_factors$MR.delta <- as.numeric(as.character(growth_factors$MR.delta))
growth_factors$aLRL.delta <- as.numeric(as.character(growth_factors$aLRL.delta))
growth_factors$LRno.delta <- as.numeric(as.character(growth_factors$LRno.delta))

growth_factors$condition <- factor(growth_factors$condition, levels = c("0", "75", "125"))

MR.delta_p_geno <- ggerrorplot(growth_factors2, y = "MR.delta", x = "genotype", fill="genotype", color="genotype", 
                        facet.by = c("condition"), ncol=3,
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="", ylab="Growth Rate (cm / day)") 
MR.delta_p_geno <- MR.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = "Col-0", 
                                                              label = "p.signif")
MR.delta_p_geno <- MR.delta_p_geno + ggtitle("Main Root Growth")
MR.delta_p_geno

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 76, 79, 72, 74, 67, 77, 80, 71, 63, 66, 41, 64, 70, 73, 75, 68, 69, 78, 65, 43, 159, 157, 156, 151, 158, 152, 154, 153, 160, 135, 146, 149, 147, 140, 148, 145, 155, 137, 123, 150

## Warning: Computation failed in `stat_compare_means()`:
## Problem with `mutate()` column `p`.
## i `p = purrr::map(...)`.
## x not enough 'x' observations

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 48, 62, 49, 57, 61, 45, 44, 50, 55, 60, 53, 54, 51, 59, 47, 56, 46, 58, 52, 42, 143, 142, 127, 141, 124, 134, 121, 122, 130, 129, 136, 144, 125, 132, 128, 131, 139, 126, 138, 133

## Warning: Removed 40 rows containing missing values (geom_segment).

## Warning: Removed 80 rows containing missing values (geom_segment).

## Warning: Removed 40 rows containing missing values (geom_segment).

growth_factors2 <- subset(growth_factors, growth_factors$LRno.delta >= 0)

LRno.delta_p_geno <- ggerrorplot(growth_factors2, y = "LRno.delta", x = "genotype", fill="genotype", color="genotype", 
                        facet.by = c("condition"), ncol=3,
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="", ylab="LR no increase (#LR / day)") 
LRno.delta_p_geno <- LRno.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = "Col-0", 
                                                              label = "p.signif")
LRno.delta_p_geno <- LRno.delta_p_geno + ggtitle("Lateral Root Increase")
LRno.delta_p_geno

growth_factors2 <- subset(growth_factors, growth_factors$aLRL.delta >= 0)

aLRL.delta_p_geno2 <- ggerrorplot(growth_factors2, y = "aLRL.delta", x = "genotype", fill="genotype", color="genotype", 
                        facet.by = c("condition"), ncol=3,
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="", ylab="Lateral Root Growth Rate (cm / day)") 
aLRL.delta_p_geno2 <- aLRL.delta_p_geno2 + rremove("legend") + stat_compare_means(method="t.test", ref.group = "Col-0", 
                                                              label = "p.signif")
aLRL.delta_p_geno2

Calculating the relative growth rates (Salt Tolerance Indexes):

So in order to calculate relative plant performance at salt, we need to divide all the growth rates by the average value for that specific genotype under control conditions.

Let’s start with calculating the average growth rate per accession per condition then:

library(doBy)

## Warning: package 'doBy' was built under R version 4.0.5

avg_growth <- summaryBy(data = growth_factors2, MR.delta + aLRL.delta + LRno.delta ~ genotype + condition)
avg_growth

Then - we subset the data into only control condition, and merge the average control with the growth_factors in all conditions:

avg_growth_C <- subset(avg_growth, avg_growth$condition == "0")
avg_growth_C

avg_growth_C <- avg_growth_C[,c(1,3:5)]
avg_growth_C

colnames(avg_growth_C) <- gsub(".mean", ".avg.Control", colnames(avg_growth_C))
avg_growth_C

now let’s merge it into the growth_factors:

STI_growth_factors <- merge(growth_factors2, avg_growth_C, id="genotype")
STI_growth_factors

Now - let’s calculate Salt Tolerance Indexes (STI) by dividing individual growth rates by their avg.Control:

