Experiment 100 processing
Data processing pipeline for Experiment 100 data collected on Crop Reporter camera within BTI’s PhenoSight facility.
list.files()## [1] "Exp00089_ExpDesign.tsv" "Exp00089_Pipeline.html"
## [3] "Exp00089_Pipeline.nb.html" "Exp00089_Pipeline.Rmd"
## [5] "Exp00089_plots.pdf" "Exp00089_PS2_Analysis_new.TXT"
## [7] "Exp00089_PS2_Analysis.TXT" "Exp00092_ExpDesign.tsv"
## [9] "Exp00092_Pipeline.html" "Exp00092_Pipeline.Rmd"
## [11] "Exp00092_plots.pdf" "Exp00092_PS_analysis_new.TXT"
## [13] "Exp00092_PS_analysis.TXT" "Exp00092_PS2_Analysis_new.TXT"
## [15] "Exp00094_ExpDesign.tsv" "Exp00094_Pipeline.html"
## [17] "Exp00094_Pipeline.Rmd" "Exp00094_plots.pdf"
## [19] "Exp00094_PS2_Analysis_clean.TXT" "Exp00094_PS2_Analysis_new.TXT"
## [21] "Exp00095_ExpDesign.tsv" "Exp00095_PS2_Analysis_clean.TXT"
## [23] "Exp00096_ExpDesign.tsv" "Exp00096_Pipeline.html"
## [25] "Exp00096_Pipeline.Rmd" "Exp00096_plots.pdf"
## [27] "Exp00096_PS2_Analysis_clean.TXT" "Exp00096_PS2_Analysis_new.TXT"
## [29] "Exp010098_ExpDesign.tsv" "Exp010098_Pipeline.html"
## [31] "Exp010098_Pipeline.Rmd" "Exp010098_plots.pdf"
## [33] "Exp010098_PS2_Analysis_clean.TXT" "Exp010098_PS2_Analysis_new.TXT"
## [35] "Exp010099_ExpDesign.tsv" "Exp010099_Pipeline.html"
## [37] "Exp010099_Pipeline.Rmd" "Exp010099_plots.pdf"
## [39] "Exp010099_PS2_Analysis_clean.TXT" "Exp010099_PS2_Analysis_new.TXT"
## [41] "Exp010100_ExpDesign.tsv" "Exp010100_Pipeline.Rmd"
## [43] "Exp010100_plots.pdf" "Exp010100_PS2_Analysis_clean.TXT"
## [45] "Exp010100_PS2_Analysis_new.TXT" "Exp010102_ExpDesign.tsv"
## [47] "Exp010102_PS2_Analysis_clean.TXT" "Exp010103_ExpDesign.tsv"
## [49] "Exp010103_PS2_Analysis_clean.TXT" "Exp010104_ExpDesign.tsv"
## [51] "Exp010104_PS2_Analysis_clean.TXT" "Exp010105_ExpDesign.tsv"
## [53] "Exp010105_PS2_Analysis_clean.TXT" "Exp010108_ExpDesign.tsv"
## [55] "Exp010108_PS2_Analysis_clean.TXT" "Exp010109_ExpDesign.tsv"
## [57] "Exp010109_PS2_Analysis_clean.TXT" "Exp010111_PS2_Analysis_clean.TXT"
## [59] "Exp010113_PS2_Analysis.TXT" "Exp010134_ExpDesign.tsv"
## [61] "Exp010134_PS2_Analysis.TXT" "Exp010136_ExpDesign.tsv"
## [63] "Exp010136_PS2_Analysis.TXT" "Exp010190A_PS2_Analysis_clean.TXT"
## [65] "Exp010190B_PS2_Analysis.TXT" "Exp010190C_PS2_Analysis_clean.TXT"
## [67] "Exp010191_ExpDesign.txt" "Exp010191_PS2_Analysis_new.TXT"
## [69] "Exp10214_PS2_Analysis_clean.TXT" "Exp10215_PS2_Analysis_clean.TXT"
## [71] "Exp10217_PS2_Analysis.TXT" "Exp10218_PS2_Analysis.TXT"
## [73] "rsconnect"#install.packages("data.table")
library(data.table)## Warning: package 'data.table' was built under R version 4.3.3data <- fread("Exp010100_PS2_Analysis_clean.TXT", sep = "\t")
datacolnames(data)## [1] "File" "Date" "Time" "Obj.No"
