# HG changeset patch
# User bornea
# Date 1454078038 18000
# Node ID ab602bbf4ac549099779b25d6e7adc879febe2d9
# Parent  64b822045467141af7631f5c9195e919d09f5328
Uploaded

diff -r 64b822045467 -r ab602bbf4ac5 bubbles_v9_NSAF_natural_log.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/bubbles_v9_NSAF_natural_log.R	Fri Jan 29 09:33:58 2016 -0500
@@ -0,0 +1,268 @@
+rm(list = ls()) 
+###################################################################################################
+# R-code: Multi-bubble graph generation from SAINTexpress output
+# Author: Brent Kuenzi
+###################################################################################################
+# This Script generates the bubble graphs based upon Saint output. 
+###################################################################################################
+# Copyright (C)  Brent Kuenzi.
+# Permission is granted to copy, distribute and/or modify this document
+# under the terms of the GNU Free Documentation License, Version 1.3
+# or any later version published by the Free Software Foundation;
+# with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
+# A copy of the license is included in the section entitled "GNU
+# Free Documentation License".
+###################################################################################################
+## REQUIRED INPUT ##
+
+# 1) listfile: SAINTexpress generated "list.txt" file
+# 2) preyfile: SAINT pre-processing generated "prey.txt" file used to run SAINTexpress
+
+## OPTIONAL INPUT ##
+
+# 3) crapome: raw output from crapome Workflow 1 query (http://www.crapome.org)
+# 4) color: bubble color (default = "red")
+#     - color = "crapome": color bubbles based on Crapome(%)
+#     - Also recognizes any color within R's built-in colors() vector
+# 5) label: Adds gene name labels to bubbles within the "zoomed in" graphs (default = FALSE)
+# 6) cutoff: Saintscore cutoff to be assigned for filtering the "zoomed in" graphs (default = 0.8)
+# 7) type: Specifies if the data is MaxQuant (MQ) or Scaffold (SC) data (default = "SC")
+# 8) inc_file: Selects only the uniprot ids in the provided list (default ="None")
+# 9) exc_file: Removes the proteins in the list (default = "None")
+###################################################################################################
+
+
+ins_check_run <- function(){
+  if ('dplyr' %in% rownames(installed.packages())){}
+  else {
+    install.packages('dplyr', repos = 'http://cran.us.r-project.org')
+  }
+  if ('tidyr' %in% rownames(installed.packages())){}
+  else {
+    install.packages('tidyr', repos = 'http://cran.us.r-project.org')
+  }
+  if ('ggplot2' %in% rownames(installed.packages())){}
+  else {
+    install.packages('ggplot2', repos = 'http://cran.us.r-project.org')
+  }
+}
+
+ins_check_run()
+library(dplyr); library(tidyr); library(ggplot2)
+
+
+main <- function(listfile, preyfile, crapome = FALSE, color = "red", label = FALSE, cutoff = 0.8, type = "SC", inc_file = "None", exc_file = "None" ) {
+  cutoff_check(cutoff)
+  listfile <- list_type(listfile, inc_file, exc_file)
+  if(type == "SC") {
+    df <- merge_files_sc(listfile, preyfile, crapome)
+  }
+  if(type == "MQ") {
+    df <- merge_files_mq(listfile, preyfile, crapome)
+  }
+  bubble_NSAF(df, color)
+  bubble_SAINT(df, color)
+  bubble_zoom_SAINT(df, color, label, cutoff)
+  bubble_zoom_NSAF(df, color, label, cutoff)
+  write.table(df, "output.txt", sep = "\t", quote = FALSE, row.names = FALSE)
+}
+###################################################################################################
+# Include and Exclude list filtering
+###################################################################################################
+list_type <- function(df, inc_file, exc_file) {
