20230502_RSA_M248M058LA1511_F1_100mM_Salt

The seeds were sterilized in 50% bleach for 10 min, then rinsed 5 times with dionized water, and incubated at 4 °C overnight. Then seeds were germinated in ¼ MS control with vitamins plate for 4 days in growth chamber. At d5, the seedlings were transferred to either 100 mM salt or control plates, and started being scanned for 5 total consecutive days (starting from d5 to d9). The growth chamber condition was 26 °C and 19 h light /5 h dark cycle, with 60% humidity (Chamber #35).The root tracing was done using ImageJ SamrtRoot plugin by Trent Donaldson. Shoot and root fresh weight were measured after 10 d at salt.

getwd()

## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20230109_RSA_M248M058LA1511_F1_100mM_Salt_Trent"

setwd("C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20230109_RSA_M248M058LA1511_F1_100mM_Salt_Trent")
list.files(pattern = ".csv")

## [1] "202305_RSA_F1_growth_factors.csv" "d5-F1.csv"                       
## [3] "d6-F1.csv"                        "d7-F1.csv"                       
## [5] "d8-F1.csv"                        "d9-F1.csv"

my_data_d5 <-  read.csv("d5-F1.csv")
my_data_d6 <-  read.csv("d6-F1.csv")
my_data_d7 <-  read.csv("d7-F1.csv")
my_data_d8 <-  read.csv("d8-F1.csv")
my_data_d9 <-  read.csv("d9-F1.csv")
head(my_data_d5)

head(my_data_d6)

head(my_data_d7)

head(my_data_d8)

head(my_data_d9)

Adding the day

my_data_d5$day <- 5
my_data_d6$day <- 6
my_data_d7$day <- 7
my_data_d8$day <- 8
my_data_d9$day <- 9
head(my_data_d8)

head(my_data_d5)

fuse days together

all_data <- rbind(my_data_d5, my_data_d6)
all_data <- rbind(all_data, my_data_d7)
all_data <- rbind(all_data, my_data_d8)
all_data <- rbind(all_data, my_data_d9)
all_data

unique(all_data$day)

## [1] 5 6 7 8 9

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

Let’s navigate the columns in the fused file

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(1:4,10,16,17,19:22)]
MR_data_2

Lateral_root <- subset(all_data, all_data$root_order != 0)
Lateral_root

unique(Lateral_root$root_order)

## [1] 1

MR_data_3 <- subset(MR_data_2, MR_data_2$n_child >0)

Because we also have secondary LR in there - we should remove them from calculations of LR

temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_3$root[1])
temporary

temporary <- subset(temporary, temporary$day == MR_data_3$day[3])
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] 3.808891

LR_number

## [1] 2

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

length(MR_data_3$root)

## [1] 315

for(i in 2:315){
  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
}

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

unique(MR_data_3$check)

## [1]   0  -6 -17  -7 -21

#let's remove check column from Child:

MR_data_Child2 <- MR_data_3[,1:13]
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)
MR_all

?strsplit()

## starting httpd help server ... done

MR_all$root_name[1]

## [1] " pl10-c-m248"

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

## [[1]]
## [1] " pl10" "c"     "m248"

plate <- text[[1]][1]
plate <- gsub(" ", "", plate)
plate

## [1] "pl10"

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

## [1] "c"

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

## [1] "m248"

dim(MR_all)

## [1] 387  13

for(i in 1:387){
  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

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

length(unique(MR_all$root_name))

## [1] 80

unique(MR_all$day)

## [1] 5 6 7 8 9

MR_all$day <- as.numeric(as.character(MR_all$day))
unique(MR_all$day)

## [1] 5 6 7 8 9

length(unique(MR_all$root_name))

## [1] 80

dim(MR_all)

## [1] 387  19

387/80

## [1] 4.8375

#it looks suspicious….

unique(MR_all$root_name)

##  [1] " pl10-c-m248"   " pl9-c-m248"    " pl8-c-m248"    " pl1-c-m248"   
##  [5] " pl10-s-m248"   " pl7-s-m248"    " pl3-s-m248"    " pl2-s-m248"   
##  [9] " pl9-s-la1511"  " pl6-c-la1511"  " pl5-s-f1"      " pl5-s-m058"   
## [13] " pl4-s-m058"    " pl8-c-m058"    " pl3-c-m058"    " pl6-s-m058"   
## [17] " pl7-c-m248"    " pl6-c-m248"    " pl5-c-m248"    " pl4-c-m248"   
## [21] " pl3-c-m248"    " pl2-c-m248"    " pl9-s-m248"    " pl8-s-m248"   
## [25] " pl5-s-m248"    " pl4-s-m248"    " pl1-s-m248"    " pl10-s-la1511"
## [29] " pl8-s-la1511"  " pl7-s-la1511"  " pl6-s-la1511"  " pl5-s-la1511" 
## [33] " pl4-s-la1511"  " pl3-s-la1511"  " pl2-s-LA1511"  " pl1-s-la1511" 
## [37] " pl10-c-la1511" " pl9-c-la1511"  " pl8-c-la1511"  " pl7-c-la1511" 
## [41] " pl5-c-la1511"  " pl4-c-la1511"  " pl3-c-la1511"  " pl2-c-la1511" 
## [45] " pl1-c-la1511"  " pl10-s-f1"     " pl9-s-f1"      " pl8-s-f1"     
## [49] " pl7-s-f1"      " pl6-s-f1"      " pl4-s-f1"      " pl3-s-f1"     
## [53] " pl2-s-f1"      " pl1-s-f1"      " pl2-s-m058"    " pl1-s-m058"   
## [57] " pl9-c-f1"      " pl8-c-f1"      " pl7-c-f1"      " pl6-c-f1"     
## [61] " pl5-c-f1"      " pl4-c-f1"      " pl3-c-f1"      " pl2-c-f1"     
## [65] " pl1-c-f1"      " pl10-c-m058"   " pl9-c-m058"    " pl6-c-m058"   
## [69] " pl5-c-m058"    " pl4-c-m058"    " pl2-c-m058"    " pl7-s-m058"   
## [73] " pl2-s-la1511"  " pl9-s-F1"      " pl6-c-1f"      " pl7-c-m058"   
## [77] " pl1-c-m058"    " pl9-c-LA1511"  " pl3-s-m058"    " root_0"

unique(MR_all$genotype)

## [1] "m248"   "la1511" "f1"     "m058"   "LA1511" "F1"     "1f"     NA

good_stuff <- c("m248", "m058", "la1511", "f1")
funk <- subset(MR_all, !(MR_all$genotype %in% good_stuff))
funk

unique(MR_all$genotype)

## [1] "m248"   "la1511" "f1"     "m058"   "LA1511" "F1"     "1f"     NA

MR_all <- subset(MR_all, (MR_all$genotype %in% good_stuff))

Data visualization

start visualizing

library(ggplot2)
library(ggpubr)
histogram_TRS <- ggdensity(MR_all, x = "TRS",
                           add = "mean", rug = TRUE, facet.by = "day",
                           color = "condition", fill = "condition",
                           palette = c("#00AFBB", "#E7B800"))

histogram_TRS

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

histogram_LRno

pdf("histogram.TRS.F1.pdf")
plot(histogram_TRS)
# if plotting multiple graphs - this command is extremely important 
dev.off()

## png 
##   2

pdf("histogram.LRno.F1.pdf")
plot(histogram_LRno)
# 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_LRno, ncol=2,
          align = "hv", labels=c("AUTO"), 
          label_size = 24)
dev.off()

## png 
##   2

unique(MR_all$genotype)

## [1] "m248"   "la1511" "f1"     "m058"

#we need to compare everything together here……Tukey test

library(ggsci)
library(ggbeeswarm)
library(gapminder)
library(RColorBrewer)
library(ggridges)
MR_all$genotype <- factor(MR_all$genotype, levels = c("m248", "m058", "la1511", "f1"))

#better_TRS_graph <- ggplot(data=MR_all, aes(x= genotype, y=TRS, color = genotype))
#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")
#better_TRS_graph <- better_TRS_graph + facet_grid(day ~ condition, scales = "free") + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange", "green"))
#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 = "a", hide.ns = TRUE) 
#better_TRS_graph



#need to create column all ID


MR_all$All.ID<-paste(MR_all$genotype, MR_all$condition, MR_all$day, sep="_")
MR_all

aov(TRS ~ All.ID, data = MR_all)

## Call:
##    aov(formula = TRS ~ All.ID, data = MR_all)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  70505.56   9178.53
## Deg. of Freedom       39       336
## 
## Residual standard error: 5.226573
## Estimated effects may be unbalanced

