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MJO_Lorne_Proteomics_2017_R_script.R
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MJO_Lorne_Proteomics_2017_R_script.R
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###############################
# MJO
# Lorne Proteomics 2017
# motway@cmri.org.au
# R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch"
# Bioconductor version 3.4 (BiocInstaller 1.24.0)
# Platform: x86_64-apple-darwin13.4.0 (64-bit)
###############################
#install.packages("VennDiagram")
library(VennDiagram)
#install.packages("Peptides")
library(Peptides)
#install.packages("fBasics")
library(fBasics)
# source("http://bioconductor.org/biocLite.R")
# biocLite("plyr")
library(plyr)
###########################
# Import data frames for peptides and FDR
pep.all <-
read.table(
"/Users/madeleineotway/Desktop/MJO_Lorne_Proteomics_2017_peptide_areas.txt",
sep = "\t", header = T)
fdr.all <-
read.table(
"/Users/madeleineotway/Desktop/MJO_Lorne_Proteomics_2017_FDR.txt",
sep = "\t", header = T)
# Create and rename columns in new data frame
pep.lmh <- data.frame("protein" = pep.all[,1],
"peptide" = pep.all[,2],
"precursor" = pep.all[,3],
"low.1" = pep.all[,6],
"low.2" = pep.all[,7],
"low.3" = pep.all[,8],
"low.4" = pep.all[,9],
"low.5" = pep.all[,10],
"low.6" = pep.all[,11],
"med.1" = pep.all[,12],
"med.2" = pep.all[,13],
"med.3" = pep.all[,14],
"med.4" = pep.all[,15],
"med.5" = pep.all[,16],
"med.6" = pep.all[,17],
"high.1" = pep.all[,18],
"high.2" = pep.all[,19],
"high.3" = pep.all[,20],
"high.4" = pep.all[,21],
"high.5" = pep.all[,22],
"high.6" = pep.all[,23])
# Create and rename columns in new data frame
fdr.lmh <- data.frame("protein" = fdr.all[,1],
"peptide" = fdr.all[,2],
"precursor" = fdr.all[,4],
"low.1" = fdr.all[,8],
"low.2" = fdr.all[,9],
"low.3" = fdr.all[,10],
"low.4" = fdr.all[,11],
"low.5" = fdr.all[,12],
"low.6" = fdr.all[,13],
"med.1" = fdr.all[,14],
"med.2" = fdr.all[,15],
"med.3" = fdr.all[,16],
"med.4" = fdr.all[,17],
"med.5" = fdr.all[,18],
"med.6" = fdr.all[,19],
"high.1" = fdr.all[,20],
"high.2" = fdr.all[,21],
"high.3" = fdr.all[,22],
"high.4" = fdr.all[,23],
"high.5" = fdr.all[,24],
"high.6" = fdr.all[,25],
"Decoy" = fdr.all[,7])
# Create new data frame with all non decoy proteins
fdr.decoy <- fdr.lmh[!(fdr.lmh$Decoy=="True"),]
# Match proteins, peps and precursors between FDR and pep lists
fdr.decoy$fdr.pep <-
ifelse(
fdr.decoy$protein %in% pep.lmh$protein &
fdr.decoy$peptide %in% pep.lmh$peptide &
fdr.decoy$precursor %in% pep.lmh$precursor, 1, 0)
# Create new data frame with only the rows that match between FDR and pep lists
fdr.match <- fdr.decoy[!(fdr.decoy$fdr.pep == "0"),]
# Sort data frames by protein, then pep, then precursor
sort.fdr <- arrange(fdr.match, protein, peptide, precursor)
sort.pep <- arrange(pep.lmh, protein, peptide, precursor)
# Create new data frame for peptide FDR
pep.fdr.01 <- sort.pep
# Function for FDR calculation at 1%
fdr.01 <- function(x, y, table.in) {
# create pep.fdr.01 from sort.pep
# table.in = pep.fdr.01
# x= first sample vector
# y= last sample vector
table.in <- pep.fdr.01
table.in [,x] <- ifelse(sort.fdr[,x] <= 0.01, sort.pep[,x], NA)
pep.fdr.01 <- table.in
for (i in x:y) {
table.in <- pep.fdr.01
table.in [,i] <- ifelse(sort.fdr[,i] <= 0.01, sort.pep[,i], NA)
pep.fdr.01 <- table.in
}
return(pep.fdr.01)
}
pep.fdr.01 <- fdr.01(4, 21, pep.fdr.01)
# New data frame per group, with group specific missing values removed
low.mv <-
pep.fdr.01[(rowSums(is.na(pep.fdr.01[4:9])) <= 0) ,]
low.mv[10:21] <- NULL
med.mv <-