+  Saint <- read.delim(df, stringsAsFactors = FALSE)
+  if (inc_file != "None") {
+    if (exc_file == "None") {
+      inc_prots <- read.delim(inc_file, sep = '\t', header = FALSE, stringsAsFactors = FALSE)
+      print(inc_prots[, 1])
+      print(Saint$Prey)
+      filtered_df = subset(Saint, Saint$Prey == inc_prots[, 1])
+    }
+    else {
+      inc_prots <- read.delim(inc_file, sep = '\t', header = FALSE, stringsAsFactors = FALSE)
+      exc_prots <- read.delim(exc_file, sep = '\t', header = FALSE, stringsAsFactors = FALSE)
+      filtered_df = subset(Saint, Saint$Prey == inc_prots[, 1])
+      filtered_df = subset(filtered_df, filtered_df$Prey != exc_prots[, 1])
+    }
+  }
+  else if (exc_file != "None") {
+    exc_prots <- read.delim(exc_file, sep = '\t', header = FALSE, stringsAsFactors = FALSE)
+    filtered_df = subset(Saint, Saint$Prey != exc_prots[, 1])
+  }
+  else {
+    filtered_df = Saint
+  }
+  return(filtered_df)
+  
+}
+###################################################################################################
+# Merge input files and caculate Crapome(%) and NSAF for each protein for each bait
+###################################################################################################
+merge_files_mq <- function(SAINT, prey_DF, crapome = FALSE) {
+  #SAINT <- read.table(SAINT_DF, sep = '\t', header = TRUE)
+  #Some of these read.table()'s don't use stringsAsFactors = FALSE. Is this on purpose? Factors give rise to some really weird and unpredictable behavior; suggest always using stringsAsFactors = FALSE
+  prey <- read.table(prey_DF, sep = '\t', header = FALSE); colnames(prey) <- c("Prey", "Length", "PreyGene")
+  DF <- merge(SAINT, prey)
+  DF$SpecSum <- log2(DF$SpecSum)
+  
+  if(crapome != FALSE) {
+    crapome <- read.table(crapome, sep = '\t', header = TRUE)
+    colnames(crapome) <- c("Prey", "Symbol", "Num.of.Exp", "Ave.SC", "Max.SC")
+    DF1 <- merge(DF, crapome); as.character(DF1$Num.of.Exp); DF1$Symbol <- NULL;
+                    DF1$Ave.SC <- NULL; DF1$Max.SC <- NULL # Removes unnecessary columns.
+    DF1$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$CrapomePCT <- 100 - (as.integer(DF$NumExp) / as.integer(DF$TotalExp) * 100) # Calculate the crapome %.
+  }
+  DF$SAF <- DF$AvgSpec / DF$Length
+  DF2 = DF %>% group_by(Bait) %>% mutate(NSAF = SAF/sum(SAF))
+  DF$NSAF = DF2$NSAF
+  return(DF)
+}
+
+merge_files_sc <- function(SAINT, 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 # Removes 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 <- 100 - (as.integer(DF$NumExp) / as.integer(DF$TotalExp) * 100) # Calculate the crapome %.
+  }
+  DF$SAF <- DF$AvgSpec / DF$Length
+  DF2 = DF %>% group_by(Bait) %>% mutate(NSAF = SAF/sum(SAF))
+  DF$NSAF = DF2$NSAF
+  return(DF)
+}
+###################################################################################################
+# Plot all proteins for each bait by x = ln(NSAF), y = Log2(FoldChange)
+###################################################################################################
+bubble_NSAF <- function(data, color) {
+    if(color == "crapome") {
+      a <- subset(data, CrapomePCT < 80, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait))
+      b <- subset(data, CrapomePCT >= 80, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait))
+      p <- qplot(x = log(NSAF), y = log2(FoldChange), data = a, colour = I("tan"), size = SpecSum) + scale_size(range = c(1, 10)) + 
+        geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = a)
+      if(length(levels(a$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")} # multiple graphs if multiple baits
+      #The text says ln() which is log base e, but the code uses log base 10. Fix code or the axis label. 