Output <- TukeyHSD(aov(TRS ~ All.ID, data = MR_all))
Output

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = TRS ~ All.ID, data = MR_all)
## 
## $All.ID
##                               diff          lwr          upr     p adj
## f1_c_6-f1_c_5           3.16444144  -6.50466618  12.83354907 0.9999998
## f1_c_7-f1_c_5          16.30260991   6.33592191  26.26929792 0.0000004
## f1_c_8-f1_c_5          32.28933357  22.62022594  41.95844119 0.0000000
## f1_c_9-f1_c_5          58.39658986  48.72748223  68.06569748 0.0000000
## f1_s_5-f1_c_5           0.56375761  -8.86052274   9.98803796 1.0000000
## f1_s_6-f1_c_5           2.95557199  -6.46870836  12.37985234 0.9999999
## f1_s_7-f1_c_5          10.96190237   1.29279475  20.63100999 0.0070939
## f1_s_8-f1_c_5          23.84025483  14.41597448  33.26453518 0.0000000
## f1_s_9-f1_c_5          42.84362778  33.41934743  52.26790813 0.0000000
## la1511_c_5-f1_c_5      -0.15104770  -9.57532805   9.27323265 1.0000000
## la1511_c_6-f1_c_5       1.33756720  -8.08671315  10.76184755 1.0000000
## la1511_c_7-f1_c_5       8.36262108  -1.06165927  17.78690143 0.1879160
## la1511_c_8-f1_c_5      17.95647084   8.28736321  27.62557846 0.0000000
## la1511_c_9-f1_c_5      33.60692724  24.18264689  43.03120759 0.0000000
## la1511_s_5-f1_c_5       0.05087919  -9.37340116   9.47515954 1.0000000
## la1511_s_6-f1_c_5       0.86275254  -8.80635508  10.53186016 1.0000000
## la1511_s_7-f1_c_5       4.89139222  -4.53288813  14.31567257 0.9929113
## la1511_s_8-f1_c_5      10.72663519   1.30235484  20.15091554 0.0066031
## la1511_s_9-f1_c_5      15.40676325   5.98248290  24.83104360 0.0000004
## m058_c_5-f1_c_5         0.06481397  -9.35946638   9.48909432 1.0000000
## m058_c_6-f1_c_5         2.41092837  -7.01335198  11.83520872 1.0000000
## m058_c_7-f1_c_5         6.26982812  -3.15445223  15.69410847 0.8226670
## m058_c_8-f1_c_5        10.76235548   1.33807513  20.18663583 0.0062133
## m058_c_9-f1_c_5        19.17737801   9.75309766  28.60165836 0.0000000
## m058_s_5-f1_c_5         1.53745919  -8.79925165  11.87417004 1.0000000
## m058_s_6-f1_c_5         2.62845198  -7.70825887  12.96516282 1.0000000
## m058_s_7-f1_c_5         4.26399364  -6.07271721  14.60070448 0.9999292
## m058_s_8-f1_c_5         6.49378102  -3.84292982  16.83049187 0.9001273
## m058_s_9-f1_c_5        10.34682150   0.01011066  20.68353235 0.0493596
## m248_c_5-f1_c_5         3.02026751  -6.40401284  12.44454786 0.9999999
## m248_c_6-f1_c_5         3.44997808  -5.97430227  12.87425843 0.9999962
## m248_c_7-f1_c_5        11.02200658   1.59772623  20.44628693 0.0039619
## m248_c_8-f1_c_5        23.25941530  13.83513495  32.68369565 0.0000000
## m248_c_9-f1_c_5        43.49743012  34.07314977  52.92171047 0.0000000
## m248_s_5-f1_c_5         0.49509575  -8.92918460   9.91937610 1.0000000
## m248_s_6-f1_c_5         2.00066201  -7.42361834  11.42494236 1.0000000
## m248_s_7-f1_c_5         4.98965462  -4.43462573  14.41393497 0.9901614
## m248_s_8-f1_c_5        10.10899571   0.68471536  19.53327606 0.0181106
## m248_s_9-f1_c_5        19.07609906   9.65181871  28.50037941 0.0000000
## f1_c_7-f1_c_6          13.13816847   3.17148046  23.10485648 0.0002814
## f1_c_8-f1_c_6          29.12489212  19.45578450  38.79399975 0.0000000
## f1_c_9-f1_c_6          55.23214841  45.56304079  64.90125604 0.0000000
## f1_s_5-f1_c_6          -2.60068383 -12.02496418   6.82359652 1.0000000
## f1_s_6-f1_c_6          -0.20886946  -9.63314981   9.21541089 1.0000000
## f1_s_7-f1_c_6           7.79746093  -1.87164670  17.46656855 0.3890550
## f1_s_8-f1_c_6          20.67581339  11.25153304  30.10009374 0.0000000
## f1_s_9-f1_c_6          39.67918633  30.25490598  49.10346668 0.0000000
## la1511_c_5-f1_c_6      -3.31548914 -12.73976949   6.10879121 0.9999987
## la1511_c_6-f1_c_6      -1.82687424 -11.25115459   7.59740611 1.0000000
## la1511_c_7-f1_c_6       5.19817963  -4.22610072  14.62245998 0.9813977
## la1511_c_8-f1_c_6      14.79202939   5.12292177  24.46113702 0.0000038
## la1511_c_9-f1_c_6      30.44248579  21.01820544  39.86676614 0.0000000
## la1511_s_5-f1_c_6      -3.11356226 -12.53784261   6.31071809 0.9999998
## la1511_s_6-f1_c_6      -2.30168891 -11.97079653   7.36741872 1.0000000
## la1511_s_7-f1_c_6       1.72695077  -7.69732958  11.15123112 1.0000000
## la1511_s_8-f1_c_6       7.56219375  -1.86208660  16.98647410 0.4011646
## la1511_s_9-f1_c_6      12.24232181   2.81804146  21.66660216 0.0004035
## m058_c_5-f1_c_6        -3.09962747 -12.52390782   6.32465288 0.9999998
## m058_c_6-f1_c_6        -0.75351307 -10.17779342   8.67076728 1.0000000
## m058_c_7-f1_c_6         3.10538667  -6.31889368  12.52966702 0.9999998
## m058_c_8-f1_c_6         7.59791404  -1.82636631  17.02219439 0.3897264
## m058_c_9-f1_c_6        16.01293656   6.58865621  25.43721691 0.0000001
## m058_s_5-f1_c_6        -1.62698225 -11.96369310   8.70972859 1.0000000
## m058_s_6-f1_c_6        -0.53598947 -10.87270031   9.80072138 1.0000000
## m058_s_7-f1_c_6         1.09955219  -9.23715865  11.43626304 1.0000000
## m058_s_8-f1_c_6         3.32933958  -7.00737127  13.66605043 0.9999999
## m058_s_9-f1_c_6         7.18238006  -3.15433079  17.51909090 0.7434217
## m248_c_5-f1_c_6        -0.14417393  -9.56845428   9.28010642 1.0000000
## m248_c_6-f1_c_6         0.28553664  -9.13874371   9.70981699 1.0000000
## m248_c_7-f1_c_6         7.85756513  -1.56671522  17.28184548 0.3113750
## m248_c_8-f1_c_6        20.09497385  10.67069350  29.51925420 0.0000000
## m248_c_9-f1_c_6        40.33298867  30.90870832  49.75726902 0.0000000
## m248_s_5-f1_c_6        -2.66934569 -12.09362604   6.75493466 1.0000000
## m248_s_6-f1_c_6        -1.16377943 -10.58805978   8.26050092 1.0000000
## m248_s_7-f1_c_6         1.82521317  -7.59906718  11.24949352 1.0000000
## m248_s_8-f1_c_6         6.94455427  -2.47972608  16.36883462 0.6124347
## m248_s_9-f1_c_6        15.91165761   6.48737726  25.33593796 0.0000001
## f1_c_8-f1_c_7          15.98672365   6.02003565  25.95341166 0.0000007
## f1_c_9-f1_c_7          42.09397994  32.12729193  52.06066795 0.0000000
## f1_s_5-f1_c_7         -15.73885230 -25.46820417  -6.00950044 0.0000005
## f1_s_6-f1_c_7         -13.34703793 -23.07638979  -3.61768606 0.0000999
## f1_s_7-f1_c_7          -5.34070754 -15.30739555   4.62598047 0.9880729
## f1_s_8-f1_c_7           7.53764492  -2.19170695  17.26699678 0.4883373
## f1_s_9-f1_c_7          26.54101786  16.81166599  36.27036973 0.0000000
## la1511_c_5-f1_c_7     -16.45365761 -26.18300948  -6.72430574 0.0000001
## la1511_c_6-f1_c_7     -14.96504271 -24.69439458  -5.23569085 0.0000032
## la1511_c_7-f1_c_7      -7.93998884 -17.66934070   1.78936303 0.3606141
## la1511_c_8-f1_c_7       1.65386092  -8.31282709  11.62054893 1.0000000
## la1511_c_9-f1_c_7      17.30431732   7.57496546  27.03366919 0.0000000
## la1511_s_5-f1_c_7     -16.25173073 -25.98108260  -6.52237886 0.0000002
## la1511_s_6-f1_c_7     -15.43985738 -25.40654538  -5.47316937 0.0000025
## la1511_s_7-f1_c_7     -11.41121770 -21.14056956  -1.68186583 0.0037486
## la1511_s_8-f1_c_7      -5.57597472 -15.30532659   4.15337715 0.9677813
## la1511_s_9-f1_c_7      -0.89584666 -10.62519853   8.83350521 1.0000000
## m058_c_5-f1_c_7       -16.23779594 -25.96714781  -6.50844408 0.0000002
## m058_c_6-f1_c_7       -13.89168154 -23.62103341  -4.16232968 0.0000325
## m058_c_7-f1_c_7       -10.03278180 -19.76213367  -0.30342993 0.0327936
## m058_c_8-f1_c_7        -5.54025443 -15.26960630   4.18909743 0.9705247
## m058_c_9-f1_c_7         2.87476809  -6.85458378  12.60411996 1.0000000
## m058_s_5-f1_c_7       -14.76515072 -25.38074391  -4.14955753 0.0000685
## m058_s_6-f1_c_7       -13.67415794 -24.28975113  -3.05856475 0.0004941
## m058_s_7-f1_c_7       -12.03861628 -22.65420946  -1.42302309 0.0070548
## m058_s_8-f1_c_7        -9.80882889 -20.42442208   0.80676430 0.1267223
## m058_s_9-f1_c_7        -5.95578841 -16.57138160   4.65980478 0.9761218
## m248_c_5-f1_c_7       -13.28234240 -23.01169427  -3.55299053 0.0001139
## m248_c_6-f1_c_7       -12.85263183 -22.58198370  -3.12327997 0.0002668
## m248_c_7-f1_c_7        -5.28060334 -15.00995520   4.44874853 0.9854367
## m248_c_8-f1_c_7         6.95680538  -2.77254648  16.68615725 0.6824288
## m248_c_9-f1_c_7        27.19482020  17.46546833  36.92417207 0.0000000
## m248_s_5-f1_c_7       -15.80751416 -25.53686603  -6.07816230 0.0000005
## m248_s_6-f1_c_7       -14.30194790 -24.03129977  -4.57259604 0.0000136
## m248_s_7-f1_c_7       -11.31295530 -21.04230716  -1.58360343 0.0044304
## m248_s_8-f1_c_7        -6.19361420 -15.92296607   3.53573766 0.8849541
## m248_s_9-f1_c_7         2.77348914  -6.95586273  12.50284101 1.0000000
## f1_c_9-f1_c_8          26.10725629  16.43814867  35.77636391 0.0000000
## f1_s_5-f1_c_8         -31.72557596 -41.14985631 -22.30129561 0.0000000
## f1_s_6-f1_c_8         -29.33376158 -38.75804193 -19.90948123 0.0000000
## f1_s_7-f1_c_8         -21.32743120 -30.99653882 -11.65832357 0.0000000
## f1_s_8-f1_c_8          -8.44907874 -17.87335909   0.97520161 0.1708766
## f1_s_9-f1_c_8          10.55429421   1.13001386  19.97857456 0.0088228
## la1511_c_5-f1_c_8     -32.44038127 -41.86466162 -23.01610092 0.0000000
## la1511_c_6-f1_c_8     -30.95176637 -40.37604672 -21.52748602 0.0000000
## la1511_c_7-f1_c_8     -23.92671249 -33.35099284 -14.50243214 0.0000000
## la1511_c_8-f1_c_8     -14.33286273 -24.00197035  -4.66375511 0.0000105
## la1511_c_9-f1_c_8       1.31759367  -8.10668668  10.74187402 1.0000000
## la1511_s_5-f1_c_8     -32.23845438 -41.66273473 -22.81417403 0.0000000
## la1511_s_6-f1_c_8     -31.42658103 -41.09568865 -21.75747341 0.0000000
## la1511_s_7-f1_c_8     -27.39794135 -36.82222170 -17.97366100 0.0000000
## la1511_s_8-f1_c_8     -21.56269838 -30.98697873 -12.13841803 0.0000000
## la1511_s_9-f1_c_8     -16.88257031 -26.30685066  -7.45828996 0.0000000
## m058_c_5-f1_c_8       -32.22451960 -41.64879995 -22.80023925 0.0000000
## m058_c_6-f1_c_8       -29.87840520 -39.30268555 -20.45412485 0.0000000
## m058_c_7-f1_c_8       -26.01950545 -35.44378580 -16.59522510 0.0000000
## m058_c_8-f1_c_8       -21.52697809 -30.95125844 -12.10269774 0.0000000
## m058_c_9-f1_c_8       -13.11195556 -22.53623591  -3.68767521 0.0000680
## m058_s_5-f1_c_8       -30.75187438 -41.08858522 -20.41516353 0.0000000
## m058_s_6-f1_c_8       -29.66088159 -39.99759244 -19.32417075 0.0000000
## m058_s_7-f1_c_8       -28.02533993 -38.36205078 -17.68862908 0.0000000
## m058_s_8-f1_c_8       -25.79555254 -36.13226339 -15.45884170 0.0000000
## m058_s_9-f1_c_8       -21.94251207 -32.27922291 -11.60580122 0.0000000
## m248_c_5-f1_c_8       -29.26906606 -38.69334641 -19.84478571 0.0000000
## m248_c_6-f1_c_8       -28.83935549 -38.26363584 -19.41507514 0.0000000
## m248_c_7-f1_c_8       -21.26732699 -30.69160734 -11.84304664 0.0000000
## m248_c_8-f1_c_8        -9.02991827 -18.45419862   0.39436208 0.0849511
## m248_c_9-f1_c_8        11.20809655   1.78381620  20.63237690 0.0028464
## m248_s_5-f1_c_8       -31.79423782 -41.21851817 -22.36995747 0.0000000
## m248_s_6-f1_c_8       -30.28867156 -39.71295191 -20.86439121 0.0000000
## m248_s_7-f1_c_8       -27.29967895 -36.72395930 -17.87539860 0.0000000
## m248_s_8-f1_c_8       -22.18033786 -31.60461821 -12.75605751 0.0000000
## m248_s_9-f1_c_8       -13.21323451 -22.63751486  -3.78895416 0.0000548
## f1_s_5-f1_c_9         -57.83283225 -67.25711260 -48.40855190 0.0000000
## f1_s_6-f1_c_9         -55.44101787 -64.86529822 -46.01673752 0.0000000
## f1_s_7-f1_c_9         -47.43468749 -57.10379511 -37.76557986 0.0000000
## f1_s_8-f1_c_9         -34.55633503 -43.98061538 -25.13205468 0.0000000
## f1_s_9-f1_c_9         -15.55296208 -24.97724243  -6.12868173 0.0000003
## la1511_c_5-f1_c_9     -58.54763755 -67.97191790 -49.12335720 0.0000000
## la1511_c_6-f1_c_9     -57.05902266 -66.48330301 -47.63474231 0.0000000
## la1511_c_7-f1_c_9     -50.03396878 -59.45824913 -40.60968843 0.0000000
## la1511_c_8-f1_c_9     -40.44011902 -50.10922664 -30.77101140 0.0000000
## la1511_c_9-f1_c_9     -24.78966262 -34.21394297 -15.36538227 0.0000000
## la1511_s_5-f1_c_9     -58.34571067 -67.76999102 -48.92143032 0.0000000
## la1511_s_6-f1_c_9     -57.53383732 -67.20294494 -47.86472970 0.0000000
## la1511_s_7-f1_c_9     -53.50519764 -62.92947799 -44.08091729 0.0000000
## la1511_s_8-f1_c_9     -47.66995466 -57.09423501 -38.24567431 0.0000000
## la1511_s_9-f1_c_9     -42.98982660 -52.41410695 -33.56554625 0.0000000
## m058_c_5-f1_c_9       -58.33177589 -67.75605624 -48.90749554 0.0000000
## m058_c_6-f1_c_9       -55.98566149 -65.40994184 -46.56138114 0.0000000
## m058_c_7-f1_c_9       -52.12676174 -61.55104209 -42.70248139 0.0000000
## m058_c_8-f1_c_9       -47.63423438 -57.05851473 -38.20995403 0.0000000
## m058_c_9-f1_c_9       -39.21921185 -48.64349220 -29.79493150 0.0000000
## m058_s_5-f1_c_9       -56.85913067 -67.19584151 -46.52241982 0.0000000
## m058_s_6-f1_c_9       -55.76813788 -66.10484873 -45.43142704 0.0000000
## m058_s_7-f1_c_9       -54.13259622 -64.46930706 -43.79588537 0.0000000
## m058_s_8-f1_c_9       -51.90280883 -62.23951968 -41.56609799 0.0000000
## m058_s_9-f1_c_9       -48.04976836 -58.38647920 -37.71305751 0.0000000
## m248_c_5-f1_c_9       -55.37632235 -64.80060270 -45.95204200 0.0000000
## m248_c_6-f1_c_9       -54.94661178 -64.37089213 -45.52233143 0.0000000
## m248_c_7-f1_c_9       -47.37458328 -56.79886363 -37.95030293 0.0000000
## m248_c_8-f1_c_9       -35.13717456 -44.56145491 -25.71289421 0.0000000
## m248_c_9-f1_c_9       -14.89915974 -24.32344009  -5.47487939 0.0000012
## m248_s_5-f1_c_9       -57.90149411 -67.32577446 -48.47721376 0.0000000
## m248_s_6-f1_c_9       -56.39592785 -65.82020820 -46.97164750 0.0000000
## m248_s_7-f1_c_9       -53.40693524 -62.83121559 -43.98265489 0.0000000
## m248_s_8-f1_c_9       -48.28759415 -57.71187450 -38.86331380 0.0000000
## m248_s_9-f1_c_9       -39.32049080 -48.74477115 -29.89621045 0.0000000
## f1_s_6-f1_s_5           2.39181438  -6.78110653  11.56473528 1.0000000
## f1_s_7-f1_s_5          10.39814476   0.97386441  19.82242511 0.0114098
## f1_s_8-f1_s_5          23.27649722  14.10357631  32.44941813 0.0000000
## f1_s_9-f1_s_5          42.27987016  33.10694925  51.45279107 0.0000000
## la1511_c_5-f1_s_5      -0.71480531  -9.88772622   8.45811560 1.0000000
## la1511_c_6-f1_s_5       0.77380959  -8.39911132   9.94673050 1.0000000
## la1511_c_7-f1_s_5       7.79886347  -1.37405744  16.97178437 0.2691834
## la1511_c_8-f1_s_5      17.39271323   7.96843288  