STI_growth_factors$MR.STI <- STI_growth_factors$MR.delta / STI_growth_factors$MR.delta.avg.Control
STI_growth_factors$aLRL.STI <- STI_growth_factors$aLRL.delta / STI_growth_factors$aLRL.delta.avg.Control
STI_growth_factors$LRno.STI <- STI_growth_factors$LRno.delta / STI_growth_factors$LRno.delta.avg.Control
head(STI_growth_factors)

odd <- subset(STI_growth_factors, STI_growth_factors$aLRL.STI < 0)
odd

library(reshape2)
STI <- subset(STI_growth_factors, STI_growth_factors$cond == "125")
STI125 <- STI[,c(1,10:12)]
STI125

mSTI125 <- melt(STI125)

## Using genotype as id variables

colnames(mSTI125)[3] <- "fraction.of.control"
mSTI125

unique(mSTI125$variable)

## [1] MR.STI   aLRL.STI LRno.STI
## Levels: MR.STI aLRL.STI LRno.STI

Cool - now let’s visualize this thing for MR:

STI125_plot <- ggplot(data = mSTI125, aes(x = genotype, y = fraction.of.control, colour = genotype)) 
STI125_plot <- STI125_plot + geom_beeswarm(alpha=0.6, priority = "density")
STI125_plot <- STI125_plot + facet_wrap(~ variable, ncol = 3)
STI125_plot <- STI125_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")

## Warning: `fun.y` is deprecated. Use `fun` instead.

STI125_plot <- STI125_plot + theme(legend.position = "none")
STI125_plot <- STI125_plot + xlab("")
STI125_plot <- STI125_plot + ylab("Fraction of control") + ggtitle("Salt Tolerance Index for roots grown at 125 mM NaCl")
STI125_plot <- STI125_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0") 
STI125_plot <- STI125_plot + scale_color_manual(values=c("royalblue", "coral3"))
STI125_plot

STI <- subset(STI_growth_factors, STI_growth_factors$cond == "75")
STI75 <- STI[,c(1,10:12)]
STI75

mSTI75 <- melt(STI75)

## Using genotype as id variables

colnames(mSTI75)[3] <- "fraction.of.control"
mSTI75

unique(mSTI75$variable)

## [1] MR.STI   aLRL.STI LRno.STI
## Levels: MR.STI aLRL.STI LRno.STI

STI75_plot <- ggplot(data = mSTI75, aes(x = genotype, y = fraction.of.control, colour = genotype)) 
STI75_plot <- STI75_plot + geom_beeswarm(alpha=0.6, priority = "density")
STI75_plot <- STI75_plot + facet_wrap(~ variable, ncol = 3)
STI75_plot <- STI75_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")

## Warning: `fun.y` is deprecated. Use `fun` instead.

STI75_plot <- STI75_plot + theme(legend.position = "none")
STI75_plot <- STI75_plot + xlab("")
STI75_plot <- STI75_plot + ylab("Fraction of control") + ggtitle("Salt Tolerance Index for roots grown at 75 mM NaCl")
STI75_plot <- STI75_plot + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "Col-0") 
STI75_plot <- STI75_plot + scale_color_manual(values=c("royalblue", "coral3"))
STI75_plot

pdf("20220609_Arabidopsis_duf-5_RSA_Growth_factors_Graphs.pdf", width = 13, height = 13)
plot_grid(MR.delta_p_geno , aLRL.delta_p_geno2, LRno.delta_p_geno, STI75_plot, labels = c("AUTO"), ncol = 2)

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 76, 79, 72, 74, 67, 77, 80, 71, 63, 66, 41, 64, 70, 73, 75, 68, 69, 78, 65, 43, 159, 157, 156, 151, 158, 152, 154, 153, 160, 135, 146, 149, 147, 140, 148, 145, 155, 137, 123, 150

## Warning: Computation failed in `stat_compare_means()`:
## Problem with `mutate()` column `p`.
## i `p = purrr::map(...)`.
## x not enough 'x' observations

## Warning: Computation failed in `stat_compare_means()`:
## Can't find specified reference group: 1. Allowed values include one of: 48, 62, 49, 57, 61, 45, 44, 50, 55, 60, 53, 54, 51, 59, 47, 56, 46, 58, 52, 42, 143, 142, 127, 141, 124, 134, 121, 122, 130, 129, 136, 144, 125, 132, 128, 131, 139, 126, 138, 133

## Warning: Removed 40 rows containing missing values (geom_segment).

## Warning: Removed 80 rows containing missing values (geom_segment).

## Warning: Removed 40 rows containing missing values (geom_segment).

dev.off()

## png 
##   2

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