+      p <- p + geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum, color = CrapomePCT), data = b) + 
+        scale_colour_gradient(limits = c(80, 100), low = "tan", high = "red") + 
+        labs(colour = "CRAPome Probability \nof Specific Interaction (%)", x = "ln(NSAF)") + 
+        geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = b)
+      return(ggsave(p, width = 8, height = 4, filename = "bubble_NSAF.png"))
+    }
+   if(color != "crapome") {
+      p <- qplot(x = log(NSAF), y = log2(FoldChange), data = data, colour = I(color), size = SpecSum) + scale_size(range = c(1, 10)) + 
+        geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = data) + # add bubble outlines
+          labs(x = "ln(NSAF)")
+        if(length(levels(data$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+      return(ggsave(p, width = 8, height = 4, filename = "bubble_NSAF.png"))
+    }
+  }
+###################################################################################################
+# Plot all proteins for each bait by x = Saintscore, y = Log2(FoldChange)
+###################################################################################################
+bubble_SAINT <- function(data, color) {
+    if(color == "crapome") {
+      a <- subset(data, CrapomePCT < 80, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait)) #filter on CRAPome
+      b <- subset(data, CrapomePCT >= 80, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait))
+      p <- qplot(x = SaintScore, y = log2(FoldChange), data = a, colour = I("tan"), size = SpecSum) + 
+        scale_size(range = c(1, 10)) + geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = a)
+      if(length(levels(a$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+      p <- p + geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum, color = CrapomePCT), data = b) + 
+        scale_colour_gradient(limits = c(80, 100), low = "tan", high = "red") + 
+        labs(colour = "CRAPome Probability \nof Specific Interaction (%)") +
+        geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = b)
+      return(ggsave(p, width = 8, height = 4, filename = "bubble_SAINT.png"))
+    }
+    if(color != "crapome") {
+      p <- qplot(x = SaintScore, y = log2(FoldChange), data = data, colour = I(color), size = SpecSum) +
+        scale_size(range = c(1, 10)) + geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = data)
+      if(length(levels(data$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+      return(ggsave(p, width = 8, height = 4, filename = "bubble_SAINT.png"))
+    }
+  }
+###################################################################################################
+# Filter proteins on Saintscore cutoff and plot for each bait x = Saintscore, y = Log2(FoldChange)
+###################################################################################################
+bubble_zoom_SAINT <- function(data, color, label = FALSE, cutoff = 0.8) {
+  if(color == "crapome") {
+    a <- subset(data, CrapomePCT < 80 & SaintScore >= cutoff, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait, PreyGene))
+    b <- subset(data, CrapomePCT >= 80 & SaintScore >= cutoff, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait, PreyGene))
+    p <- qplot(x = SaintScore, y = log2(FoldChange), data = a, colour = I("tan"), size = SpecSum) + 
+      scale_size(range = c(1, 10)) + ggtitle("Filtered on SAINT score")+geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = a)
+    if(label == TRUE & length(a$NSAF != 0)) {
+      p <- p + geom_text(data = a, aes(label = PreyGene, size = 10, vjust = 0, hjust = 0), colour = "black")
+    }
+    if(length(levels(a$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+    p <- p + geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum, color = CrapomePCT), data = b) + 
+      scale_colour_gradient(limits = c(80, 100), low = "tan", high = "red") + 
+      labs(colour = "CRAPome Probability \nof Specific Interaction (%)") + 
+      geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = b)
+    if(label == TRUE & length(b$NSAF != 0)) {
