/
analysis_single_run.R
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analysis_single_run.R
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library(dplyr)
library(tidyverse)
library(MSstatsTMT)
library(ggfortify)
library(broom)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(data.table)
library(factoextra)
library(cluster)
# condition normalization -------------------------------------------------
args = commandArgs(trailingOnly=TRUE)
if (length(args)==0) {
stop("Specify output directory", call.=FALSE)
}
# condition normalization -------------------------------------------------
path<-args[1]
step1_path <- file.path(path, 'step1_msstats_psm.csv')
input <- read.csv(step1_path)
fn <- input %>%
dplyr::filter(!str_detect(GeneName, ";")) %>% #unique to gene, concern for P62805
dplyr::group_by(GeneName) %>%
mutate(num_pep = n_distinct(PeptideSequence, Charge)) %>%
filter(num_pep >= 1) %>%
dplyr::group_by(PSM) %>% #total intensity if same psm, across all channels
dplyr::mutate(total = sum(Intensity)) %>%
dplyr::group_by(PeptideSequence, Charge, Run) %>%
dplyr::filter(total == max(total)) %>%
dplyr::mutate(Mixture = as.factor(Mixture),
TechRepMixture = as.factor(TechRepMixture),
Channel = as.factor(Channel),
BioReplicate = as.factor(BioReplicate),
Condition = as.factor(Condition)) %>%
dplyr::select(-total) %>%
dplyr::group_by(Mixture, BioReplicate) %>%
dplyr::mutate(median_run = median(Intensity)) %>% #normalize
dplyr::group_by(Mixture, Condition) %>%
dplyr::mutate(median_condition = median(Intensity))%>%
dplyr::mutate(Intensity = Intensity / median_run * median_condition)
write.csv(fn, file.path(path,"step2_normalized_msstats.csv"), row.names = F, quote = F, na= "")
#without histone
fn_without_hist <- fn %>%
filter(!str_detect(ProteinName, ';H2A.D|;H2B.C|;H4|;xH|;H3|^H2A.D|^H2B.C|^H4|^xH|^H3'))
#excel export
exp <- read.csv(file.path(path, 'experiment.csv'))
norm_xcel <- fn %>% select("Intensity", "BioReplicate","GeneName", "ProteinName", "Mixture") %>%
dplyr::group_by(GeneName, ProteinName, BioReplicate) %>%
dplyr::mutate(total = sum(Intensity)) %>%
dplyr::select(-Intensity) %>%
pivot_wider(names_from=c("BioReplicate", "Mixture"), values_from="total", names_sep="_",
names_sort = T, values_fn = unique) %>%
dplyr::select(c("ProteinName","GeneName",exp$Class)) %>%
dplyr:: select(-contains("Norm")) %>%
group_by(GeneName, ProteinName) %>%
summarise_all(list(~ sum(., na.rm = T)))
cols <- read.csv(file.path(path, 'xcel_step1_raw_noise_corrected.csv'))
cols <- cols %>% select(GeneName, ProteinName)
norm_xcel2 <- merge(cols, norm_xcel, by =c("GeneName", "ProteinName"), all = T)
norm_xcel2 <- norm_xcel2 %>%
dplyr::mutate(MultipleGenes = str_detect(GeneName, ";")) %>%
arrange(MultipleGenes, GeneName) %>%
select(-MultipleGenes)
write.csv(norm_xcel2, file.path(path,"xcel_step2_normalized_msstats.csv"), row.names = F, quote = F, na= "")
#use gene
fn_genes <- fn_without_hist %>%
select(ProteinName, PeptideSequence, Charge, PSM,
Mixture, TechRepMixture, Run, Channel, Condition,
BioReplicate, Intensity)
prot.mstats_gene <- proteinSummarization(fn_genes,
method="msstats",
global_norm=F,
reference_norm=T, remove_norm_channel=T,
MBimpute = T, verbose = TRUE)
get_accession <- function(string) {
y <- NULL
for (i in str_split(string[[1]][1], ";")[[1]]){
if (grepl("|",i, fixed = T)){
y<-c(y, strsplit(i, "|", fixed = T)[[1]][2])}
#else{
#y=c(y,i)}
}
return(paste(y, collapse=";"))
}
prot.mstats_gene$temp2$GeneName <- lapply(unlist(prot.mstats_gene$temp2[,'Protein']), get_accession)