26.81699358 0.0000000
## la1511_c_9-f1_s_5      33.04316963  23.87024872  42.21609054 0.0000000
## la1511_s_5-f1_s_5      -0.51287843  -9.68579934   8.66004248 1.0000000
## la1511_s_6-f1_s_5       0.29899493  -9.12528542   9.72327528 1.0000000
## la1511_s_7-f1_s_5       4.32763461  -4.84528630  13.50055552 0.9987499
## la1511_s_8-f1_s_5      10.16287758   0.98995667  19.33579849 0.0106316
## la1511_s_9-f1_s_5      14.84300564   5.67008473  24.01592655 0.0000005
## m058_c_5-f1_s_5        -0.49894364  -9.67186455   8.67397727 1.0000000
## m058_c_6-f1_s_5         1.84717076  -7.32575015  11.02009167 1.0000000
## m058_c_7-f1_s_5         5.70607050  -3.46685041  14.87899141 0.9104438
## m058_c_8-f1_s_5        10.19859787   1.02567696  19.37151878 0.0100094
## m058_c_9-f1_s_5        18.61362039   9.44069948  27.78654130 0.0000000
## m058_s_5-f1_s_5         0.97370158  -9.13436495  11.08176811 1.0000000
## m058_s_6-f1_s_5         2.06469436  -8.04337217  12.17276090 1.0000000
## m058_s_7-f1_s_5         3.70023603  -6.40783050  13.80830256 0.9999962
## m058_s_8-f1_s_5         5.93002341  -4.17804312  16.03808994 0.9559460
## m058_s_9-f1_s_5         9.78306389  -0.32500264  19.89113042 0.0754231
## m248_c_5-f1_s_5         2.45650990  -6.71641101  11.62943081 1.0000000
## m248_c_6-f1_s_5         2.88622047  -6.28670044  12.05914138 0.9999999
## m248_c_7-f1_s_5        10.45824897   1.28532806  19.63116988 0.0064018
## m248_c_8-f1_s_5        22.69565769  13.52273678  31.86857859 0.0000000
## m248_c_9-f1_s_5        42.93367250  33.76075159  52.10659341 0.0000000
## m248_s_5-f1_s_5        -0.06866186  -9.24158277   9.10425905 1.0000000
## m248_s_6-f1_s_5         1.43690440  -7.73601651  10.60982531 1.0000000
## m248_s_7-f1_s_5         4.42589701  -4.74702390  13.59881792 0.9980743
## m248_s_8-f1_s_5         9.54523810   0.37231719  18.71815901 0.0287555
## m248_s_9-f1_s_5        18.51234144   9.33942053  27.68526235 0.0000000
## f1_s_7-f1_s_6           8.00633038  -1.41794997  17.43061073 0.2708103
## f1_s_8-f1_s_6          20.88468284  11.71176193  30.05760375 0.0000000
## f1_s_9-f1_s_6          39.88805579  30.71513488  49.06097670 0.0000000
## la1511_c_5-f1_s_6      -3.10661968 -12.27954059   6.06630122 0.9999995
## la1511_c_6-f1_s_6      -1.61800479 -10.79092570   7.55491612 1.0000000
## la1511_c_7-f1_s_6       5.40704909  -3.76587182  14.57997000 0.9531495
## la1511_c_8-f1_s_6      15.00089885   5.57661850  24.42517920 0.0000010
## la1511_c_9-f1_s_6      30.65135525  21.47843434  39.82427616 0.0000000
## la1511_s_5-f1_s_6      -2.90469280 -12.07761371   6.26822811 0.9999999
## la1511_s_6-f1_s_6      -2.09281945 -11.51709980   7.33146090 1.0000000
## la1511_s_7-f1_s_6       1.93582023  -7.23710068  11.10874114 1.0000000
## la1511_s_8-f1_s_6       7.77106321  -1.40185770  16.94398411 0.2766800
## la1511_s_9-f1_s_6      12.45119127   3.27827036  21.62411218 0.0001326
## m058_c_5-f1_s_6        -2.89075802 -12.06367893   6.28216289 0.9999999
## m058_c_6-f1_s_6        -0.54464362  -9.71756453   8.62827729 1.0000000
## m058_c_7-f1_s_6         3.31425613  -5.85866478  12.48717704 0.9999973
## m058_c_8-f1_s_6         7.80678349  -1.36613742  16.97970440 0.2670711
## m058_c_9-f1_s_6        16.22180602   7.04888511  25.39472693 0.0000000
## m058_s_5-f1_s_6        -1.41811280 -11.52617933   8.68995374 1.0000000
## m058_s_6-f1_s_6        -0.32712001 -10.43518654   9.78094652 1.0000000
## m058_s_7-f1_s_6         1.30842165  -8.79964488  11.41648818 1.0000000
## m058_s_8-f1_s_6         3.53820904  -6.56985750  13.64627557 0.9999988
## m058_s_9-f1_s_6         7.39124951  -2.71681702  17.49931605 0.6308273
## m248_c_5-f1_s_6         0.06469552  -9.10822538   9.23761643 1.0000000
## m248_c_6-f1_s_6         0.49440609  -8.67851482   9.66732700 1.0000000
## m248_c_7-f1_s_6         8.06643459  -1.10648632  17.23935550 0.2036956
## m248_c_8-f1_s_6        20.30384331  11.13092240  29.47676422 0.0000000
## m248_c_9-f1_s_6        40.54185813  31.36893722  49.71477904 0.0000000
## m248_s_5-f1_s_6        -2.46047624 -11.63339715   6.71244467 1.0000000
## m248_s_6-f1_s_6        -0.95490998 -10.12783088   8.21801093 1.0000000
## m248_s_7-f1_s_6         2.03408263  -7.13883828  11.20700354 1.0000000
## m248_s_8-f1_s_6         7.15342372  -2.01949719  16.32634463 0.4718555
## m248_s_9-f1_s_6        16.12052707   6.94760616  25.29344798 0.0000000
## f1_s_8-f1_s_7          12.87835246   3.45407211  22.30263281 0.0001110
## f1_s_9-f1_s_7          31.88172540  22.45744505  41.30600575 0.0000000
## la1511_c_5-f1_s_7     -11.11295007 -20.53723042  -1.68866972 0.0033735
## la1511_c_6-f1_s_7      -9.62433517 -19.04861552  -0.20005482 0.0376116
## la1511_c_7-f1_s_7      -2.59928129 -12.02356164   6.82499906 1.0000000
## la1511_c_8-f1_s_7       6.99456847  -2.67453916  16.66367609 0.6560099
## la1511_c_9-f1_s_7      22.64502487  13.22074452  32.06930522 0.0000000
## la1511_s_5-f1_s_7     -10.91102319 -20.33530354  -1.48674284 0.0048100
## la1511_s_6-f1_s_7     -10.09914983 -19.76825746  -0.43004221 0.0272197
## la1511_s_7-f1_s_7      -6.07051015 -15.49479050   3.35377020 0.8700994
## la1511_s_8-f1_s_7      -0.23526718  -9.65954753   9.18901317 1.0000000
## la1511_s_9-f1_s_7       4.44486088  -4.97941947  13.86914123 0.9987573
## m058_c_5-f1_s_7       -10.89708840 -20.32136875  -1.47280805 0.0049278
## m058_c_6-f1_s_7        -8.55097400 -17.97525435   0.87330635 0.1522970
## m058_c_7-f1_s_7        -4.69207426 -14.11635461   4.73220609 0.9965629
## m058_c_8-f1_s_7        -0.19954689  -9.62382724   9.22473346 1.0000000
## m058_c_9-f1_s_7         8.21547563  -1.20880472  17.63975598 0.2196684
## m058_s_5-f1_s_7        -9.42444318 -19.76115403   0.91226767 0.1451740
## m058_s_6-f1_s_7        -8.33345040 -18.67016124   2.00326045 0.3897470
## m058_s_7-f1_s_7        -6.69790873 -17.03461958   3.63880211 0.8621198
## m058_s_8-f1_s_7        -4.46812135 -14.80483219   5.86858950 0.9997968
## m058_s_9-f1_s_7        -0.61508087 -10.95179172   9.72162998 1.0000000
## m248_c_5-f1_s_7        -7.94163486 -17.36591521   1.48264549 0.2880357
## m248_c_6-f1_s_7        -7.51192429 -16.93620464   1.91235606 0.4174935
## m248_c_7-f1_s_7         0.06010421  -9.36417614   9.48438456 1.0000000
## m248_c_8-f1_s_7        12.29751293   2.87323258  21.72179328 0.0003617
## m248_c_9-f1_s_7        32.53552774  23.11124739  41.95980809 0.0000000
## m248_s_5-f1_s_7       -10.46680662 -19.89108697  -1.04252627 0.0101966
## m248_s_6-f1_s_7        -8.96124036 -18.38552071   0.46303999 0.0927545
## m248_s_7-f1_s_7        -5.97224775 -15.39652810   3.45203260 0.8903356
## m248_s_8-f1_s_7        -0.85290666 -10.27718701   8.57137369 1.0000000
## m248_s_9-f1_s_7         8.11419668  -1.31008367  17.53847703 0.2435568
## f1_s_9-f1_s_8          19.00337295   9.83045204  28.17629386 0.0000000
## la1511_c_5-f1_s_8     -23.99130253 -33.16422344 -14.81838162 0.0000000
## la1511_c_6-f1_s_8     -22.50268763 -31.67560854 -13.32976672 0.0000000
## la1511_c_7-f1_s_8     -15.47763375 -24.65055466  -6.30471284 0.0000001
## la1511_c_8-f1_s_8      -5.88378399 -15.30806434   3.54049636 0.9067404
## la1511_c_9-f1_s_8       9.76667241   0.59375150  18.93959332 0.0203436
## la1511_s_5-f1_s_8     -23.78937564 -32.96229655 -14.61645474 0.0000000
## la1511_s_6-f1_s_8     -22.97750229 -32.40178264 -13.55322194 0.0000000
## la1511_s_7-f1_s_8     -18.94886261 -28.12178352  -9.77594170 0.0000000
## la1511_s_8-f1_s_8     -13.11361964 -22.28654055  -3.94069873 0.0000314
## la1511_s_9-f1_s_8      -8.43349157 -17.60641248   0.73942933 0.1334370
## m058_c_5-f1_s_8       -23.77544086 -32.94836177 -14.60251995 0.0000000
## m058_c_6-f1_s_8       -21.42932646 -30.60224737 -12.25640555 0.0000000
## m058_c_7-f1_s_8       -17.57042671 -26.74334762  -8.39750581 0.0000000
## m058_c_8-f1_s_8       -13.07789935 -22.25082026  -3.90497844 0.0000340
## m058_c_9-f1_s_8        -4.66287682 -13.83579773   4.51004409 0.9950330
## m058_s_5-f1_s_8       -22.30279564 -32.41086217 -12.19472911 0.0000000
## m058_s_6-f1_s_8       -21.21180285 -31.31986939 -11.10373632 0.0000000
## m058_s_7-f1_s_8       -19.57626119 -29.68432772  -9.46819466 0.0000000
## m058_s_8-f1_s_8       -17.34647381 -27.45454034  -7.23840727 0.0000001
## m058_s_9-f1_s_8       -13.49343333 -23.60149986  -3.38536680 0.0002047
## m248_c_5-f1_s_8       -20.81998732 -29.99290823 -11.64706641 0.0000000
## m248_c_6-f1_s_8       -20.39027675 -29.56319766 -11.21735584 0.0000000
## m248_c_7-f1_s_8       -12.81824825 -21.99116916  -3.64532734 0.0000602
## m248_c_8-f1_s_8        -0.58083953  -9.75376044   8.59208138 1.0000000
## m248_c_9-f1_s_8        19.65717529  10.48425438  28.83009619 0.0000000
## m248_s_5-f1_s_8       -23.34515908 -32.51807999 -14.17223817 0.0000000
## m248_s_6-f1_s_8       -21.83959282 -31.01251373 -12.66667191 0.0000000
## m248_s_7-f1_s_8       -18.85060021 -28.02352112  -9.67767930 0.0000000
## m248_s_8-f1_s_8       -13.73125912 -22.90418003  -4.55833821 0.0000077
## m248_s_9-f1_s_8        -4.76415577 -13.93707668   4.40876514 0.9928304
## la1511_c_5-f1_s_9     -42.99467547 -52.16759638 -33.82175456 0.0000000
## la1511_c_6-f1_s_9     -41.50606057 -50.67898148 -32.33313967 0.0000000
## la1511_c_7-f1_s_9     -34.48100670 -43.65392761 -25.30808579 0.0000000
## la1511_c_8-f1_s_9     -24.88715694 -34.31143729 -15.46287659 0.0000000
## la1511_c_9-f1_s_9      -9.23670054 -18.40962145  -0.06377963 0.0455998
## la1511_s_5-f1_s_9     -42.79274859 -51.96566950 -33.61982768 0.0000000
## la1511_s_6-f1_s_9     -41.98087524 -51.40515559 -32.55659489 0.0000000
## la1511_s_7-f1_s_9     -37.95223556 -47.12515647 -28.77931465 0.0000000
## la1511_s_8-f1_s_9     -32.11699258 -41.28991349 -22.94407167 0.0000000
## la1511_s_9-f1_s_9     -27.43686452 -36.60978543 -18.26394361 0.0000000
## m058_c_5-f1_s_9       -42.77881380 -51.95173471 -33.60589290 0.0000000
## m058_c_6-f1_s_9       -40.43269941 -49.60562031 -31.25977850 0.0000000
## m058_c_7-f1_s_9       -36.57379966 -45.74672057 -27.40087875 0.0000000
## m058_c_8-f1_s_9       -32.08127230 -41.25419321 -22.90835139 0.0000000
## m058_c_9-f1_s_9       -23.66624977 -32.83917068 -14.49332886 0.0000000
## m058_s_5-f1_s_9       -41.30616858 -51.41423512 -31.19810205 0.0000000
## m058_s_6-f1_s_9       -40.21517580 -50.32324233 -30.10710927 0.0000000
## m058_s_7-f1_s_9       -38.57963414 -48.68770067 -28.47156761 0.0000000
## m058_s_8-f1_s_9       -36.34984675 -46.45791328 -26.24178022 0.0000000
## m058_s_9-f1_s_9       -32.49680627 -42.60487281 -22.38873974 0.0000000
## m248_c_5-f1_s_9       -39.82336026 -48.99628117 -30.65043935 0.0000000
## m248_c_6-f1_s_9       -39.39364970 -48.56657060 -30.22072879 0.0000000
## m248_c_7-f1_s_9       -31.82162120 -40.99454211 -22.64870029 0.0000000
## m248_c_8-f1_s_9       -19.58421248 -28.75713339 -10.41129157 0.0000000
## m248_c_9-f1_s_9         0.65380234  -8.51911857   9.82672325 1.0000000
## m248_s_5-f1_s_9       -42.34853203 -51.52145293 -33.17561112 0.0000000
## m248_s_6-f1_s_9       -40.84296576 -50.01588667 -31.67004485 0.0000000
## m248_s_7-f1_s_9       -37.85397316 -47.02689407 -28.68105225 0.0000000
## m248_s_8-f1_s_9       -32.73463206 -41.90755297 -23.56171116 0.0000000
## m248_s_9-f1_s_9       -23.76752872 -32.94044963 -14.59460781 0.0000000
## la1511_c_6-la1511_c_5   1.48861490  -7.68430601  10.66153581 1.0000000
## la1511_c_7-la1511_c_5   8.51366877  -0.65925213  17.68658968 0.1209393
## la1511_c_8-la1511_c_5  18.10751853   8.68323818  27.53179888 0.0000000
## la1511_c_9-la1511_c_5  33.75797494  24.58505403  42.93089584 0.0000000
## la1511_s_5-la1511_c_5   0.20192688  -8.97099403   9.37484779 1.0000000
## la1511_s_6-la1511_c_5   1.01380024  -8.41048011  10.43808059 1.0000000
## la1511_s_7-la1511_c_5   5.04243992  -4.13048099  14.21536083 0.9823176
## la1511_s_8-la1511_c_5  10.87768289   1.70476198  20.05060380 0.0030162
## la1511_s_9-la1511_c_5  15.55781095   6.38489004  24.73073186 0.0000001
## m058_c_5-la1511_c_5     0.21586167  -8.95705924   9.38878258 1.0000000
## m058_c_6-la1511_c_5     2.56197607  -6.61094484  11.73489698 1.0000000
## m058_c_7-la1511_c_5     6.42087581  -2.75204510  15.59379672 0.7283386
## m058_c_8-la1511_c_5    10.91340318   1.74048227  20.08632409 0.0028242
## m058_c_9-la1511_c_5    19.32842570  10.15550479  28.50134661 0.0000000
## m058_s_5-la1511_c_5     1.68850689  -8.41955964  11.79657342 1.0000000
## m058_s_6-la1511_c_5     2.77949967  -7.32856686  12.88756620 1.0000000
## m058_s_7-la1511_c_5     4.41504134  -5.69302520  14.52310787 0.9997450
## m058_s_8-la1511_c_5     6.64482872  -3.46323781  16.75289525 0.8413747
## m058_s_9-la1511_c_5    10.49786920   0.38980267  20.60593573 0.0295859
## m248_c_5-la1511_c_5     3.17131521  -6.00160570  12.34423612 0.9999992
## m248_c_6-la1511_c_5     3.60102578  -5.57189513  12.77394669 0.9999781
## m248_c_7-la1511_c_5    11.17305428   2.00013337  20.34597519 0.0017376
## m248_c_8-la1511_c_5    23.41046299  14.23754209  32.58338390 0.0000000
## m248_c_9-la1511_c_5    43.64847781  34.47555690  52.82139872 0.0000000
## m248_s_5-la1511_c_5     0.64614345  -8.52677746   9.81906436 1.0000000
## m248_s_6-la1511_c_5     2.15170971  -7.02121120  11.32463062 1.0000000
## m248_s_7-la1511_c_5     5.14070232  -4.03221859  14.31362323 0.9764996
## m248_s_8-la1511_c_5    10.26004341   1.08712250  19.43296432 0.0090170
## m248_s_9-la1511_c_5    19.22714675  10.05422584  28.40006766 0.0000000
## la1511_c_7-la1511_c_6   7.02505388  -2.14786703  16.19797479 0.5172837
## la1511_c_8-la1511_c_6  16.61890364   7.19462329  26.04318399 0.0000000
## la1511_c_9-la1511_c_6  32.26936004  23.09643913  41.44228095 0.0000000
## la1511_s_5-la1511_c_6  -1.28668802 -10.45960892   7.88623289 1.0000000
## la1511_s_6-la1511_c_6  -0.47481466  -9.89909501   8.94946569 1.0000000
## la1511_s_7-la1511_c_6   3.55382502  -5.61909589  12.72674593 0.9999842
## la1511_s_8-la1511_c_6   9.38906799   0.21614708  18.56198890 0.0364287
## la1511_s_9-la1511_c_6  14.06919605   4.89627514  23.24211696 0.0000035
## m058_c_5-la1511_c_6    -1.27275323 -10.44567414   7.90016768 1.0000000
## m058_c_6-la1511_c_6     1.07336117  -8.09955974  10.24628208 1.0000000
## m058_c_7-la1511_c_6     4.93226091  -4.24065999  14.10518182 0.9874099
## m058_c_8-la1511_c_6     9.42478828   0.25186737  18.59770919 0.0345297
## m058_c_9-la1511_c_6    17.83981080   8.66688990  27.01273171 0.0000000
## m058_s_5-la1511_c_6     0.19989199  -9.90817454  10.30795852 1.0000000
## m058_s_6-la1511_c_6     