+      p <- p + geom_text(data = b, aes(label = PreyGene, size = 10, vjust = 0, hjust = 0), colour = "black", show_guide = FALSE)
+    }
+    return(ggsave(p, width = 8, height = 4, filename = "bubble_zoom_SAINT.png"))
+  }
+  if(color != "crapome") {
+    a <- subset(data, SaintScore >= cutoff, select = c(NSAF, SpecSum, FoldChange, SaintScore, Bait, PreyGene))
+    p <- qplot(x = SaintScore, y = log2(FoldChange), data = a, colour = I(color), size = SpecSum) +
+      scale_size(range = c(1, 10)) + ggtitle("Filtered on SAINT score") + 
+      geom_point(aes(x = SaintScore, y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = a)
+    if(label == TRUE & length(a$NSAF != 0)) {
+      p <- p + geom_text(data = a, aes(label = PreyGene, size = 10, vjust = 0, hjust = 0), colour = "black", show_guide = FALSE)
+    }
+    if(length(levels(data$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+    return(ggsave(p, width = 8, height = 4, filename = "bubble_zoom_SAINT.png"))
+  }
+}
+###################################################################################################
+# Filter proteins on Saintscore cutoff and plot for each bait x = log(NSAF), y = Log2(FoldChange)
+###################################################################################################
+bubble_zoom_NSAF <- function(data, color, label = FALSE, cutoff = 0.8) {
+  if(color == "crapome") {
+    a <- subset(data, CrapomePCT < 80 & SaintScore >= cutoff, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait, PreyGene))
+    b <- subset(data, CrapomePCT >= 80 & SaintScore >= cutoff, select = c(NSAF, SpecSum, CrapomePCT, FoldChange, SaintScore, Bait, PreyGene))
+    p <- qplot(x = log(NSAF), y = log2(FoldChange), data = a, colour = I("tan"), size = SpecSum) + 
+      scale_size(range = c(1, 10)) + ggtitle("Filtered on SAINT score") + 
+      geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = a)
+    if(label == TRUE & length(a$NSAF != 0)) {
+      p <- p + geom_text(data = a, aes(label = PreyGene, size = 10, vjust = 0, hjust = 0), colour = "black")
+    }
+    if(length(levels(a$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+    p <- p + geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum, color = CrapomePCT), data = b) + 
+      scale_colour_gradient(limits = c(80, 100), low = "tan", high = "red") + 
+      labs(colour = "CRAPome Probability \nof Specific Interaction (%)", x = "ln(NSAF)") + 
+      geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = b)
+    if(label == TRUE & length(b$NSAF != 0)) {
+      p <- p + geom_text(data = b, aes(label = PreyGene, size = 10, vjust = 0, hjust = 0), colour = "black", show_guide = FALSE)
+    }
+    return(ggsave(p, width = 8, height = 4, filename = "bubble_zoom_NSAF.png"))
+  }
+  if(color != "crapome") {
+    a <- subset(data, SaintScore >= cutoff, select = c(NSAF, SpecSum, FoldChange, SaintScore, Bait, PreyGene))
+    p <- qplot(x = log(NSAF), y = log2(FoldChange), data = a, colour = I(color), size = SpecSum) +
+      scale_size(range = c(1, 10)) + ggtitle("Filtered on SAINT score") + 
+      geom_point(aes(x = log(NSAF), y = log2(FoldChange), size = SpecSum), colour = "black", shape = 21, data = a) + 
+      labs(x = "ln(NSAF)")
+    if(label == TRUE & length(a$NSAF != 0)) {
+      p <- p + geom_text(data = a, aes(label = PreyGene, size = 10, vjust = 0, hjust = 0), colour = "black", show_guide = FALSE)
+    }
+    if(length(levels(data$Bait) > 1)) {p <- p + facet_wrap(~Bait, scales = "free_y")}
+    return(ggsave(p, width = 8, height = 4, filename = "bubble_zoom_NSAF.png"))
+  }
+}
+###################################################################################################
+# Check Saintscore cutoff and stop program if not between 0 and 1
+###################################################################################################
+cutoff_check <- function(cutoff){
+  if( any(cutoff < 0 | cutoff > 1) ) stop('SAINT score cutoff not between 0 and 1. Please correct and try again')
+}
+
+args <- commandArgs(trailingOnly = TRUE)
+main(args[1], args[2], args[3], args[4], args[5], args[6], args[7], args[8], args[9])