saveRDS(prot.mstats_gene, file = file.path(path,'step3_protein_abundance_woHist.rds'))
# profile plot ------------------------------------------------------------
# TODO: sorted alphabetically
# dataProcessPlotsTMT(prot.mstats_gene,type= "ProfilePlot",
# which.Protein = array(unique(prot.mstats_gene$ProteinLevelData$Protein)),
# address=path, legend.size=10, width = 21, height =7)
# pca ---------------------------------------------------------------------
prot_for_pca <- prot.mstats_gene$ProteinLevelData %>%
select(Abundance, Mixture, Channel, Protein) %>%
pivot_wider(names_from=c("Channel", "Mixture"), values_from="Abundance", names_sep="_", names_sort = T)
meta <- unique( select(prot.mstats_gene$ProteinLevelData, c('Condition','Mixture','Channel')) )
meta['Class'] <- str_c(meta$Channel, '_', meta$Mixture)
prot_for_pca <- prot_for_pca %>%
drop_na() %>%
column_to_rownames("Protein")
pca<-prcomp(t(prot_for_pca), center = T, scale = F)
pca_fit <- augment(pca) %>%
rename_at(vars(starts_with(".fitted")),
list(~str_replace(.,".fitted","")))
pca_fit <- merge(pca_fit, meta, by.x = ".rownames", by.y = "Class")
pca.var <- facto_summarize(pca, "var", c("coord", "contrib", "cos2"), axes = c(1,2))
colnames(pca.var)[2:3] <- c("x", "y")
pca.ind <- get_pca_ind(pca)
ind <- data.frame(pca.ind$coord[, c(1,2), drop=FALSE], stringsAsFactors = TRUE)
colnames(ind) <- c("x", "y")
r <- min(
(max(ind[,"x"])-min(ind[,"x"])/(max(pca.var[,"x"])-min(pca.var[,"x"]))),
(max(ind[,"y"])-min(ind[,"y"])/(max(pca.var[,"y"])-min(pca.var[,"y"])))
)
options(ggrepel.max.overlaps = 10)
pdf(file = file.path(path, 'PCA.pdf'))
ggplot(pca_fit, aes(x=PC1, y=PC2)) + #sometimes we need shape = Mixture
geom_point(size=3, aes(x=PC1, y=PC2, color=Condition, shape = Mixture)) +
#scale_shape_discrete(name = "Condition") +
#scale_color_discrete(guide='none') +
xlab(paste("PC1 (", summary(pca)$importance[2,1]*100, "% explained variance)", sep=""))+
ylab(paste("PC2 (", summary(pca)$importance[2,2]*100, "% explained variance)", sep=""))+
theme_clean()+
theme(panel.grid.major.y = element_blank()) +
scale_color_manual(breaks = c("WT","AP","Q24A","E56A","E56Q",
"E61A","E64A","N68A","D72A", "N89A",
"D90A","E91A","E92A","E92K","Q47A",
"E113A","E113Q"),
values=c('#000000','#ff0000','#808080','#0000ff','#00ffff',
"#ff5dff","#ffff00", "#b7b7ff","#ffbf00", "#bcffbc",
"#00bfff", "#ab7942", "#00ac00", "#00ff00","#bcbc00",
"#aa00ff", "#ffb7e7","#ffffff"))+
geom_text_repel(data = pca.var,mapping = aes( x=x*r*-0.75, y = y*r*-0.75, label = rownames(pca.var)),
color = "steelblue")
dev.off()
# other pca ---------------------------------------------------------------------
summarization <- prot.mstats_gene$ProteinLevelData %>%
select("Abundance", "Channel","Protein", "Mixture") %>%
dplyr::rename(ProteinName = Protein) %>%
pivot_wider(names_from=c("Channel", "Mixture"), values_from="Abundance", names_sep="_", names_sort = T, values_fn = unique)
names(summarization)[1] <- "GeneName"
summarization <- merge(cols, summarization, by =c("GeneName"), all = T)
summarization <- summarization %>%
dplyr::mutate(MultipleGenes = str_detect(GeneName, ";")) %>%
arrange(MultipleGenes, GeneName) %>%
select(-MultipleGenes)
write.csv(summarization, file.path(path,"xcel_step3_abundance_woHist.csv"), row.names = F, quote = F, na="")
write.csv(pca$x, file.path(path,"pca_coord.csv"), row.names = T, quote = F, na="")
write.csv(pca$rotation, file.path(path,"eigen.csv"), row.names = T, quote = F, na="")