1.29088477  -8.81718176  11.39895131 1.0000000
## m058_s_7-la1511_c_6     2.92642644  -7.18164009  13.03449297 1.0000000
## m058_s_8-la1511_c_6     5.15621382  -4.95185271  15.26428035 0.9947228
## m058_s_9-la1511_c_6     9.00925430  -1.09881223  19.11732083 0.1804434
## m248_c_5-la1511_c_6     1.68270031  -7.49022060  10.85562122 1.0000000
## m248_c_6-la1511_c_6     2.11241088  -7.06051003  11.28533179 1.0000000
## m248_c_7-la1511_c_6     9.68443938   0.51151847  18.85736029 0.0231667
## m248_c_8-la1511_c_6    21.92184810  12.74892719  31.09476900 0.0000000
## m248_c_9-la1511_c_6    42.15986291  32.98694201  51.33278382 0.0000000
## m248_s_5-la1511_c_6    -0.84247145 -10.01539236   8.33044946 1.0000000
## m248_s_6-la1511_c_6     0.66309481  -8.50982610   9.83601572 1.0000000
## m248_s_7-la1511_c_6     3.65208742  -5.52083349  12.82500833 0.9999693
## m248_s_8-la1511_c_6     8.77142851  -0.40149240  17.94434942 0.0869566
## m248_s_9-la1511_c_6    17.73853185   8.56561095  26.91145276 0.0000000
## la1511_c_8-la1511_c_7   9.59384976   0.16956941  19.01813011 0.0393053
## la1511_c_9-la1511_c_7  25.24430616  16.07138525  34.41722707 0.0000000
## la1511_s_5-la1511_c_7  -8.31174189 -17.48466280   0.86117902 0.1543088
## la1511_s_6-la1511_c_7  -7.49986854 -16.92414889   1.92441181 0.4214476
## la1511_s_7-la1511_c_7  -3.47122886 -12.64414977   5.70169205 0.9999912
## la1511_s_8-la1511_c_7   2.36401412  -6.80890679  11.53693502 1.0000000
## la1511_s_9-la1511_c_7   7.04414218  -2.12877873  16.21706309 0.5104783
## m058_c_5-la1511_c_7    -8.29780711 -17.47072802   0.87511380 0.1568478
## m058_c_6-la1511_c_7    -5.95169271 -15.12461362   3.22122820 0.8602928
## m058_c_7-la1511_c_7    -2.09279296 -11.26571387   7.08012795 1.0000000
## m058_c_8-la1511_c_7     2.39973440  -6.77318651  11.57265531 1.0000000
## m058_c_9-la1511_c_7    10.81475693   1.64183602  19.98767784 0.0033846
## m058_s_5-la1511_c_7    -6.82516189 -16.93322842   3.28290465 0.7975902
## m058_s_6-la1511_c_7    -5.73416910 -15.84223563   4.37389743 0.9720462
## m058_s_7-la1511_c_7    -4.09862744 -14.20669397   6.00943909 0.9999526
## m058_s_8-la1511_c_7    -1.86884005 -11.97690659   8.23922648 1.0000000
## m058_s_9-la1511_c_7     1.98420042  -8.12386611  12.09226696 1.0000000
## m248_c_5-la1511_c_7    -5.34235357 -14.51527447   3.83056734 0.9599655
## m248_c_6-la1511_c_7    -4.91264300 -14.08556391   4.26027791 0.9881769
## m248_c_7-la1511_c_7     2.65938550  -6.51353541  11.83230641 1.0000000
## m248_c_8-la1511_c_7    14.89679422   5.72387331  24.06971513 0.0000005
## m248_c_9-la1511_c_7    35.13480904  25.96188813  44.30772995 0.0000000
## m248_s_5-la1511_c_7    -7.86752533 -17.04044624   1.30539558 0.2512199
## m248_s_6-la1511_c_7    -6.36195907 -15.53487997   2.81096184 0.7471342
## m248_s_7-la1511_c_7    -3.37296646 -12.54588737   5.79995445 0.9999958
## m248_s_8-la1511_c_7     1.74637463  -7.42654628  10.91929554 1.0000000
## m248_s_9-la1511_c_7    10.71347798   1.54055707  19.88639889 0.0040674
## la1511_c_9-la1511_c_8  15.65045640   6.22617605  25.07473675 0.0000002
## la1511_s_5-la1511_c_8 -17.90559165 -27.32987200  -8.48131130 0.0000000
## la1511_s_6-la1511_c_8 -17.09371830 -26.76282592  -7.42461068 0.0000000
## la1511_s_7-la1511_c_8 -13.06507862 -22.48935897  -3.64079827 0.0000751
## la1511_s_8-la1511_c_8  -7.22983564 -16.65411599   2.19444471 0.5130212
## la1511_s_9-la1511_c_8  -2.54970758 -11.97398793   6.87457277 1.0000000
## m058_c_5-la1511_c_8   -17.89165687 -27.31593722  -8.46737652 0.0000000
## m058_c_6-la1511_c_8   -15.54554247 -24.96982282  -6.12126212 0.0000003
## m058_c_7-la1511_c_8   -11.68664272 -21.11092307  -2.26236237 0.0011803
## m058_c_8-la1511_c_8    -7.19411536 -16.61839571   2.23016499 0.5254345
## m058_c_9-la1511_c_8     1.22090717  -8.20337318  10.64518752 1.0000000
## m058_s_5-la1511_c_8   -16.41901165 -26.75572249  -6.08230080 0.0000010
## m058_s_6-la1511_c_8   -15.32801886 -25.66472971  -4.99130802 0.0000104
## m058_s_7-la1511_c_8   -13.69247720 -24.02918804  -3.35576635 0.0002491
## m058_s_8-la1511_c_8   -11.46268981 -21.79940066  -1.12597897 0.0104671
## m058_s_9-la1511_c_8    -7.60964934 -17.94636018   2.72706151 0.6147247
## m248_c_5-la1511_c_8   -14.93620333 -24.36048368  -5.51192298 0.0000011
## m248_c_6-la1511_c_8   -14.50649276 -23.93077311  -5.08221241 0.0000031
## m248_c_7-la1511_c_8    -6.93446426 -16.35874461   2.48981609 0.6159267
## m248_c_8-la1511_c_8     5.30294446  -4.12133589  14.72722481 0.9750944
## m248_c_9-la1511_c_8    25.54095928  16.11667893  34.96523963 0.0000000
## m248_s_5-la1511_c_8   -17.46137509 -26.88565544  -8.03709474 0.0000000
## m248_s_6-la1511_c_8   -15.95580883 -25.38008918  -6.53152848 0.0000001
## m248_s_7-la1511_c_8   -12.96681622 -22.39109657  -3.54253587 0.0000923
## m248_s_8-la1511_c_8    -7.84747513 -17.27175548   1.57680522 0.3142469
## m248_s_9-la1511_c_8     1.11962822  -8.30465213  10.54390857 1.0000000
## la1511_s_5-la1511_c_9 -33.55604805 -42.72896896 -24.38312714 0.0000000
## la1511_s_6-la1511_c_9 -32.74417470 -42.16845505 -23.31989435 0.0000000
## la1511_s_7-la1511_c_9 -28.71553502 -37.88845593 -19.54261411 0.0000000
## la1511_s_8-la1511_c_9 -22.88029205 -32.05321295 -13.70737114 0.0000000
## la1511_s_9-la1511_c_9 -18.20016398 -27.37308489  -9.02724307 0.0000000
## m058_c_5-la1511_c_9   -33.54211327 -42.71503418 -24.36919236 0.0000000
## m058_c_6-la1511_c_9   -31.19599887 -40.36891978 -22.02307796 0.0000000
## m058_c_7-la1511_c_9   -27.33709912 -36.51002003 -18.16417821 0.0000000
## m058_c_8-la1511_c_9   -22.84457176 -32.01749267 -13.67165085 0.0000000
## m058_c_9-la1511_c_9   -14.42954923 -23.60247014  -5.25662832 0.0000015
## m058_s_5-la1511_c_9   -32.06946805 -42.17753458 -21.96140152 0.0000000
## m058_s_6-la1511_c_9   -30.97847526 -41.08654179 -20.87040873 0.0000000
## m058_s_7-la1511_c_9   -29.34293360 -39.45100013 -19.23486707 0.0000000
## m058_s_8-la1511_c_9   -27.11314622 -37.22121275 -17.00507968 0.0000000
## m058_s_9-la1511_c_9   -23.26010574 -33.36817227 -13.15203921 0.0000000
## m248_c_5-la1511_c_9   -30.58665973 -39.75958064 -21.41373882 0.0000000
## m248_c_6-la1511_c_9   -30.15694916 -39.32987007 -20.98402825 0.0000000
## m248_c_7-la1511_c_9   -22.58492066 -31.75784157 -13.41199975 0.0000000
## m248_c_8-la1511_c_9   -10.34751194 -19.52043285  -1.17459103 0.0077603
## m248_c_9-la1511_c_9     9.89050288   0.71758197  19.06342379 0.0166758
## m248_s_5-la1511_c_9   -33.11183149 -42.28475240 -23.93891058 0.0000000
## m248_s_6-la1511_c_9   -31.60626523 -40.77918614 -22.43334432 0.0000000
## m248_s_7-la1511_c_9   -28.61727262 -37.79019353 -19.44435171 0.0000000
## m248_s_8-la1511_c_9   -23.49793153 -32.67085244 -14.32501062 0.0000000
## m248_s_9-la1511_c_9   -14.53082818 -23.70374909  -5.35790727 0.0000012
## la1511_s_6-la1511_s_5   0.81187335  -8.61240700  10.23615370 1.0000000
## la1511_s_7-la1511_s_5   4.84051303  -4.33240788  14.01343394 0.9906764
## la1511_s_8-la1511_s_5  10.67575601   1.50283510  19.84867692 0.0043532
## la1511_s_9-la1511_s_5  15.35588407   6.18296316  24.52880498 0.0000001
## m058_c_5-la1511_s_5     0.01393479  -9.15898612   9.18685569 1.0000000
## m058_c_6-la1511_s_5     2.36004918  -6.81287172  11.53297009 1.0000000
## m058_c_7-la1511_s_5     6.21894893  -2.95397198  15.39186984 0.7903579
## m058_c_8-la1511_s_5    10.71147629   1.53855538  19.88439720 0.0040821
## m058_c_9-la1511_s_5    19.12649882   9.95357791  28.29941973 0.0000000
## m058_s_5-la1511_s_5     1.48658001  -8.62148653  11.59464654 1.0000000
## m058_s_6-la1511_s_5     2.57757279  -7.53049374  12.68563932 1.0000000
## m058_s_7-la1511_s_5     4.21311445  -5.89495208  14.32118099 0.9999101
## m058_s_8-la1511_s_5     6.44290184  -3.66516469  16.55096837 0.8834097
## m058_s_9-la1511_s_5    10.29594232   0.18787578  20.40400885 0.0389894
## m248_c_5-la1511_s_5     2.96938833  -6.20353258  12.14230924 0.9999999
## m248_c_6-la1511_s_5     3.39909890  -5.77382201  12.57201980 0.9999948
## m248_c_7-la1511_s_5    10.97112739   1.79820648  20.14404830 0.0025380
## m248_c_8-la1511_s_5    23.20853611  14.03561520  32.38145702 0.0000000
## m248_c_9-la1511_s_5    43.44655093  34.27363002  52.61947184 0.0000000
## m248_s_5-la1511_s_5     0.44421656  -8.72870434   9.61713747 1.0000000
## m248_s_6-la1511_s_5     1.94978283  -7.22313808  11.12270374 1.0000000
## m248_s_7-la1511_s_5     4.93877543  -4.23414548  14.11169634 0.9871464
## m248_s_8-la1511_s_5    10.05811653   0.88519562  19.23103743 0.0126671
## m248_s_9-la1511_s_5    19.02521987   9.85229896  28.19814078 0.0000000
## la1511_s_7-la1511_s_6   4.02863968  -5.39564067  13.45292003 0.9998411
## la1511_s_8-la1511_s_6   9.86388265   0.43960230  19.28816300 0.0263946
## la1511_s_9-la1511_s_6  14.54401072   5.11973037  23.96829107 0.0000028
## m058_c_5-la1511_s_6    -0.79793857 -10.22221892   8.62634178 1.0000000
## m058_c_6-la1511_s_6     1.54817583  -7.87610452  10.97245618 1.0000000
## m058_c_7-la1511_s_6     5.40707558  -4.01720477  14.83135593 0.9672914
## m058_c_8-la1511_s_6     9.89960294   0.47532259  19.32388329 0.0250069
## m058_c_9-la1511_s_6    18.31462547   8.89034512  27.73890582 0.0000000
## m058_s_5-la1511_s_6     0.67470665  -9.66200419  11.01141750 1.0000000
## m058_s_6-la1511_s_6     1.76569944  -8.57101141  12.10241028 1.0000000
## m058_s_7-la1511_s_6     3.40124110  -6.93546975  13.73795195 0.9999998
## m058_s_8-la1511_s_6     5.63102848  -4.70568236  15.96773933 0.9845852
## m058_s_9-la1511_s_6     9.48406896  -0.85264188  19.82077981 0.1362475
## m248_c_5-la1511_s_6     2.15751497  -7.26676538  11.58179532 1.0000000
## m248_c_6-la1511_s_6     2.58722554  -6.83705481  12.01150589 1.0000000
## m248_c_7-la1511_s_6    10.15925404   0.73497369  19.58353439 0.0167356
## m248_c_8-la1511_s_6    22.39666276  12.97238241  31.82094311 0.0000000
## m248_c_9-la1511_s_6    42.63467758  33.21039723  52.05895793 0.0000000
## m248_s_5-la1511_s_6    -0.36765679  -9.79193714   9.05662356 1.0000000
## m248_s_6-la1511_s_6     1.13790947  -8.28637088  10.56218982 1.0000000
## m248_s_7-la1511_s_6     4.12690208  -5.29737827  13.55118243 0.9997305
## m248_s_8-la1511_s_6     9.24624317  -0.17803718  18.67052352 0.0638526
## m248_s_9-la1511_s_6    18.21334652   8.78906617  27.63762687 0.0000000
## la1511_s_8-la1511_s_7   5.83524297  -3.33767794  15.00816388 0.8858101
## la1511_s_9-la1511_s_7  10.51537104   1.34245013  19.68829194 0.0057908
## m058_c_5-la1511_s_7    -4.82657825 -13.99949916   4.34634266 0.9911051
## m058_c_6-la1511_s_7    -2.48046385 -11.65338476   6.69245706 1.0000000
## m058_c_7-la1511_s_7     1.37843590  -7.79448501  10.55135681 1.0000000
## m058_c_8-la1511_s_7     5.87096326  -3.30195765  15.04388417 0.8783155
## m058_c_9-la1511_s_7    14.28598579   5.11306488  23.45890670 0.0000021
## m058_s_5-la1511_s_7    -3.35393303 -13.46199956   6.75413350 0.9999997
## m058_s_6-la1511_s_7    -2.26294024 -12.37100677   7.84512629 1.0000000
## m058_s_7-la1511_s_7    -0.62739858 -10.73546511   9.48066795 1.0000000
## m058_s_8-la1511_s_7     1.60238880  -8.50567773  11.71045534 1.0000000
## m058_s_9-la1511_s_7     5.45542928  -4.65263725  15.56349581 0.9866516
## m248_c_5-la1511_s_7    -1.87112471 -11.04404562   7.30179620 1.0000000
## m248_c_6-la1511_s_7    -1.44141414 -10.61433505   7.73150677 1.0000000
## m248_c_7-la1511_s_7     6.13061436  -3.04230655  15.30353527 0.8151403
## m248_c_8-la1511_s_7    18.36802308   9.19510217  27.54094399 0.0000000
## m248_c_9-la1511_s_7    38.60603790  29.43311699  47.77895881 0.0000000
## m248_s_5-la1511_s_7    -4.39629647 -13.56921738   4.77662444 0.9983050
## m248_s_6-la1511_s_7    -2.89073021 -12.06365112   6.28219070 0.9999999
## m248_s_7-la1511_s_7     0.09826240  -9.07465851   9.27118331 1.0000000
## m248_s_8-la1511_s_7     5.21760349  -3.95531742  14.39052440 0.9709784
## m248_s_9-la1511_s_7    14.18470684   5.01178593  23.35762775 0.0000027
## la1511_s_9-la1511_s_8   4.68012806  -4.49279285  13.85304897 0.9947044
## m058_c_5-la1511_s_8   -10.66182122 -19.83474213  -1.48890031 0.0044634
## m058_c_6-la1511_s_8    -8.31570682 -17.48862773   0.85721409 0.1535920
## m058_c_7-la1511_s_8    -4.45680708 -13.62972799   4.71611383 0.9978048
## m058_c_8-la1511_s_8     0.03572029  -9.13720062   9.20864120 1.0000000
## m058_c_9-la1511_s_8     8.45074281  -0.72217810  17.62366372 0.1306661
## m058_s_5-la1511_s_8    -9.18917600 -19.29724253   0.91889053 0.1494250
## m058_s_6-la1511_s_8    -8.09818322 -18.20624975   2.00988331 0.4049845
## m058_s_7-la1511_s_8    -6.46264155 -16.57070809   3.64542498 0.8796401
## m058_s_8-la1511_s_8    -4.23285417 -14.34092070   5.87521236 0.9999000
## m058_s_9-la1511_s_8    -0.37981369 -10.48788022   9.72825284 1.0000000
## m248_c_5-la1511_s_8    -7.70636768 -16.87928859   1.46655323 0.2946159
## m248_c_6-la1511_s_8    -7.27665711 -16.44957802   1.89626380 0.4292995
## m248_c_7-la1511_s_8     0.29537139  -8.87754952   9.46829230 1.0000000
## m248_c_8-la1511_s_8    12.53278010   3.35985920  21.70570101 0.0001115
## m248_c_9-la1511_s_8    32.77079492  23.59787401  41.94371583 0.0000000
## m248_s_5-la1511_s_8   -10.23153944 -19.40446035  -1.05861853 0.0094655
## m248_s_6-la1511_s_8    -8.72597318 -17.89889409   0.44694773 0.0923025
## m248_s_7-la1511_s_8    -5.73698057 -14.90990148   3.43594034 0.9049002
## m248_s_8-la1511_s_8    -0.61763948  -9.79056039   8.55528143 1.0000000
## m248_s_9-la1511_s_8     8.34946386  -0.82345705  17.52238477 0.1475919
## m058_c_5-la1511_s_9   -15.34194928 -24.51487019  -6.16902837 0.0000002
## m058_c_6-la1511_s_9   -12.99583488 -22.16875579  -3.82291398 0.0000407
## m058_c_7-la1511_s_9    -9.13693514 -18.30985605   0.03598577 0.0526414
## m058_c_8-la1511_s_9    -4.64440778 -13.81732868   4.52851313 0.9953655
## m058_c_9-la1511_s_9     3.77061475  -5.40230616  12.94353566 0.9999347
## m058_s_5-la1511_s_9   -13.86930406 -23.97737059  -3.76123753 0.0000994
## m058_s_6-la1511_s_9   -12.77831128 -22.88637781  -2.67024475 0.0007659
## m058_s_7-la1511_s_9   -11.14276962 -21.25083615  -1.03470308 0.0115813
## m058_s_8-la1511_s_9    -8.91298223 -19.02104876   1.19508430 0.1988615
## m058_s_9-la1511_s_9    -5.05994175 -15.16800828   5.04812478 0.9962109
## m248_c_5-la1511_s_9   -12.38649574 -21.55941665  -3.21357483 0.0001521
## m248_c_6-la1511_s_9   -11.95678517 -21.12970608  -2.78386427 0.0003712
## m248_c_7-la1511_s_9    -4.38475668 -13.55767758   4.78816423 0.9983882
## m248_c_8-la1511_s_9     7.85265204  -1.32026887  17.02557295 0.2550439
## m248_c_9-la1511_s_9    