A novel gas phase separation based MS acquisition method could improve the specificity and sensitivity of Orbitrap-based DIA analysis.
This script can generate a customized PulseDIA isolation window scheme.
#----------------------------- Please modify the parameters ---------------------------------------------------
wins_type <- "Fixed" #Fixed or Variable
pulse_num <- 4
out_name <- "BRP_4_part"
out_filename_MS <- "4/MS"
out_filename_Cirt <- "4/Cirt"
overlap <- FALSE # TRUE or FALSE
mz_start <- 400
mz_end <- 1200
win_num <- 24
input_name <- "BRP_QE_peptides.txt"
#----------------------------------------------------------------------------------------------------------------
wins_type: type of the designed window width (default: Fixed
; valid: Fixed
, Variable
), Fixed
means the experiment with a fixed width window, Variable
means the experiment with variable width windows that designed according to the ion density.
pulse_num: number of injections
out_name: the name of output file
out_filename_MS: the output folder name. The generated files in this folder will be used for mass spectrometry
out_filename_Cirt: the output folder name. The generated files in this folder will be used to build the windows file of CiRT for OpenSWATH analysis.
overlap: Whether the windows have overlap(default: FALSE
;valid: TRUE
, FALSE
), FALSE
means 1 Thomas overlap between two adjacent windows; TRUE
means half width overlap between two adjacent windows
mz_start: The start m/z of MS1 acquisition range
mz_end: The end m/z of MS1 acquisition range
win_num: The number of isolation windows, default is 24 Windows
input_name: precursor ion intensity file for special samples, window width designed according to the precursor ion density from this file
This script is used to extract peptide or protein quantification results from DIA-NN output
df <- read.delim("report.tsv")
bb <- which(df$Precursor.Quantity==0)
df <- df[-bb,]
df1 <- data.frame(sample=df$File.Name,pep=df$Stripped.Sequence,prot=df$Protein.Ids,intesity=df$Precursor.Quantity)
df2 <- dcast(df1,pep+prot~sample,value.var = 'intesity',mean)
sum_dia <- apply(df2,2,function(x) sum(!is.na(x)))
write.table(df2,file="diann_CCA_pd2_pep20191206.txt",sep="\t",col.names = T,row.names = F,quote = F)
The code above is used to extract peptide quantitative results and export them
df <- read.delim("report.tsv")
bb <- which(df$Precursor.Quantity==0)
df <- df[-bb,]
#df <- df[-which(df$Protein.Ids==""),]
df1 <- data.frame(sample=df$File.Name,prot=df$Protein.Ids,intesity=df$PG.Quantity)
df1 <- unique(df1)
library(reshape2)
df2 <- dcast(df1,prot~sample,value.var = 'intesity',mean)
sum_dia <- apply(df2,2,function(x) sum(!is.na(x)))
write.table(df2,file="diann_CCA_pd2_prot20191206.txt",sep="\t",col.names = T,row.names = F,quote = F)
The code above is used to extract protein quantitative results and export them
The script could to combine peptides or proteins quantitative results from multiple PulseDIA injections for the same sample.
overlap_calcu <- "mean" ## "mean" or "max"
nm <- as.character(sapply(colnames(df)[-(1:2)],function(v) {str_split(v,"\\.")[[1]][6]}))
nm <- as.character(sapply(nm,function(v){str_split(v,"_part")[[1]][1]}))
The code above is to keep the filenames of different parts consistent. This is the only part for users to change according the actual filenames.
overlap_calcu: the combined method for calculating the values of overlapping Windows (default: mean
; valid: mean
, max
), mean
means that the values are combined by averaging, max
represents a combination of values by maximizing them.