pep.fdr.01[(rowSums(is.na(pep.fdr.01[10:15])) <= 0) ,]
med.mv[16:21] <- NULL
med.mv[4:9] <- NULL
high.mv <-
pep.fdr.01[(rowSums(is.na(pep.fdr.01[16:21])) <= 0) ,]
high.mv[4:15] <- NULL
# Calculate and remove all peptides with a CV >20%
low.mv$mean <- rowMeans(low.mv[4:9])
low.mv$sd <- rowStdevs(low.mv[4:9])
low.mv$cv <- (low.mv$sd / low.mv$mean * 100)
low.mv$cv.20 <- ifelse ((low.mv$cv < 20), 0, 1)
med.mv$mean <- rowMeans(med.mv[4:9])
med.mv$sd <- rowStdevs(med.mv[4:9])
med.mv$cv <- (med.mv$sd / med.mv$mean * 100)
med.mv$cv.20 <- ifelse ((med.mv$cv < 20), 0, 1)
high.mv$mean <- rowMeans(high.mv[4:9])
high.mv$sd <- rowStdevs(high.mv[4:9])
high.mv$cv <- (high.mv$sd / high.mv$mean * 100)
high.mv$cv.20 <- ifelse ((high.mv$cv < 20), 0, 1)
low.cv.20 <- data.frame(low.mv[low.mv$cv.20 != 1,])
med.cv.20 <- data.frame(med.mv[med.mv$cv.20 != 1,])
high.cv.20 <- data.frame(high.mv[high.mv$cv.20 != 1,])
# Venn diagram of proteins
pro.venn.list <-
list("Low (2 μg)" = low.cv.20$protein,
"Medium (4 μg)" = med.cv.20$protein,
"High (6 μg)" = high.cv.20$protein)
venn.diagram(
pro.venn.list,
filename = "/Users/madeleineotway/Desktop/Pro_Venn_cv_pc.png",
imagetype = "png",
print.mode = "percent",
sigdigs = 1,
fill = c("darkseagreen3", "lightgoldenrod2", "pink2"),
alpha = 0.5,
cex = 1.5,
cat.col = "black",
cat.cex = 1.5,
cat.fontface = "bold",
cat.default.pos = "text",
cat.dist = c(0.04, 0.04, 0.03),
cat.pos = 0 )
# Venn diagram of peptides
pep.venn.list <-
list("Low (2 μg)" = low.cv.20$peptide,
"Medium (4 μg)" = med.cv.20$peptide,
"High (6 μg)" = high.cv.20$peptide)
venn.diagram(
pep.venn.list,
filename = "/Users/madeleineotway/Desktop/Pep_Venn_cv_pc.png",
imagetype = "png",
print.mode = "percent",
sigdigs = 1,
fill = c("darkseagreen3", "lightgoldenrod2", "pink2"),
alpha = 0.5,
cex = 1.5,
cat.col = "black",
cat.cex = 1.5,
cat.fontface = "bold",
cat.default.pos = "text",
cat.dist = c(0.04, 0.04, 0.03),
cat.pos = 0 )
# New data frame with the frequency of peptides per protein
low.pro.count <- count(low.cv.20$protein)
low.pro.freq <- data.frame(pep.per.pro = c(1, 2, 3, 4, 5, 6),
freq = c((length(which(low.pro.count$freq == 1))),
(length(which(low.pro.count$freq == 2))),
(length(which(low.pro.count$freq == 3))),
(length(which(low.pro.count$freq == 4))),
(length(which(low.pro.count$freq == 5))),
(length(which(low.pro.count$freq == 6))) ))
med.pro.count <- count(med.cv.20$protein)
med.pro.freq <- data.frame(pep.per.pro = c(1, 2, 3, 4, 5, 6),
freq = c((length(which(med.pro.count$freq == 1))),
(length(which(med.pro.count$freq == 2))),
(length(which(med.pro.count$freq == 3))),
(length(which(med.pro.count$freq == 4))),
(length(which(med.pro.count$freq == 5))),
(length(which(med.pro.count$freq == 6))) ))
high.pro.count <- count(high.cv.20$protein)
high.pro.freq <- data.frame(pep.per.pro = c(1, 2, 3, 4, 5, 6),
freq = c((length(which(high.pro.count$freq == 1))),
(length(which(high.pro.count$freq == 2))),
(length(which(high.pro.count$freq == 3))),
(length(which(high.pro.count$freq == 4))),
(length(which(high.pro.count$freq == 5))),
(length(which(high.pro.count$freq == 6))) ))
# Plot the disriputiopn of the proteins
png(file="/Users/madeleineotway/Desktop/Pep_per_Pro_Freq.png",
width = 6400,
height = 6400,
units = "px",
res = 1600)
par(bty = "l")
plot(x = c(low.pro.freq$pep.per.pro,
med.pro.freq$pep.per.pro,
high.pro.freq$pep.per.pro),
y = c(low.pro.freq$freq,
med.pro.freq$freq,
high.pro.freq$freq),
col = c(rep("seagreen", 6), rep("goldenrod", 6), rep("hotpink", 6)),
pch = 19, cex = 1.5,
xlab = "# Peptides/Protein",
ylab = "Frequency")
dev.off()