28.09066686  18.91774595  37.26358777 0.0000000
## m248_s_5-la1511_s_9   -14.91166750 -24.08458841  -5.73874660 0.0000005
## m248_s_6-la1511_s_9   -13.40610124 -22.57902215  -4.23318033 0.0000162
## m248_s_7-la1511_s_9   -10.41710864 -19.59002955  -1.24418773 0.0068784
## m248_s_8-la1511_s_9    -5.29776754 -14.47068845   3.87515337 0.9642117
## m248_s_9-la1511_s_9     3.66933580  -5.50358511  12.84225671 0.9999656
## m058_c_6-m058_c_5       2.34611440  -6.82680651  11.51903531 1.0000000
## m058_c_7-m058_c_5       6.20501414  -2.96790676  15.37793505 0.7943699
## m058_c_8-m058_c_5      10.69754151   1.52462060  19.87046242 0.0041859
## m058_c_9-m058_c_5      19.11256403   9.93964313  28.28548494 0.0000000
## m058_s_5-m058_c_5       1.47264522  -8.63542131  11.58071175 1.0000000
## m058_s_6-m058_c_5       2.56363800  -7.54442853  12.67170454 1.0000000
## m058_s_7-m058_c_5       4.19917967  -5.90888686  14.30724620 0.9999167
## m058_s_8-m058_c_5       6.42896705  -3.67909948  16.53703358 0.8860260
## m058_s_9-m058_c_5      10.28200753   0.17394100  20.39007406 0.0397261
## m248_c_5-m058_c_5       2.95545354  -6.21746737  12.12837445 0.9999999
## m248_c_6-m058_c_5       3.38516411  -5.78775680  12.55808502 0.9999953
## m248_c_7-m058_c_5      10.95719261   1.78427170  20.13011352 0.0026045
## m248_c_8-m058_c_5      23.19460133  14.02168042  32.36752223 0.0000000
## m248_c_9-m058_c_5      43.43261614  34.25969524  52.60553705 0.0000000
## m248_s_5-m058_c_5       0.43028178  -8.74263913   9.60320269 1.0000000
## m248_s_6-m058_c_5       1.93584804  -7.23707287  11.10876895 1.0000000
## m248_s_7-m058_c_5       4.92484065  -4.24808026  14.09776156 0.9877046
## m248_s_8-m058_c_5      10.04418174   0.87126083  19.21710265 0.0129633
## m248_s_9-m058_c_5      19.01128508   9.83836418  28.18420599 0.0000000
## m058_c_7-m058_c_6       3.85889975  -5.31402116  13.03182065 0.9998890
## m058_c_8-m058_c_6       8.35142711  -0.82149380  17.52434802 0.1472485
## m058_c_9-m058_c_6      16.76644964   7.59352873  25.93937054 0.0000000
## m058_s_5-m058_c_6      -0.87346918 -10.98153571   9.23459735 1.0000000
## m058_s_6-m058_c_6       0.21752361  -9.89054293  10.32559014 1.0000000
## m058_s_7-m058_c_6       1.85306527  -8.25500126  11.96113180 1.0000000
## m058_s_8-m058_c_6       4.08285265  -6.02521388  14.19091918 0.9999567
## m058_s_9-m058_c_6       7.93589313  -2.17217340  18.04395966 0.4550331
## m248_c_5-m058_c_6       0.60933914  -8.56358177   9.78226005 1.0000000
## m248_c_6-m058_c_6       1.03904971  -8.13387120  10.21197062 1.0000000
## m248_c_7-m058_c_6       8.61107821  -0.56184270  17.78399912 0.1070242
## m248_c_8-m058_c_6      20.84848693  11.67556602  30.02140784 0.0000000
## m248_c_9-m058_c_6      41.08650175  31.91358084  50.25942265 0.0000000
## m248_s_5-m058_c_6      -1.91583262 -11.08875353   7.25708829 1.0000000
## m248_s_6-m058_c_6      -0.41026636  -9.58318727   8.76265455 1.0000000
## m248_s_7-m058_c_6       2.57872625  -6.59419466  11.75164716 1.0000000
## m248_s_8-m058_c_6       7.69806734  -1.47485357  16.87098825 0.2969659
## m248_s_9-m058_c_6      16.66517069   7.49224978  25.83809159 0.0000000
## m058_c_8-m058_c_7       4.49252736  -4.68039354  13.66544827 0.9974533
## m058_c_9-m058_c_7      12.90754989   3.73462898  22.08047080 0.0000495
## m058_s_5-m058_c_7      -4.73236892 -14.84043545   5.37569761 0.9989250
## m058_s_6-m058_c_7      -3.64137614 -13.74944267   6.46669039 0.9999975
## m058_s_7-m058_c_7      -2.00583448 -12.11390101   8.10223206 1.0000000
## m058_s_8-m058_c_7       0.22395291  -9.88411362  10.33201944 1.0000000
## m058_s_9-m058_c_7       4.07699339  -6.03107314  14.18505992 0.9999581
## m248_c_5-m058_c_7      -3.24956060 -12.42248151   5.92336031 0.9999984
## m248_c_6-m058_c_7      -2.81985003 -11.99277094   6.35307087 1.0000000
## m248_c_7-m058_c_7       4.75217846  -4.42074245  13.92509937 0.9931272
## m248_c_8-m058_c_7      16.98958718   7.81666627  26.16250809 0.0000000
## m248_c_9-m058_c_7      37.22760200  28.05468109  46.40052291 0.0000000
## m248_s_5-m058_c_7      -5.77473236 -14.94765327   3.39818854 0.8978306
## m248_s_6-m058_c_7      -4.26916610 -13.44208701   4.90375481 0.9990444
## m248_s_7-m058_c_7      -1.28017350 -10.45309441   7.89274741 1.0000000
## m248_s_8-m058_c_7       3.83916760  -5.33375331  13.01208850 0.9999012
## m248_s_9-m058_c_7      12.80627094   3.63335003  21.97919185 0.0000618
## m058_c_9-m058_c_8       8.41502253  -0.75789838  17.58794344 0.1364540
## m058_s_5-m058_c_8      -9.22489629 -19.33296282   0.88317024 0.1437803
## m058_s_6-m058_c_8      -8.13390350 -18.24197003   1.97416303 0.3942722
## m058_s_7-m058_c_8      -6.49836184 -16.60642837   3.60970469 0.8726299
## m058_s_8-m058_c_8      -4.26857446 -14.37664099   5.83949208 0.9998790
## m058_s_9-m058_c_8      -0.41553398 -10.52360051   9.69253255 1.0000000
## m248_c_5-m058_c_8      -7.74208797 -16.91500888   1.43083294 0.2846289
## m248_c_6-m058_c_8      -7.31237740 -16.48529831   1.86054351 0.4172220
## m248_c_7-m058_c_8       0.25965110  -8.91326981   9.43257201 1.0000000
## m248_c_8-m058_c_8      12.49705982   3.32413891  21.66998073 0.0001203
## m248_c_9-m058_c_8      32.73507464  23.56215373  41.90799555 0.0000000
## m248_s_5-m058_c_8     -10.26725973 -19.44018064  -1.09433882 0.0089066
## m248_s_6-m058_c_8      -8.76169347 -17.93461438   0.41122744 0.0880795
## m248_s_7-m058_c_8      -5.77270086 -14.94562177   3.40022005 0.8982194
## m248_s_8-m058_c_8      -0.65335977  -9.82628068   8.51956114 1.0000000
## m248_s_9-m058_c_8       8.31374358  -0.85917733  17.48666449 0.1539466
## m058_s_5-m058_c_9     -17.63991881 -27.74798535  -7.53185228 0.0000000
## m058_s_6-m058_c_9     -16.54892603 -26.65699256  -6.44085950 0.0000003
## m058_s_7-m058_c_9     -14.91338437 -25.02145090  -4.80531784 0.0000121
## m058_s_8-m058_c_9     -12.68359698 -22.79166351  -2.57553045 0.0009070
## m058_s_9-m058_c_9      -8.83055650 -18.93862304   1.27751003 0.2156605
## m248_c_5-m058_c_9     -16.15711049 -25.33003140  -6.98418958 0.0000000
## m248_c_6-m058_c_9     -15.72739993 -24.90032083  -6.55447902 0.0000001
## m248_c_7-m058_c_9      -8.15537143 -17.32829234   1.01754948 0.1846275
## m248_c_8-m058_c_9       4.08203729  -5.09088362  13.25495820 0.9996197
## m248_c_9-m058_c_9      24.32005211  15.14713120  33.49297302 0.0000000
## m248_s_5-m058_c_9     -18.68228226 -27.85520316  -9.50936135 0.0000000
## m248_s_6-m058_c_9     -17.17671599 -26.34963690  -8.00379508 0.0000000
## m248_s_7-m058_c_9     -14.18772339 -23.36064430  -5.01480248 0.0000026
## m248_s_8-m058_c_9      -9.06838229 -18.24130320   0.10453861 0.0580073
## m248_s_9-m058_c_9      -0.10127895  -9.27419986   9.07164196 1.0000000
## m058_s_6-m058_s_5       1.09099278  -9.87274472  12.05473029 1.0000000
## m058_s_7-m058_s_5       2.72653445  -8.23720305  13.69027195 1.0000000
## m058_s_8-m058_s_5       4.95632183  -6.00741567  15.92005933 0.9994713
## m058_s_9-m058_s_5       8.80936231  -2.15437519  19.77309981 0.3978792
## m248_c_5-m058_s_5       1.48280832  -8.62525821  11.59087485 1.0000000
## m248_c_6-m058_s_5       1.91251889  -8.19554764  12.02058542 1.0000000
## m248_c_7-m058_s_5       9.48454739  -0.62351914  19.59261392 0.1075679
## m248_c_8-m058_s_5      21.72195611  11.61388957  31.83002264 0.0000000
## m248_c_9-m058_s_5      41.95997092  31.85190439  52.06803746 0.0000000
## m248_s_5-m058_s_5      -1.04236344 -11.15042997   9.06570309 1.0000000
## m248_s_6-m058_s_5       0.46320282  -9.64486371  10.57126935 1.0000000
## m248_s_7-m058_s_5       3.45219543  -6.65587110  13.56026196 0.9999994
## m248_s_8-m058_s_5       8.57153652  -1.53653001  18.67960305 0.2746505
## m248_s_9-m058_s_5      17.53863986   7.43057333  27.64670640 0.0000000
## m058_s_7-m058_s_6       1.63554166  -9.32819584  12.59927916 1.0000000
## m058_s_8-m058_s_6       3.86532905  -7.09840845  14.82906655 0.9999986
## m058_s_9-m058_s_6       7.71836953  -3.24536798  18.68210703 0.7163490
## m248_c_5-m058_s_6       0.39181554  -9.71625100  10.49988207 1.0000000
## m248_c_6-m058_s_6       0.82152610  -9.28654043  10.92959264 1.0000000
## m248_c_7-m058_s_6       8.39355460  -1.71451193  18.50162113 0.3204806
## m248_c_8-m058_s_6      20.63096332  10.52289679  30.73902985 0.0000000
## m248_c_9-m058_s_6      40.86897814  30.76091161  50.97704467 0.0000000
## m248_s_5-m058_s_6      -2.13335623 -12.24142276   7.97471031 1.0000000
## m248_s_6-m058_s_6      -0.62778996 -10.73585650   9.48027657 1.0000000
## m248_s_7-m058_s_6       2.36120264  -7.74686389  12.46926917 1.0000000
## m248_s_8-m058_s_6       7.48054373  -2.62752280  17.58861027 0.6020459
## m248_s_9-m058_s_6      16.44764708   6.33958055  26.55571361 0.0000004
## m058_s_8-m058_s_7       2.22978738  -8.73395012  13.19352489 1.0000000
## m058_s_9-m058_s_7       6.08282786  -4.88090964  17.04656536 0.9797082
## m248_c_5-m058_s_7      -1.24372613 -11.35179266   8.86434040 1.0000000
## m248_c_6-m058_s_7      -0.81401556 -10.92208209   9.29405097 1.0000000
## m248_c_7-m058_s_7       6.75801294  -3.35005359  16.86607947 0.8145471
## m248_c_8-m058_s_7      18.99542166   8.88735513  29.10348819 0.0000000
## m248_c_9-m058_s_7      39.23343648  29.12536995  49.34150301 0.0000000
## m248_s_5-m058_s_7      -3.76889789 -13.87696442   6.33916864 0.9999939
## m248_s_6-m058_s_7      -2.26333163 -12.37139816   7.84473490 1.0000000
## m248_s_7-m058_s_7       0.72566098  -9.38240555  10.83372751 1.0000000
## m248_s_8-m058_s_7       5.84500207  -4.26306446  15.95306860 0.9636162
## m248_s_9-m058_s_7      14.81210542   4.70403889  24.92017195 0.0000150
## m058_s_9-m058_s_8       3.85304048  -7.11069702  14.81677798 0.9999987
## m248_c_5-m058_s_8      -3.47351351 -13.58158004   6.63455302 0.9999993
## m248_c_6-m058_s_8      -3.04380294 -13.15186947   7.06426359 1.0000000
## m248_c_7-m058_s_8       4.52822556  -5.57984098  14.63629209 0.9995625
## m248_c_8-m058_s_8      16.76563427   6.65756774  26.87370081 0.0000002
## m248_c_9-m058_s_8      37.00364909  26.89558256  47.11171562 0.0000000
## m248_s_5-m058_s_8      -5.99868527 -16.10675180   4.10938126 0.9489267
## m248_s_6-m058_s_8      -4.49311901 -14.60118554   5.61494752 0.9996287
## m248_s_7-m058_s_8      -1.50412640 -11.61219294   8.60394013 1.0000000
## m248_s_8-m058_s_8       3.61521469  -6.49285184  13.72328122 0.9999979
## m248_s_9-m058_s_8      12.58231803   2.47425150  22.69038456 0.0010852
## m248_c_5-m058_s_9      -7.32655399 -17.43462052   2.78151254 0.6514417
## m248_c_6-m058_s_9      -6.89684342 -17.00490995   3.21122311 0.7786855
## m248_c_7-m058_s_9       0.67518508  -9.43288145  10.78325161 1.0000000
## m248_c_8-m058_s_9      12.91259380   2.80452726  23.02066033 0.0006012
## m248_c_9-m058_s_9      33.15060861  23.04254208  43.25867515 0.0000000
## m248_s_5-m058_s_9      -9.85172575 -19.95979228   0.25634078 0.0692933
## m248_s_6-m058_s_9      -8.34615949 -18.45422602   1.76190704 0.3333689
## m248_s_7-m058_s_9      -5.35716688 -15.46523341   4.75089965 0.9899972
## m248_s_8-m058_s_9      -0.23782579 -10.34589232   9.87024074 1.0000000
## m248_s_9-m058_s_9       8.72927755  -1.37878898  18.83734409 0.2376102
## m248_c_6-m248_c_5       0.42971057  -8.74321034   9.60263148 1.0000000
## m248_c_7-m248_c_5       8.00173907  -1.17118184  17.17465998 0.2184119
## m248_c_8-m248_c_5      20.23914778  11.06622688  29.41206869 0.0000000
## m248_c_9-m248_c_5      40.47716260  31.30424169  49.65008351 0.0000000
## m248_s_5-m248_c_5      -2.52517176 -11.69809267   6.64774915 1.0000000
## m248_s_6-m248_c_5      -1.01960550 -10.19252641   8.15331541 1.0000000
## m248_s_7-m248_c_5       1.96938711  -7.20353380  11.14230802 1.0000000
## m248_s_8-m248_c_5       7.08872820  -2.08419271  16.26164911 0.4946428
## m248_s_9-m248_c_5      16.05583154   6.88291063  25.22875245 0.0000000
## m248_c_7-m248_c_6       7.57202850  -1.60089241  16.74494941 0.3339712
## m248_c_8-m248_c_6      19.80943722  10.63651631  28.98235813 0.0000000
## m248_c_9-m248_c_6      40.04745204  30.87453113  49.22037294 0.0000000
## m248_s_5-m248_c_6      -2.95488233 -12.12780324   6.21803858 0.9999999
## m248_s_6-m248_c_6      -1.44931607 -10.62223698   7.72360484 1.0000000
## m248_s_7-m248_c_6       1.53967654  -7.63324437  10.71259745 1.0000000
## m248_s_8-m248_c_6       6.65901763  -2.51390328  15.83193854 0.6478506
## m248_s_9-m248_c_6      15.62612098   6.45320007  24.79904188 0.0000001
## m248_c_8-m248_c_7      12.23740872   3.06448781  21.41032963 0.0002080
## m248_c_9-m248_c_7      32.47542354  23.30250263  41.64834445 0.0000000
## m248_s_5-m248_c_7     -10.52691083 -19.69983174  -1.35398992 0.0056742
## m248_s_6-m248_c_7      -9.02134457 -18.19426548   0.15157634 0.0619546
## m248_s_7-m248_c_7      -6.03235196 -15.20527287   3.14056895 0.8408069
## m248_s_8-m248_c_7      -0.91301087 -10.08593178   8.25991004 1.0000000
## m248_s_9-m248_c_7       8.05409248  -1.11882843  17.22701339 0.2064479
## m248_c_9-m248_c_8      20.23801482  11.06509391  29.41093573 0.0000000
## m248_s_5-m248_c_8     -22.76431955 -31.93724046 -13.59139864 0.0000000
## m248_s_6-m248_c_8     -21.25875329 -30.43167419 -12.08583238 0.0000000
## m248_s_7-m248_c_8     -18.26976068 -27.44268159  -9.09683977 0.0000000
## m248_s_8-m248_c_8     -13.15041959 -22.32334050  -3.97749868 0.0000289
## m248_s_9-m248_c_8      -4.18331624 -13.35623715   4.98960467 0.9993663
## m248_s_5-m248_c_9     -43.00233437 -52.17525527 -33.82941346 0.0000000
## m248_s_6-m248_c_9     -41.49676810 -50.66968901 -32.32384719 0.0000000
## m248_s_7-m248_c_9     -38.50777550 -47.68069641 -29.33485459 0.0000000
## m248_s_8-m248_c_9     -33.38843440 -42.56135531 -24.21551350 0.0000000
## m248_s_9-m248_c_9     -24.42133106 -33.59425197 -15.24841015 0.0000000
## m248_s_6-m248_s_5       1.50556626  -7.66735465  10.67848717 1.0000000
## m248_s_7-m248_s_5       4.49455887  -4.67836204  13.66747978 0.9974319
## m248_s_8-m248_s_5       9.61389996   0.44097905  18.78682087 0.0258638
## m248_s_9-m248_s_5      18.58100331   9.40808240  27.75392421 0.0000000
## m248_s_7-m248_s_6       2.98899261  -6.18392830  12.16191352 0.9999998
## m248_s_8-m248_s_6       8.10833370  -1.06458721  17.28125461 0.1945454
## m248_s_9-m248_s_6      17.07543704   7.90251613  26.24835795 0.0000000
## m248_s_8-m248_s_7       5.11934109  -4.05357982  14.29226200 0.9778770
## m248_s_9-m248_s_7      14.08644444   4.91352353  23.25936535 0.0000033
## m248_s_9-m248_s_8       8.96710335  -0.20581756  18.14002425 0.0667885