## [5] "nTmPam" "Obj.Size" "Obj.Xc" "Obj.Yc"
## [9] "Fv/Fm" "s.d." "Fq'/Fm'" "s.d."
## [13] "NPQ" "s.d." "F0'" "s.d."
## [17] "qP" "s.d." "qN" "s.d."
## [21] "qL" "s.d." "qE" "s.d."
## [25] "qI" "s.d." "фno" "s.d."
## [29] "фnpq" "s.d." "npq(t)" "s.d."
## [33] "ChlIdx" "s.d." "AriIdx" "s.d."
## [37] "NDVI" "s.d." "Border" "Mask/Border"
## [41] "Points" "Area (CH)" "Mask/Area (CH)" "X-Centre"
## [45] "Y-Centre" "Radius" "Area (MC)" "Mask/Area (MC)"
## [49] "Width" "Height" "Area (MR)" "Mask/Area (MR)"
## [53] "Alpha" "Size (SK)" "Junction (SK)" "Endpoint (SK)"
## [57] "Path (SK)" "V58"PS <- subset(data, data$Obj.No == "All")
PSPS1 <- subset(PS, PS$nTmPam == 1)
PS2 <- subset(PS, PS$nTmPam == 2)
PS1PS2PS1 <- PS1[,c(1:4, 6, 9)]
PS1PS2 <- PS2[,c(1:4, 6, 11, 13, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37)]
PS2PS_extra <- subset(PS, PS$nTmPam > 2)
PS_extra <- PS_extra[,c(1:6, 9, 11, 13, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37)]
PS_extra <- PS_extra[,c(1:7)]
PS_extraPS_melt <- melt(PS_extra, id.vars = c("File", "Date", "Time", "Obj.No", "Obj.Size", "nTmPam"))
PS_meltPS_cast <- dcast(PS_melt, File + Date + Time + Obj.No + Obj.Size ~ variable + nTmPam)
PS_castPS3 <- merge(PS1, PS_cast, by = c("File", "Date", "Time", "Obj.No", "Obj.Size"))
PS4 <- merge(PS3, PS2, by = c("File", "Date", "Time", "Obj.No", "Obj.Size"))
PS4for(i in 1:nrow(PS4)){
PS4$PhenoTray.ID[i] <- strsplit(PS4$File[i], "_")[[1]][3]
}
length(unique(PS4$PhenoTray.ID))## [1] 16for(i in 1:nrow(PS4)){
PS4$month <- substr(PS4$Date, 5, 6)
PS4$day <- substr(PS4$Date, 7, 8)
PS4$hour <- substr(PS4$Time, 1, 2)
}
PS4unique(PS4$month)## [1] "05"temp <- subset(PS4, PS4$month == "05")
min(temp$day)## [1] "31"temp <- subset(temp, temp$day == "31")
min(temp$hour)## [1] "12"PS4$cum.days <- 0
for(i in 1:nrow(PS4)){
if(PS4$month[i] == "05"){
PS4$cum.days[i] <- 0}
if(PS4$month[i] == "06"){
PS4$cum.days[i] <- 31}
}
PS4so first day of the experiment is May 31 and the measurements are starting around 12:00.
Let’s calculate Time of Experiment (TOE).
PS4$TOE <- (as.numeric(as.character(PS4$day)) - 31)*24 + (PS4$cum.days*24) + (as.numeric(as.character(PS4$hour)) - 12)
unique(PS4$TOE)## [1] 0 1PS4PS4 <- PS4[,c(1:4, 24, 29, 5:23)]
PS4There are still some odd things in the column names - that I would like to straighten out:
colnames(PS4)[8] <- "Fv.Fm"
colnames(PS4)[9] <- "Fv.Fm.3"
colnames(PS4)[10] <- "Fv.Fm.4"
colnames(PS4)[11] <- "Fv.Fm.5"
colnames(PS4)[12] <- "Fv.Fm.6"
colnames(PS4)[13] <- "Fq.Fm"
colnames(PS4)[20] <- "phi.no"
colnames(PS4)[21] <- "phi.npq"
colnames(PS4)[22] <- "npq.t"
PS4PS4 <- na.omit(PS4)
unique(PS4$PhenoTray.ID)## [1] "34835" "34836" "34837" "34838" "34839" "34840" "34841" "34842" "34843"
## [10] "34844" "34845" "34846" "34847" "34848" "34849" "34850"write.table(PS4, file = "Exp010100_PS2_Analysis_new.TXT", sep="\t", row.names = F)Data visualization
#install.packages("ggplot2")
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3FvFm_graph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm, group = PhenoTray.ID))
FvFm_graph <- FvFm_graph + geom_line(alpha = 0.7)
FvFm_graph <- FvFm_graph + ylab("Fv/Fm") + xlab("Hours of Imaging")
FvFm_graph## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
FqFm_graph <- ggplot(data=PS4, aes(x= TOE, y=Fq.Fm, group = PhenoTray.ID))
FqFm_graph <- FqFm_graph + geom_line(alpha = 0.7)
FqFm_graph <- FqFm_graph + ylab("Fq/Fm") + xlab("Hours of Imaging")
FqFm_graph## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
ChlIdx_graph <- ggplot(data=PS4, aes(x= TOE, y=ChlIdx, group = PhenoTray.ID))
ChlIdx_graph <- ChlIdx_graph + geom_line(alpha = 0.7)
ChlIdx_graph <- ChlIdx_graph + ylab("ChlIdx") + xlab("Hours of Imaging")
ChlIdx_graph## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
AriIdx_graph <- ggplot(data=PS4, aes(x= TOE, y=AriIdx, group = PhenoTray.ID))
AriIdx_graph <- AriIdx_graph + geom_line(alpha = 0.7)
AriIdx_graph <- AriIdx_graph + ylab("AriIdx") + xlab("Hours of Imaging")
AriIdx_graph## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
NDVI_graph <- ggplot(data=PS4, aes(x= TOE, y=NDVI, group = PhenoTray.ID))
NDVI_graph <- NDVI_graph + geom_line(alpha = 0.7)
NDVI_graph <- NDVI_graph + ylab("NDVI") + xlab("Hours of Imaging")
NDVI_graph## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
library(cowplot)
pdf("Exp010100_plots.pdf", height = 10, width = 7)
plot_grid(FvFm_graph, FqFm_graph, NDVI_graph, AriIdx_graph, ChlIdx_graph, labels = "AUTO", ncol = 2)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?dev.off()## quartz_off_screen
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