Comments update

rcode/plotResultsLiveCells_JoePadgett.R

-# ----
+# Script to plot live cell data from Joe Padgett
+# subtract automatically bg and use Segementation results from J.Hossain
+# Antonio Politi, EMBL September 2016
 
 library('ggplot2')
 library('readxl')
 library('reshape')
 library('plyr')
-# ----
-concatResultsNew <- function (files, bgSub, coeff) {
+# fuction concatResultsNew ----
+concatResultsNew <- function (files, bgSub = 1, coeff = 0.6) {
+  # concatenate different files generated by segmentation algorithm of J. Hossain for inner-core, outer-core surface intensity estimation
+  # and subtract background
+  # INPUT:
+  #   files: a list of files
+  #   bgSub: 0, subtract background computed at anaphase; 1: subtract background from file located in BGcomp/bg/cellname_bg.txt
+  #   coeff: fraction of nuclear/cytoplasm signal used for background subtraction
+  # OUTPUT:
+  #   totalNuc; concatenated result file with continuous number nuclei and subtracted background
   
   totalNuc <- data.frame()
   id =0
@@ -95,13 +105,19 @@ concatResultsNew <- function (files, bgSub, coeff) {
   return(totalNuc)
 }
 
-# ----
+# fuction computeMeansMax --------
 computeMeansMax <- function(tot, sirna=0) {
+  # Aggregate data and compute means and max
+  # INPUT:
+  #   tot: concatenated files from concatResultsNew
+  #   sirna: 0 or 1 to specify if siRNA has been used
+  # OUTPUT:
+  #   mval: maximal value of different variables
   colNames = c('AvgIntOuterCore_ss', 'AvgIntInnerCore_ss', 'AvgIntNonCore_ss', 'AvgIntNuc_ss', 
                'AvgIntOuterCore_ms', 'AvgIntInnerCore_ms', 'AvgIntNonCore_ms', 'AvgIntNuc_ms','TotIntOuterCore_ss', 'TotIntInnerCore_ss', 'TotIntNonCore_ss', 'TotIntNuc_ss', 
                'TotIntOuterCore_ms', 'TotIntInnerCore_ms', 'TotIntNonCore_ms', 'TotIntNuc_ms', 'Area', 'bg')
   if (sirna > 0) {
-  tot<-subset(tot,  siRNA == sirna)
+    tot<-subset(tot,  siRNA == sirna)
   }
   mVal = data.frame()
   for (coln in colNames ){ 
@@ -123,8 +139,9 @@ computeMeansMax <- function(tot, sirna=0) {
   
 }
 
-# -----
+# function plotfun -----
 plotfun <- function(totalNuc, varName, siRNAname, varAxisName, xl, yl) {
+  # function to plot data
   siR <- unique(totalNuc$siRNA) 
   for (si in siR) {
     p <- ggplot(data = subset(totalNuc,  siRNA == si), aes_string(x='time', y = varName))+ 
@@ -150,8 +167,9 @@ plotfun <- function(totalNuc, varName, siRNAname, varAxisName, xl, yl) {
  p
 }
 
-# ----
+# function plotfunmul ----
 plotfunmul <- function(totalNuc, varName, siRNAname, varAxisName, xl, yl) {
+  # plot multiple dataset in one graph
   siR <- unique(totalNuc$siRNA)
   col = c('green', 'red', 'blue')
   colName <- c("WholeNuc","InnerCore","NonCore")
@@ -183,7 +201,7 @@ plotfunmul <- function(totalNuc, varName, siRNAname, varAxisName, xl, yl) {
   ggsave(filename=paste('regions_siRNA', paste(siR, collapse = '') , '.png', sep=''), plot=p, width = 151, height = 80, units = 'mm')
 }
 
-# -----
+# function plotmeans ----
 plotMeans <- function(mean1, mean2, colname, regionname, ylabT, filenameT) {
   pdata <- rbind(subset(mean1,Region %in% colname), 
         subset(mean2,Region %in% colname))
@@ -197,13 +215,12 @@ plotMeans <- function(mean1, mean2, colname, regionname, ylabT, filenameT) {
   return(p)
 }
 
-# -----
+# READ DATA in-----
 # Scrambled ELYSKD
 if (Sys.info()['sysname'] == 'Darwin'){
   setwd('/Users/toni/Dropbox/NPCMaturation/expdata/livecell_siRNA/images/ELYSKD/ScrambledsiRNA')
 } else {
   setwd('C:\\Users\\toni\\Dropbox\\NPCMaturation\\expdata\\livecell_siRNA\\images\\ELYSKD\\ScrambledsiRNA')
-  
 }
 
 files = c('ScrambledsiRNA_subset1', 'ScrambledsiRNA_subset2', 'ScrambledsiRNA_subset3', 'ScrambledsiRNA_subset4', 'ScrambledsiRNA_subset5',

rcode/plotResultsLiveCells_elifePaper.R

+# script to plot live cell data acquired for 
+# Shotaro et al. 2016 eLife paper
+
 library("Hmisc")
 library("ggplot2")
 # -----