library(multcompView)
P17 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P17)
stat.test

##     f1_c_6     f1_c_7     f1_c_8     f1_c_9     f1_s_5     f1_s_6     f1_s_7 
##     "abcd"      "efg"       "hi"        "j"       "ab"     "abcd"     "cefk" 
##     f1_s_8     f1_s_9 la1511_c_5 la1511_c_6 la1511_c_7 la1511_c_8 la1511_c_9 
##       "gh"        "l"        "a"      "abd"   "abcdek"       "fg"        "i" 
## la1511_s_5 la1511_s_6 la1511_s_7 la1511_s_8 la1511_s_9   m058_c_5   m058_c_6 
##        "a"      "abd"     "abcd"     "cefk"     "efgk"        "a"     "abcd" 
##   m058_c_7   m058_c_8   m058_c_9   m058_s_5   m058_s_6   m058_s_7   m058_s_8 
##    "abcdk"     "cefk"       "fg"     "abcd"     "abcd"     "abcd"   "abcdek" 
##   m058_s_9   m248_c_5   m248_c_6   m248_c_7   m248_c_8   m248_c_9   m248_s_5 
##   "bcdefk"     "abcd"     "abcd"     "cefk"       "gh"        "l"       "ab" 
##   m248_s_6   m248_s_7   m248_s_8   m248_s_9     f1_c_5 
##     "abcd"     "abcd"    "cdefk"       "fg"        "a"

