Yana 2025-10-17 brachy and wheat assay analysis

This notebook describes a quick analysis of the results collected by PhenoSight facility on wheat and brachypodium mutant lines, developed by Prof. Vatmaniuk’s lab, plants grown by Yana Kavulych. The data analysis was performed by Magda Julkowska

Load data and clean it up:

data1 <- read.csv("Exp_10236_PS2_Analysis.csv")
data1

data <- subset(data1, data1$Roi.No != "All")
data

OK - let’s separate the tray info from experiment:

strsplit(data$File[1], "_")

## [[1]]
## [1] "HDR"    "10236"  "41914"  "10.INF"

for(i in 1:nrow(data)){
  data$Tray[i] <- strsplit(data$File[i], "_")[[1]][3]
}
unique(data$Tray)

## [1] "41914" "41934"

data

colnames(data)[27] <- "phi.no"
colnames(data)[29] <- "phi.npq"

data

PS1 <- subset(data, data$nTmPam < 2)
PS2 <- subset(data, data$nTmPam > 1)
PS1

PS2

now - let’s add all of these additional FvFm into a better organized dataframe:

library(reshape2)
PS1 <- PS1[,c(1:4,6,9)]
PS1

PS2 <- PS2[,c(1:4,6, 10:39)]
PS2

PS3 <- merge(PS1, PS2, by = c("File", "Date", "Time", "Roi.No", "Roi.Mask"))
PS3

colnames(PS3)

##  [1] "File"     "Date"     "Time"     "Roi.No"   "Roi.Mask" "Fv.Fm"   
##  [7] "s.d."     "Fq..Fm."  "s.d..1"   "NPQ"      "s.d..2"   "F0."     
## [13] "s.d..3"   "qP"       "s.d..4"   "qN"       "s.d..5"   "qL"      
## [19] "s.d..6"   "qE"       "s.d..7"   "qI"       "s.d..8"   "phi.no"  
## [25] "s.d..9"   "phi.npq"  "s.d..10"  "npq.t."   "s.d..11"  "ChlIdx"  
## [31] "s.d..12"  "AriIdx"   "s.d..13"  "NDVI"     "s.d..14"  "Tray"

PS4 <- PS3[,c(1:5, 36, 6, 8, 10, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34)]
PS4

for(i in 1:nrow(PS4)){
  PS4$month[i] <- substr(PS4$Date[i], 5, 6)
  PS4$day[i] <- substr(PS4$Date[i], 7, 8)
  PS4$hour[i] <- substr(PS4$Time[i], 1,2)
}
PS4

unique(PS4$month)

## [1] "09" "10"

temp <- subset(PS4, PS4$month == "09")
min(temp$day)

## [1] "17"

temp <- subset(temp, temp$day == "17")
min(temp$hour)

## [1] "22"

unique(PS4$day)

##  [1] "21" "22" "23" "24" "25" "27" "28" "17" "29" "30" "01" "02" "03" "18" "04"
## [16] "05" "06" "07" "08" "09" "10" "11" "12" "13" "19" "14" "15" "16" "20"

unique(PS4$hour)

##  [1] "20" "10" "08" "09" "11" "22" "14" "18" "16" "07" "12" "15"

PS4

OK - let’s calculate TOE (time of experiment):

PS4$cum.days <- 0
for(i in 1:nrow(PS4)){
  if(PS4$month[i] == "09"){
    PS4$cum.days[i] <- 0}
  if(PS4$month[i] == "10"){
    PS4$cum.days[i] <- 30}
}

PS4$TOE <- (as.numeric(as.character(PS4$day))-17)*24 + (PS4$cum.days*24) + as.numeric(as.character(PS4$hour))-22
PS4

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

## Warning: package 'ggplot2' was built under R version 4.3.3

lgraph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm, group = Tray)) 
lgraph <- lgraph + geom_line(alpha = 0.6) 
lgraph <- lgraph + ylab("Maximum Quantum Yield (Fv/Fm)") + xlab("Hours of Imaging")
lgraph

This is wrong - bc FvFm cannot be below 0.70 - so let’s use that to trim the data to appropriate timeline:

PS5 <- subset(PS4, PS4$Fv.Fm > 0.7)

lgraph <- ggplot(data=PS5, aes(x= TOE, y=Fv.Fm, group = Tray)) 
lgraph <- lgraph + geom_line(alpha = 0.2) 
lgraph <- lgraph + ylab("Maximum Quantum Yield (Fv/Fm)") + xlab("Hours of Imaging")
lgraph

decoding the frames:

unique(PS5$Tray)

## [1] "41914" "41934"

PS5

decode <- read.csv("decode_soil.csv")
PS6 <- merge(PS5, decode, by = c("Tray", "Roi.No"), all= T)
PS6

PS6 <- PS6[,c(1:5, 25:27, 6:20)]
PS6$Plant.ID <- paste(PS6$Tray, PS6$Roi.No, sep="_")
PS6

ok - now let’s plot the data to be compared to one another:

FvFm_graph <- ggplot(data=PS6, aes(x= TOE, y=Fv.Fm, group = Plant.ID, color = species)) 
FvFm_graph <- FvFm_graph + geom_line(alpha = 0.7) 
FvFm_graph <- FvFm_graph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= species), alpha=0.3)
FvFm_graph <- FvFm_graph + stat_summary(fun=mean, aes(group= species),  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.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

#FvFm_graph <- FvFm_graph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = TRUE)
FvFm_graph <- FvFm_graph + ylab("Fv/Fm (a.u.)") + xlab("Time of imaging (h)")
FvFm_graph

OK - so as you see - this graph is extremely messy. Let’s try to separate it into three comparisons we are interested in making. For this - we have to make a new column for the genotype

unique(PS6$Genotype)

##  [1] "empty" "2.C"   "5.C"   "8.D"   "3.C"   "6.D"   "10.D"  "1.C"   "9.D"  
## [10] "4.C"   "7.D"   "2.A"   "5.A"   "8.B"   "3.A"   "6.B"   "10.B"  "1.A"  
## [19] "9.B"   "4.A"   "7.B"

PS6$Genotype_clean <- PS6$Genotype
PS6$Genotype_clean <- gsub("1.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("2.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("3.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("4.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("5.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("6.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("7.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("8.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("9.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub("10.", "", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub(".B", "B", PS6$Genotype_clean)
PS6$Genotype_clean <- gsub(".D", "D", PS6$Genotype_clean)
PS7 <- subset(PS6, PS6$Genotype != "empty")
PS7

FvFm_graph <- ggplot(data=PS7, aes(x= TOE, y=Fv.Fm, group = Plant.ID, color = Genotype_clean)) 
FvFm_graph <- FvFm_graph + geom_line(alpha = 0.7) + facet_wrap(~ species)
FvFm_graph <- FvFm_graph + stat_summary(fun.data = mean_se, geom="ribbon", linetype=0, aes(group= Genotype_clean), alpha=0.3)
FvFm_graph <- FvFm_graph + stat_summary(fun=mean, aes(group= Genotype_clean),  size=0.7, geom="line", linetype = "dashed")
#FvFm_graph <- FvFm_graph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = TRUE)
FvFm_graph <- FvFm_graph + ylab("Fv/Fm (a.u.)") + xlab("Time of imaging (h)")
FvFm_graph

OK - this is better - but still a little messy. As you can see - there are some lines that are not exactly the same timepoints - so we will get in trouble when calculating averages. Therefore - we need to do splines!

Splines

So - let’s first isolate one plant to establish our spline calculations

max(PS7$TOE)

## [1] 706

hours <- seq(0, 706, by = 12)
length(hours)

## [1] 59

hours

##  [1]   0  12  24  36  48  60  72  84  96 108 120 132 144 156 168 180 192 204 216
## [20] 228 240 252 264 276 288 300 312 324 336 348 360 372 384 396 408 420 432 444
## [39] 456 468 480 492 504 516 528 540 552 564 576 588 600 612 624 636 648 660 672
## [58] 684 696

temp <- subset(PS7, PS7$Plant.ID == unique(PS7$Plant.ID)[1])
temp$TOE <- as.numeric(as.character(temp$TOE))
temp <- temp[order(temp$TOE, decreasing = F),]
plot.spl <- with(temp, smooth.spline(TOE, Fv.Fm, df = 32))
plot(Fv.Fm ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

