Mercurial > repos > bornea > nsaf_scoring
view nsaf_scoring.R @ 4:9ff511e32040 draft
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author | bornea |
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date | Wed, 11 May 2016 10:53:40 -0400 |
parents | 2843d0da7f20 |
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################################################################################################### # R-code: APOSTL Global Variables # Author: Brent Kuenzi ################################################################################################### # This program performs the file merging as well as a serious of calculations # Following merging the following parameters will be calculated: # 1) CRAPomePCT # 2) NSAF # 3) NSAFscore # The resulting table will be exported. This is performed as its own tool and should not be used # for input into the interactive analysis tool or the standalone bubble graph tool ################################## Dependencies ################################################### library(dplyr); library(tidyr) ################################# Read in Data #################################################### ## REQUIRED INPUTS ## ## 1) listfile (filename) #listfile <- "EGFR_list.txt" ## 2) Prey File (filename) #preyfile <- "EGFR_prey.txt" ## 3) crapome File (filename or FALSE) #crapfile <- "EGFR_crap.txt" ## 4) Inter File (filename) #interfile <- "inter.txt" ################################# Create Table #################################################### merge_files <- function(SAINT_DF, prey_DF, crapome=FALSE) { SAINT <- read.table(SAINT_DF, sep='\t', header=TRUE) prey <- read.table(prey_DF, sep='\t', header=FALSE); colnames(prey) <- c("Prey", "Length", "PreyGene") DF <- merge(SAINT,prey) if(crapome!=FALSE) { crapome <- read.table(crapome, sep='\t', header=TRUE) colnames(crapome) <- c("Prey", "Symbol", "Num.of.Exp", "Ave.SC", "Max.SC") DF1 <- merge(DF, crapome); as.character(DF1$Num.of.Exp); DF1$Symbol <- NULL; DF1$Ave.SC <- NULL; DF1$Max.SC <- NULL #remove unnecessary columns DF1$Num.of.Exp <- sub("^$", "0 / 1", DF1$Num.of.Exp ) #replace blank values with 0 / 1 DF <- DF1 %>% separate(Num.of.Exp, c("NumExp", "TotalExp"), " / ") #split into 2 columns DF$CrapomePCT <- round(100 - (as.integer(DF$NumExp) / as.integer(DF$TotalExp) * 100), digits=2) #calculate crapome % } DF$FoldChange <- round(log2(DF$FoldChange),digits=2) colnames(DF)[(colnames(DF)=="FoldChange")] <- "log2(FoldChange)" DF$SAF <- DF$AvgSpec / DF$Length by_bait <- DF %>% group_by(Bait) %>% mutate("NSAF" = SAF/sum(SAF)) by_bait$SAF <- NULL return(by_bait[!duplicated(by_bait),]) } main <- function(listfile, preyfile, crapfile, interfile) { working <- as.data.frame(merge_files(listfile, preyfile, crapfile)) inter_df <- read.table(interfile, sep='\t', header=FALSE) working$temp <- strsplit(as.character(working$ctrlCounts),"[|]") cnt <- 0 for(i in working$temp){ cnt <- cnt+1 working$ctrl_mean[cnt] <- mean(as.numeric(unlist(i))) working$ctrl_number[cnt] <- length(i)} working$ctrl_SAF <- working$ctrl_mean / working$Length main.data <- working %>% group_by(Bait) %>% mutate("control_NSAF" = ctrl_SAF/sum(ctrl_SAF)) ctrl_SAF_constant <- 1/mean(main.data$ctrl_SAF) # add ctrl_SAF_constant to prevent dividing by 0 cnt <- 0 for(i in main.data$control_NSAF){ cnt <- cnt + 1 main.data$nsafScore[cnt] <- ((main.data$NSAF[cnt])+ctrl_SAF_constant)/((i/main.data$ctrl_number[cnt])+ctrl_SAF_constant) } main.data$NSAF <- log(main.data$NSAF) main.data$nsafScore <- log(main.data$nsafScore) main.data <- filter(main.data, NSAF > -Inf) colnames(main.data)[colnames(main.data)=="NSAF"] <- "ln(NSAF)" colnames(main.data)[colnames(main.data)=="nsafScore"] <- "NSAFScore" main.data$SAF <- NULL; main.data$ctrl_SAF <- NULL main.data$control_NSAF <- NULL; main.data$temp <- NULL main.data$ctrl_mean <- NULL write.table(main.data,file="SaintTable.txt",sep="\t",row.names=FALSE,quote=FALSE) } args <- commandArgs(trailingOnly = TRUE) main(args[1], args[2], args[3], args[4])