test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test

test

for(i in 1:nrow(test)){
  test$genotype[i] <- strsplit(test$group1[i], "_")[[1]][1]
  test$condition[i] <- strsplit(test$group1[i], "_")[[1]][2]
  test$day[i] <- strsplit(test$group1[i], "_")[[1]][3]
}

test

MR_all$genotype<- factor(MR_all$genotype, levels=c("la1511", "m058", "m248","f1"))

TRS_graph <- ggplot(data=MR_all, mapping = aes(x = All.ID, y = TRS , colour = condition)) 
TRS_graph  <- TRS_graph  + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
TRS_graph  <- TRS_graph  + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
TRS_graph  <- TRS_graph  + scale_color_manual(values = c("turquoise3", "maroon3", "dark orange", "green"))
TRS_graph  <- TRS_graph  + ylab("Total root size (cm)") + xlab("")
TRS_graph  <- TRS_graph  + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
TRS_graph  <- TRS_graph  + stat_pvalue_manual(test, label = "Tukey", y.position = 80) 
TRS_graph <- TRS_graph + rremove("legend")
TRS_graph

###I am not sure the graph below represents what day????? very confusing

so I continue with line 240….

test

test2 <- test[,c(1,4:6)]
rownames(test) <- 1:40

MR_all$genotype <- factor(MR_all$genotype, levels = c("m248", "m058", "la1511", "f1"))

better_TRS_graph <- ggplot(data=MR_all, aes(x= genotype, y=TRS, color = genotype))
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: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.

better_TRS_graph <- better_TRS_graph + facet_grid(condition ~ day, scales = "free") + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange", "green"))
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_pvalue_manual(test, label = "Tukey", y.position = 80) 
better_TRS_graph

## Warning: Combining variables of class <numeric> and <character> was deprecated in
## ggplot2 3.4.0.
## ℹ Please ensure your variables are compatible before plotting (location:
##   `combine_vars()`)

#to avoid the complication of comparing all days together, we better graph each indvidual and compare everything with that setting. below is the d9.

MR_all_d5 <- subset(MR_all, MR_all$day == 5)
MR_all_d6 <- subset(MR_all, MR_all$day == 6)
MR_all_d7 <- subset(MR_all, MR_all$day == 7)
MR_all_d8 <- subset(MR_all, MR_all$day == 8)
MR_all_d9 <- subset(MR_all, MR_all$day == 9)

MR_all_d9$new_id <- paste(MR_all_d9$genotype, MR_all_d9$condition, sep="_")
Output <- TukeyHSD(aov(TRS ~ new_id, data = MR_all_d9))
Output

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = TRS ~ new_id, data = MR_all_d9)
## 
## $new_id
##                          diff        lwr        upr     p adj
## f1_s-f1_c         -15.5529621 -27.683258  -3.422666 0.0036469
## la1511_c-f1_c     -24.7896626 -36.919959 -12.659367 0.0000005
## la1511_s-f1_c     -42.9898266 -55.120123 -30.859531 0.0000000
## m058_c-f1_c       -39.2192119 -51.349508 -27.088916 0.0000000
## m058_s-f1_c       -48.0497684 -61.354483 -34.745054 0.0000000
## m248_c-f1_c       -14.8991597 -27.029456  -2.768864 0.0062724
## m248_s-f1_c       -39.3204908 -51.450787 -27.190195 0.0000000
## la1511_c-f1_s      -9.2367005 -21.043464   2.570063 0.2365053
## la1511_s-f1_s     -27.4368645 -39.243628 -15.630101 0.0000000
## m058_c-f1_s       -23.6662498 -35.473013 -11.859487 0.0000008
## m058_s-f1_s       -32.4968063 -45.507226 -19.486387 0.0000000
## m248_c-f1_s         0.6538023 -11.152961  12.460565 0.9999997
## m248_s-f1_s       -23.7675287 -35.574292 -11.960766 0.0000007
## la1511_s-la1511_c -18.2001640 -30.006927  -6.393401 0.0002171
## m058_c-la1511_c   -14.4295492 -26.236312  -2.622786 0.0066631
## m058_s-la1511_c   -23.2601057 -36.270525 -10.249686 0.0000117
## m248_c-la1511_c     9.8905029  -1.916260  21.697266 0.1669376
## m248_s-la1511_c   -14.5308282 -26.337591  -2.724065 0.0061214
## m058_c-la1511_s     3.7706148  -8.036148  15.577378 0.9731105
## m058_s-la1511_s    -5.0599418 -18.070361   7.950478 0.9243310
## m248_c-la1511_s    28.0906669  16.283904  39.897430 0.0000000
## m248_s-la1511_s     3.6693358  -8.137427  15.476099 0.9768981
## m058_s-m058_c      -8.8305565 -21.840976   4.179863 0.4114128
## m248_c-m058_c      24.3200521  12.513289  36.126815 0.0000004
## m248_s-m058_c      -0.1012789 -11.908042  11.705484 1.0000000
## m248_c-m058_s      33.1506086  20.140189  46.161028 0.0000000
## m248_s-m058_s       8.7292776  -4.281142  21.739697 0.4265530
## m248_s-m248_c     -24.4213311 -36.228094 -12.614568 0.0000003

P17 = Output$new_id[,'p adj']
stat.test<- multcompLetters(P17)
stat.test

##     f1_s la1511_c la1511_s   m058_c   m058_s   m248_c   m248_s     f1_c 
##      "a"      "a"      "b"      "b"      "b"      "a"      "b"      "c"

test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test

for(i in 1:nrow(test)){
  test$genotype[i] <- strsplit(test$group1[i], "_")[[1]][1]
  test$condition[i] <- strsplit(test$group1[i], "_")[[1]][2]
}

MR_all_d9$genotype <- factor(MR_all_d9$genotype, levels = c("m248", "m058", "la1511", "f1"))

TRS_graph_d9 <- ggplot(data=MR_all_d9, aes(x= new_id, y=TRS, color = genotype))
TRS_graph_d9 <- TRS_graph_d9 + geom_beeswarm(alpha=0.6, priority = "density")
TRS_graph_d9 <- TRS_graph_d9 + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")
TRS_graph_d9 <- TRS_graph_d9 + facet_grid( ~ condition, scales = "free") + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange", "green"))
TRS_graph_d9 <- TRS_graph_d9 + ylab("Total root size (cm)") + xlab("Genotype") + theme(legend.position='none')
TRS_graph_d9 <- TRS_graph_d9 + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
TRS_graph_d9 <- TRS_graph_d9 + stat_pvalue_manual(test, label = "Tukey", y.position = 80) 
TRS_graph_d9

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':
## 
##     layout

head(MR_all)

#to remove outliers from the day 5, for two seedlings, we can subset the following 
MR_all2 <- subset(MR_all, MR_all$length < 10)

my_graph <- ggplot(data=MR_all2, 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)

geno_m248 <- subset(MR_all2, MR_all2$genotype == "m248")

my_graph <- ggplot(data=geno_m248, 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_m248, 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_m248, 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_m248, 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

geno_m058 <- subset(MR_all2, MR_all2$genotype == "m058")
geno_m058

my_graph <- ggplot(data=geno_m058, 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_m058, 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_m058, 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_m058, 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

geno_la1511 <- subset(MR_all2, MR_all2$genotype == "la1511")
geno_la1511

my_graph <- ggplot(data=geno_la1511, 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_la1511, 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_la1511, 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_la1511, 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

geno_f1 <- subset(MR_all2, MR_all2$genotype == "f1")
geno_f1

my_graph <- ggplot(data=geno_f1, 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_f1, 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_f1, 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_f1, 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

MR <- rbind(geno_m248, geno_m058)
MR <- rbind(MR, geno_la1511)
MR <- rbind(MR, geno_f1)


MR_time_graph <- ggplot(data=MR, 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 + ylab("Main root 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: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

MR_time_graph

###very interesting to see m058 growth is more than la and m248!!!!!!!

LRL_time_graph <- ggplot(data=MR, 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("Lateral root 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")
LRL_time_graph

LRno_time_graph <- ggplot(data=MR, 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("Lateral root number") + 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")
LRno_time_graph

TRS_time_graph <- ggplot(data=MR, 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")
TRS_time_graph

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, MR$root_name == unique(MR$root_name)[1])
temp2 <- temp1[order(temp1$day),]
temp2

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

#Although I do not have growth inhibition for the M248 M058 LA1511 in all days but I still would follow the following steps to make sure this is true for all plants……“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

OK - the above looks good - so now we have to subset into MR temp where MRdouble == no, and calculate the growth rate:

temp_MR2 <- subset(temp_MR, temp_MR$MRdouble == "no")
plot(temp_MR2$length~ temp_MR2$day)
# let's add the regression line to this graph
abline(lm(temp_MR2$length~ temp_MR2$day))

This looks good! now we just have to get the model paramerers out of the linear model (lm) that was used to draw the regression line:

MR_model <- lm(temp_MR2$length~ temp_MR2$day)
MR_model

## 
## Call:
## lm(formula = temp_MR2$length ~ temp_MR2$day)
## 
## Coefficients:
##  (Intercept)  temp_MR2$day  
##       -1.982         1.064

summary(MR_model)

## 
## Call:
## lm(formula = temp_MR2$length ~ temp_MR2$day)
## 
## Residuals:
##        1        2        3        4        5 
## -0.25105  0.13882  0.30524 -0.02275 -0.17027 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -1.98182    0.59030  -3.357  0.04382 * 
## temp_MR2$day  1.06352    0.08266  12.866  0.00101 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2614 on 3 degrees of freedom
## Multiple R-squared:  0.9822, Adjusted R-squared:  0.9763 
## F-statistic: 165.5 on 1 and 3 DF,  p-value: 0.001013

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

# 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  
##        -35.8           7.0

summary(LRno_model)

## 
## Call:
## lm(formula = LR_temp2$LRno ~ LR_temp2$day)
## 
## Residuals:
##    1    2    3    4    5 
##  1.8 -1.2 -2.2  0.8  0.8 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -35.800      4.285  -8.355  0.00359 **
## LR_temp2$day    7.000      0.600  11.667  0.00135 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.897 on 3 degrees of freedom
## Multiple R-squared:  0.9784, Adjusted R-squared:  0.9712 
## F-statistic: 136.1 on 1 and 3 DF,  p-value: 0.001353

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

## [1] 7

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  
##      -1.2775        0.2778

summary(aLRL_model)

## 
## Call:
## lm(formula = LR_temp2$aLRL ~ LR_temp2$day)
## 
## Residuals:
##        1        2        3        4        5 
##  0.13648 -0.10089 -0.05686 -0.12951  0.15078 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -1.27748    0.34875  -3.663   0.0352 *
## LR_temp2$day  0.27781    0.04884   5.689   0.0108 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1544 on 3 degrees of freedom
## Multiple R-squared:  0.9152, Adjusted R-squared:  0.8869 
## F-statistic: 32.36 on 1 and 3 DF,  p-value: 0.01077