temp

plot.spl <- with(temp, smooth.spline(TOE, Fq..Fm., df = 32))
plot(Fq..Fm. ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, NPQ, df = 32))
plot(NPQ ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, qP, df = 32))
plot(qP ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, qN, df = 32))
plot(qN ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, qL, df = 32))
plot(qL ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, qE, df = 32))
plot(qE ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, qI, df = 32))
plot(qI ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, phi.no, df = 32))
plot(phi.no ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, phi.npq, df = 32))
plot(phi.npq ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, npq.t., df = 32))
plot(npq.t. ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, ChlIdx, df = 32))
plot(ChlIdx ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, AriIdx, df = 32))
plot(AriIdx ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

plot.spl <- with(temp, smooth.spline(TOE, NDVI, df = 32))
plot(NDVI ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

temp

plot.spl <- with(temp, smooth.spline(TOE, Roi.Mask, df = 32))
plot(Roi.Mask ~ TOE, data = temp)
lines(plot.spl, col = "blue")
lines(predict(plot.spl, hours), col = "red")

Then - let’s save all of the information into one file

names <- c(text = "Plant.ID", "Genotype", "comp", "TOE", "Roi.Mask","Fv.Fm", "Fq.Fm", "NPQ", "qP", "qN", "qL", "qE", "qI", "phiNO", "phiNPQ", "npq.t", "ChlIdx", "AriIdx", "NDVI")
spline_data <- data.frame()
for (k in names) {
  spline_data[[k]] <- as.character()}
pred_temp <- predict(plot.spl, hours)
length(pred_temp$x)

## [1] 59

temp

dim(temp)

## [1] 38 25

spline_data[1:59,1] <- temp$Plant.ID[1]
spline_data[1:59,2] <- temp$Genotype_clean[1]
spline_data[1:59,3] <- temp$species[1]
spline_data[1:59,4] <- pred_temp$x

plot.spl <- with(temp, smooth.spline(TOE, Roi.Mask, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,5] <- pred_temp$y
spline_data

plot.spl <- with(temp, smooth.spline(TOE, Fv.Fm, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,6] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, Fq..Fm., df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,7] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, NPQ, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,8] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, qP, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,9] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, qN, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,10] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, qL, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,11] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, qE, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,12] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, qI, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,13] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, phi.no, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,14] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, phi.npq, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,15] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, npq.t., df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,16] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, ChlIdx, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,17] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, AriIdx, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,18] <- pred_temp$y

plot.spl <- with(temp, smooth.spline(TOE, NDVI, df = 32))
pred_temp <- predict(plot.spl, hours)
spline_data[1:59,19] <- pred_temp$y

final_spline <- spline_data
final_spline

all_plants <- unique(PS7$Plant.ID)
all_plants

##  [1] "41914_10" "41914_12" "41914_14" "41914_16" "41914_18" "41914_2" 
##  [7] "41914_20" "41914_4"  "41914_6"  "41914_8"  "41934_10" "41934_12"
## [13] "41934_14" "41934_16" "41934_18" "41934_2"  "41934_20" "41934_4" 
## [19] "41934_6"  "41934_8"

for(i in 2:20){
    temp <- subset(PS7, PS7$Plant.ID == all_plants[i])
    