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

## [1] 0.2778086

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

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

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

growth_factors[1,1] <- temp2$root_name[1]
growth_factors[1,2] <- as.character(temp2$genotype[1])
growth_factors[1,3] <- as.character(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 - the above data table looks good - let’s loop it for all the root_names in the MR_OK dataset:

length(unique(MR$root_name))

## [1] 75

# we are starting the loop from 2nd plant (i in 2:...), because we already calculated growth rates for the 1st plant:
for(e in 1:75){
  temp1 <- subset(MR, MR$root_name == unique(MR$root_name)[e])
  temp2 <- temp1[order(temp1$day),]
  temp2
  ############ MR calculations
  temp_MR <- temp2[,c("day", "length")]
  temp_MR$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
  ############ 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] <- as.character(temp2$genotype[1])
  growth_factors[e,3] <- as.character(temp2$cond[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

Looks good - now let’s save this file and have a look at how the growth factos compare between stress and genotypes

write.csv(growth_factors, "202305_RSA_F1_growth_factors.csv", row.names = FALSE)

Visualizing the growth factors:

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))


library(multcompView)
growth_factors$GenoCond <- paste(growth_factors$condition, "_", growth_factors$genotype, sep = "")
growth_factors

Output <- TukeyHSD(aov(MR.delta ~ GenoCond, data = growth_factors))
Output

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = MR.delta ~ GenoCond, data = growth_factors)
## 
## $GenoCond
##                            diff        lwr          upr     p adj
## c_la1511-c_f1      0.0363174935 -0.5389293  0.611564322 0.9999993
## c_m058-c_f1        0.1492396531 -0.4260072  0.724486482 0.9918892
## c_m248-c_f1       -0.3035762609 -0.8788231  0.271670568 0.7177686
## s_f1-c_f1         -0.0004483468 -0.5756952  0.574798482 1.0000000
## s_la1511-c_f1     -0.3073100169 -0.8825568  0.267936812 0.7051815
## s_m058-c_f1       -0.7202639476 -1.3512044 -0.089323450 0.0144597
## s_m248-c_f1       -0.5834031628 -1.1735940  0.006787631 0.0549046
## c_m058-c_la1511    0.1129221596 -0.4469820  0.672826308 0.9983010
## c_m248-c_la1511   -0.3398937544 -0.8997979  0.220010394 0.5562566
## s_f1-c_la1511     -0.0367658403 -0.5966700  0.523138308 0.9999991
## s_la1511-c_la1511 -0.3436275104 -0.9035317  0.216276638 0.5423677
## s_m058-c_la1511   -0.7565814411 -1.3735658 -0.139597120 0.0064195
## s_m248-c_la1511   -0.6197206563 -1.1949675 -0.044473827 0.0258931
## c_m248-c_m058     -0.4528159140 -1.0127201  0.107088234 0.2009488
## s_f1-c_m058       -0.1496879999 -0.7095921  0.410216149 0.9902949
## s_la1511-c_m058   -0.4565496700 -1.0164538  0.103354478 0.1926639
## s_m058-c_m058     -0.8695036007 -1.4864879 -0.252519279 0.0009596
## s_m248-c_m058     -0.7326428159 -1.3078896 -0.157395987 0.0039963
## s_f1-c_m248        0.3031279141 -0.2567762  0.863032063 0.6910844
## s_la1511-c_m248   -0.0037337560 -0.5636379  0.556170392 1.0000000
## s_m058-c_m248     -0.4166876867 -1.0336720  0.200296635 0.4174791
## s_m248-c_m248     -0.2798269019 -0.8550737  0.295419927 0.7929561
## s_la1511-s_f1     -0.3068616701 -0.8667658  0.253042478 0.6778082
## s_m058-s_f1       -0.7198156009 -1.3367999 -0.102831279 0.0114242
## s_m248-s_f1       -0.5829548160 -1.1582016 -0.007707987 0.0447704
## s_m058-s_la1511   -0.4129539307 -1.0299383  0.204030391 0.4292944
## s_m248-s_la1511   -0.2760931459 -0.8513400  0.299153683 0.8038709
## s_m248-s_m058      0.1368607849 -0.4940797  0.767801283 0.9973046

P4 = Output$GenoCond[,'p adj']
P4

##     c_la1511-c_f1       c_m058-c_f1       c_m248-c_f1         s_f1-c_f1 
##      0.9999993357      0.9918891941      0.7177686316      1.0000000000 
##     s_la1511-c_f1       s_m058-c_f1       s_m248-c_f1   c_m058-c_la1511 
##      0.7051815372      0.0144596527      0.0549046215      0.9983010003 
##   c_m248-c_la1511     s_f1-c_la1511 s_la1511-c_la1511   s_m058-c_la1511 
##      0.5562566400      0.9999991275      0.5423677229      0.0064194612 
##   s_m248-c_la1511     c_m248-c_m058       s_f1-c_m058   s_la1511-c_m058 
##      0.0258931035      0.2009487896      0.9902949385      0.1926639450 
##     s_m058-c_m058     s_m248-c_m058       s_f1-c_m248   s_la1511-c_m248 
##      0.0009595996      0.0039963138      0.6910843690      1.0000000000 
##     s_m058-c_m248     s_m248-c_m248     s_la1511-s_f1       s_m058-s_f1 
##      0.4174791446      0.7929560662      0.6778081777      0.0114242212 
##       s_m248-s_f1   s_m058-s_la1511   s_m248-s_la1511     s_m248-s_m058 
##      0.0447703873      0.4292944311      0.8038709352      0.9973046392

stat.test<- multcompLetters(P4)
stat.test

## c_la1511   c_m058   c_m248     s_f1 s_la1511   s_m058   s_m248     c_f1 
##      "a"      "a"    "abc"      "a"    "abc"      "b"     "bc"     "ac"

test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test

growth_factors$GenoCond <- factor(growth_factors$GenoCond, levels = c("c_m248", "c_m058", "c_la1511", "c_f1","s_m248", "s_m058", "s_la1511", "s_f1"))

MR.delta_p_geno <- ggerrorplot(growth_factors, y = "MR.delta", x = "GenoCond", fill="genotype", 
                               color="genotype", 
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Genotype", ylab="Growth Rate (cm / day)") 
MR.delta_p_geno <- MR.delta_p_geno + rremove("legend")
MR.delta_p_geno <- MR.delta_p_geno + stat_pvalue_manual(test, label = "Tukey", y.position = 3)
MR.delta_p_geno <- MR.delta_p_geno + ggtitle("Main Root Growth")
MR.delta_p_geno

Output <- TukeyHSD(aov(LRno.delta ~ GenoCond, data = growth_factors))
P4 = Output$GenoCond[,'p adj']
stat.test<- multcompLetters(P4)
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"


LRno.delta_p_geno <- ggerrorplot(growth_factors, y = "LRno.delta", x = "GenoCond", fill="genotype", 
                                 color="genotype", 
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Genotype", ylab="LR increase Rate (# LR / day)") 
LRno.delta_p_geno <- LRno.delta_p_geno + rremove("legend") 
LRno.delta_p_geno <- LRno.delta_p_geno + stat_pvalue_manual(test, label = "Tukey", y.position = 11)
LRno.delta_p_geno <- LRno.delta_p_geno + ggtitle("Lateral Root Number Increase")
LRno.delta_p_geno

Output <- TukeyHSD(aov(aLRL.delta ~ GenoCond, data = growth_factors))
P4 = Output$GenoCond[,'p adj']
stat.test<- multcompLetters(P4)
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"

aLRL.delta_p_geno <- ggerrorplot(growth_factors, y = "aLRL.delta", x = "GenoCond", fill="genotype",
                                   color="genotype", 
                        desc_stat = "mean_sd", add = "jitter", 
                        add.params = list(color = "darkgray"),
                        xlab="Genotype", ylab="growth (cm / day)") 
aLRL.delta_p_geno <- aLRL.delta_p_geno + rremove("legend") 
aLRL.delta_p_geno <- aLRL.delta_p_geno + stat_pvalue_manual(test, label = "Tukey", y.position = 1.3)
aLRL.delta_p_geno <- aLRL.delta_p_geno + ggtitle("average Lateral Root Growth")
aLRL.delta_p_geno

### Calculating the relative growth rates (Salt Tolerance Indixes):

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)
avg_growth <- summaryBy(data = growth_factors, 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 == "c")
avg_growth_C <- avg_growth_C[,c(1,3:5)]
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_factors, 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)

and because we are only interested in STI under SALT conditions - we subset this dataset for cond == s

STI <- subset(STI_growth_factors, STI_growth_factors$condition == "s")
STI

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

Output <- TukeyHSD(aov(MR.STI ~ genotype, data = STI))
P4 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P4)
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
STI$genotype <- factor(STI$genotype, levels = c("la1511", "m058", "m248", "f1"))

MR.STI_plot <- ggplot(data = STI, mapping = aes(x = genotype, y = MR.STI, colour = genotype)) 
MR.STI_plot <- MR.STI_plot + geom_beeswarm(alpha=0.6, priority = "density")
MR.STI_plot <- MR.STI_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
MR.STI_plot <- MR.STI_plot + theme(legend.position = "none")
MR.STI_plot <- MR.STI_plot + xlab("")
MR.STI_plot <- MR.STI_plot + ylab("Fraction of control") + ggtitle("Salt Tolerance Index based on Main Root Growth")
MR.STI_plot <- MR.STI_plot + stat_pvalue_manual(test, label = "Tukey", y.position = 1.2)
MR.STI_plot <- MR.STI_plot + scale_color_manual(values=c("royalblue", "coral3", "deeppink", "green"))
MR.STI_plot

Then for aLRL:

Output <- TukeyHSD(aov(aLRL.STI ~ genotype, data = STI))
P4 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P4)
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"

aLRL.STI_plot <- ggplot(data = STI, mapping = aes(x = genotype, y = aLRL.STI, colour = genotype)) 
aLRL.STI_plot <- aLRL.STI_plot + geom_beeswarm(alpha=0.6, priority = "density")
aLRL.STI_plot <- aLRL.STI_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
aLRL.STI_plot <- aLRL.STI_plot + theme(legend.position = "none")
aLRL.STI_plot <- aLRL.STI_plot + xlab("")
aLRL.STI_plot <- aLRL.STI_plot + ylab("Salt tolerance index based on average lateral root growth (Fraction of control)")  #+ ggtitle("Salt Tolerance Index based on average Lateral Root Growth")
aLRL.STI_plot <- aLRL.STI_plot + stat_pvalue_manual(test, label = "Tukey", y.position = 2.2) 
aLRL.STI_plot <- aLRL.STI_plot + scale_color_manual(values=c("royalblue", "coral3", "deeppink", "green"))
aLRL.STI_plot

#very weired, m058 has increased STI....I never saw that!!!

Output <- TukeyHSD(aov(LRno.STI ~ genotype, data = STI))
P4 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P4)
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"

LRno.STI_plot <- ggplot(data = STI, mapping = aes(x = genotype, y = LRno.STI, colour = genotype)) 
LRno.STI_plot <- LRno.STI_plot + geom_beeswarm(alpha=0.6, priority = "density")
LRno.STI_plot <- LRno.STI_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
LRno.STI_plot <- LRno.STI_plot + theme(legend.position = "none")
LRno.STI_plot <- LRno.STI_plot + xlab("")
LRno.STI_plot <- LRno.STI_plot + ylab("Salt tolerance index based on average increase in lateral root number (Fraction of control)")  #+ ggtitle("Salt Tolerance Index based on average Increase in Lateral Root Number")
LRno.STI_plot <- LRno.STI_plot  + stat_pvalue_manual(test, label = "Tukey", y.position = 2.2)  
LRno.STI_plot <- LRno.STI_plot + scale_color_manual(values=c("royalblue", "coral3", "deeppink", "green"))
LRno.STI_plot

####I decided to plot Main root length, Lateral root length, and lateral root number, and main root growth together in one pdf. Let’s see

library(cowplot)

pdf("Figure1_F1-MainRootLength.pdf", height = 6, width = 12)
plot_grid(MR_time_graph,
          align = "hv", labels=c(""), 
          label_size = 24)
dev.off()

## png 
##   2

pdf("Figure2_F1-LateralRootLength.pdf", height = 6, width = 12)
plot_grid(LRL_time_graph,
          align = "hv", labels=c(""), 
          label_size = 24)
dev.off()

## png 
##   2

pdf("Figure3_F1-LateralRootNumber.pdf", height = 6, width = 12)
plot_grid(LRno_time_graph,
          align = "hv", labels=c(""), 
          label_size = 24)
dev.off()

## png 
##   2

pdf("Figure4_F1-MainRootGrowth.pdf", height = 6, width = 12)
plot_grid(MR.delta_p_geno,
          align = "hv", labels=c(""), 
          label_size = 24)
dev.off()

## png 
##   2

lets try to create a single pdf for all 4 graphs here

library(cowplot)
plot_grid(MR_time_graph, LRL_time_graph, LRno_time_graph, MR.delta_p_geno, labels = c("AUTO"), ncol = 2)

pdf("20230503_F1_RSA_analysis_all.pdf", width = 13, height = 10)
plot_grid(MR_time_graph, LRL_time_graph, LRno_time_graph, MR.delta_p_geno, labels = c("AUTO"), ncol = 2)
dev.off()

## png 
##   2

plot_grid(MR.STI_plot, LRno.STI_plot, aLRL.STI_plot,labels = c("AUTO"), ncol = 2)

pdf("20230503_F1_STI.pdf", width = 13, height = 10)
plot_grid(MR.STI_plot, LRno.STI_plot, aLRL.STI_plot, labels = c("AUTO"), ncol = 2)
dev.off()

## png 
##   2

plot_grid(MR_time_graph, LRL_time_graph, LRno_time_graph, MR.delta_p_geno,LRno.delta_p_geno,aLRL.delta_p_geno, labels = c("AUTO"), ncol = 3)

pdf("20230503_F1-RSA_analysis_allv2.pdf", width = 13, height = 10)
plot_grid(MR_time_graph, LRL_time_graph, LRno_time_graph, MR.delta_p_geno,LRno.delta_p_geno,aLRL.delta_p_geno, labels = c("AUTO"), ncol = 2)
dev.off()

## png 
##   2