   if (dim(temp)[1] > 3) {
        spline_data[1:59,1] <- temp$Plant.ID[1]
        spline_data[1:59,2] <- temp$Genotype_clean[1]
        spline_data[1:59,3] <- temp$species[1]
        temp$TOE <- as.numeric(as.character(temp$TOE))
        temp <- temp[order(temp$TOE, decreasing = F),]
        plot.spl <- with(temp, smooth.spline(TOE, Roi.Mask, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,4] <- pred_temp$x
        spline_data[1:59,5] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, Fv.Fm, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,6] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, Fq..Fm., df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,7] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, NPQ, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,8] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, qP, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,9] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, qN, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,10] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, qL, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,11] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, qE, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,12] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, qI, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,13] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, phi.no, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,14] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, phi.npq, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,15] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, npq.t., df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,16] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, ChlIdx, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,17] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, AriIdx, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,18] <- pred_temp$y
        plot.spl <- with(temp, smooth.spline(TOE, NDVI, df = 32))
        pred_temp <- predict(plot.spl, hours)
        spline_data[1:59,19] <- pred_temp$y
        final_spline <- rbind(final_spline, spline_data)
  } else {
        spline_data[1:59,1] <- temp$Plant.ID[1]
        spline_data[1:59,2] <- temp$Genotype_clean[1]
        spline_data[1:59,3] <- temp$species[1]
        spline_data[1:59,4] <- hours
        spline_data[1:59,5] <- 0
        spline_data[1:59,6] <- 0
        spline_data[1:59,7] <- 0
        spline_data[1:59,8] <- 0
        spline_data[1:59,9] <- 0
        spline_data[1:59,10] <- 0
        spline_data[1:59,11] <- 0
        spline_data[1:59,12] <- 0
        spline_data[1:59,13] <- 0
        spline_data[1:59,14] <- 0
        spline_data[1:59,15] <- 0
        spline_data[1:59,16] <- 0
        spline_data[1:59,17] <- 0
        spline_data[1:59,18] <- 0
        spline_data[1:59,19] <- 0
        final_spline <- rbind(final_spline, spline_data)
  }}

final_spline

final_spline2 <- final_spline
final_spline2[4:19] <- sapply(final_spline2[4:19],as.numeric)
final_spline2

Plotting spline data

FvFm

library(ggsci)

## Warning: package 'ggsci' was built under R version 4.3.3

C1_lgraph <- ggplot(data = final_spline2, aes(x = TOE, y = Fv.Fm, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 2)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "Fv/Fm (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Maximum Quantum Yield of Photosystem II")
C1_lgraph

I think I lost my “compare” with multiple times wild type - let’s re-instate it:

fs2 <- subset(final_spline2, final_spline2$Fv.Fm < 0.85) 
fs2 <- subset(fs2, fs2$Fq.Fm < 0.4) 
fs2 <- subset(fs2, fs2$Fv.Fm > 0.6)
fs2 <- subset(fs2, fs2$Fq.Fm > 0) 
fs2 <- subset(fs2, fs2$NPQ > 0)
fs2 <- subset(fs2, fs2$TOE > 48)

unique(fs2$TOE)

##  [1] 168 180 192 204 216 228 240 252 264 276 288 300 312 324 336 348 360 372 384
## [20] 396 408 420 432 444 456 468 480 492 504 516 528 540 552 564 576 588 600 612
## [39] 624 636 648 660 672 684 696  60  72  84  96 108 120 132 144 156

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = Fv.Fm, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 2)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "Fv/Fm (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Maximum Quantum Yield of Photosystem II")
C1_lgraph

OK - cool - let’s add the p-value plots to this graph:

library("dplyr")

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library("cowplot")

get_p_value <- function(fs2) {
  t.test(Fv.Fm ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `p_value = get_p_value(cur_data())`.
## ℹ In group 1: `TOE = 60` `comp = "Brachypodium"`.
## Caused by warning:
## ! `cur_data()` was deprecated in dplyr 1.1.0.
## ℹ Please use `pick()` instead.

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("FvFm_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")
dev.off()

## quartz_off_screen 
##                 2

FqFm

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = Fq.Fm, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "Fq'/Fm' (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Operational Quantum Yield of Photosystem II")
C1_lgraph

get_p_value <- function(fs2) {
  t.test(Fq.Fm ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("FqFm_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")
dev.off()

## quartz_off_screen 
##                 2

NPQ

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = NPQ, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "NPQ (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Non-photochemical quenching ((Fm-Fm’)/Fm’)")
C1_lgraph

get_p_value <- function(fs2) {
  t.test(NPQ ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("NPQ_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

## quartz_off_screen 
##                 2

qP

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = qP, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "qP (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Fraction of Open Reaction Centers (puddle mode) / photochemical quenching (Fm’ – Fs’)/(Fm’ – F0’)")
C1_lgraph

get_p_value <- function(fs2) {
  t.test(qP ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("qP_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

## quartz_off_screen 
##                 2

qN

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = qN, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "qN (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("non-photochemical quenching (1 – (Fm’ – F0’)/(Fm-F0))")

get_p_value <- function(fs2) {
  t.test(qN ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("qN_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

## quartz_off_screen 
##                 2

qL

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = qL, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "qL (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Fraction of Open Reaction Centers (lake mode) / photochemical quenching (qP*F0’/Fs’)")

get_p_value <- function(fs2) {
  t.test(qL ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("qL_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

## quartz_off_screen 
##                 2

qI

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = qI, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "qI (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("slow relaxing component of NPQ (Fm-Fm’’)/Fm’’")

get_p_value <- function(fs2) {
  t.test(qI ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("qI_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

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##                 2

phiNO

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = phiNO, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "ɸNO (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("ɸNO – QY of non-regulated energy dissipation = 1/(NPQ+1+qI*Fm/F0)")

get_p_value <- function(fs2) {
  t.test(phiNO ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("phiNO_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

## quartz_off_screen 
##                 2

phiNPQ

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = phiNPQ, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "ɸNPQ (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("ɸNPQ – QY of NPQ (regulated energy dissipation) = 1-Fq’/Fm’- ɸNO")

get_p_value <- function(fs2) {
  t.test(phiNPQ ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("phiNPQ_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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npq(t)

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = npq.t, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "NPQ(t) (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("NPQ(t) – fast NPQ = 4.88/(Fm’/F0’-1)-1")

get_p_value <- function(fs2) {
  t.test(npq.t ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("NPQ(t)_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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ChlIdx

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = ChlIdx, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "ChlIdx (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Chlorophyll Index = (R769 – R710)/(R769 + R710)")

get_p_value <- function(fs2) {
  t.test(ChlIdx ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("ChlIdx_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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##                 2

AriIdx

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = AriIdx, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "AriIdx (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Anthocyanin Index = R769*((1/R540) – 1(R710))")

get_p_value <- function(fs2) {
  t.test(AriIdx ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("AriIdx_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

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dev.off()

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NDVI

fs2

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = NDVI, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "NDVI (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Normalized Difference Vegetation Index (NDVI)")

get_p_value <- function(fs2) {
  t.test(NDVI ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("NDVI_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")
dev.off()

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Area

C1_lgraph <- ggplot(data = fs2, aes(x = TOE, y = Roi.Mask, group = Plant.ID, color = Genotype)) 
C1_lgraph <- C1_lgraph + geom_line(alpha = 0.1) + facet_wrap(~ comp, ncol = 3)
C1_lgraph <- C1_lgraph + stat_summary(fun.data = mean_se, geom = "ribbon", linetype = 0, aes(group = Genotype), alpha = 0.3) 
C1_lgraph <- C1_lgraph + stat_summary(fun = mean, aes(group = Genotype), size = 0.7, geom = "line", linetype = "dashed") 
#C1_lgraph <- C1_lgraph + stat_compare_means(aes(group = Genotype), label = "p.signif", method = "t.test", hide.ns = F) 
C1_lgraph <- C1_lgraph + labs(x = "", y = "Digital Biomass (a.u.)") + scale_color_d3("category10") + theme(legend.position = "top") + ggtitle("Projected Rosette Area")

get_p_value <- function(fs2) {
  t.test(Roi.Mask ~ Genotype, data = fs2)$p.value
}
p_values <- fs2 %>%
  group_by(TOE, comp) %>%
  summarise(p_value = get_p_value(cur_data())) %>%
  ungroup()

## `summarise()` has grouped output by 'TOE'. You can override using the `.groups`
## argument.

p_values$LOD <- -log10(p_values$p_value)

C1_pplot <- ggplot(p_values, aes(x = TOE, y = LOD)) 
C1_pplot <- C1_pplot + geom_line() + geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") + facet_wrap(~ comp, ncol = 3)
C1_pplot <- C1_pplot + labs(x = "Hours Of Imaging", y = "-log10(p-value)") 
C1_pplot <- C1_pplot + theme_minimal() + theme_bw() + theme(strip.text = element_blank(), strip.background = element_blank()) 

plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")

OK - let’s save it and move on to the next trait:

pdf("RosetteArea_graph.pdf")
plot_grid(C1_lgraph, C1_pplot, rel_heights = c(4,1), ncol = 1, align = "v", axis = "l")
dev.off()

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