changeset 6:16bbe277d15d draft

Uploaded
author modencode-dcc
date Thu, 17 Jan 2013 15:57:35 -0500
parents 48767bec000d
children aeea5ac02b3b
files batch-consistency-analysis.r batch-consistency-plot.r functions-all-clayton-12-13.r genome_tables/genome_table.dmel.r5.32.txt genome_tables/genome_table.human.hg18.txt genome_tables/genome_table.human.hg19.txt genome_tables/genome_table.mm9.txt genome_tables/genome_table.worm.ws220.txt idrPlotDef.xml idrPlotWrapper.sh idrToolDef.xml
diffstat 11 files changed, 94 insertions(+), 3480 deletions(-) [+]
line wrap: on
line diff
--- a/batch-consistency-analysis.r	Thu Jan 17 15:45:48 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,182 +0,0 @@
-##############################################################################
-
-# Modified 06/29/12: Kar Ming Chu
-# Modified to work with Galaxy
-
-# Usage: 	Rscript batch-consistency-analysis.r peakfile1 peakfile2 half.width overlap.ratio  is.broadpeak sig.value gtable r.output overlap.output npeaks.output em.sav.output uri.sav.output
-
-# Changes:
-# 	- Appended parameter for input gnome table called gtable
-#	- Appended parameter for specifying Rout output file name (required by Galaxy)
-#	- Appended parameter for specifying Peak overlap output file name (required by Galaxy)
-#	- Appended parameter for specifying Npeak above IDR output file name (required by Galaxy)
-#	- Removed parameter outfile.prefix since main output files are replaced with strict naming
-#	- Appended parameter for specifying em.sav output file (for use with batch-consistency-plot.r)
-#	- Appended parameter for specifying uri.sav output file (for use with batch-consistency-plot.r)
-
-##############################################################################
-
-# modified 3-29-10: Qunhua Li
-# add 2 columns in the output of "-overlapped-peaks.txt": local.idr and IDR
-
-# 01-20-2010 Qunhua Li
-#
-# This program performs consistency analysis for a pair of peak calling outputs
-# It takes narrowPeak or broadPeak formats.
-# 
-# usage: Rscript batch-consistency-analysis2.r peakfile1 peakfile2 half.width outfile.prefix overlap.ratio  is.broadpeak sig.value
-#
-# peakfile1 and peakfile2 : the output from peak callers in narrowPeak or broadPeak format
-# half.width: -1 if using the reported peak width, 
-#             a numerical value to truncate the peaks to
-# outfile.prefix: prefix of output file
-# overlap.ratio: a value between 0 and 1. It controls how much overlaps two peaks need to have to be called as calling the same region. It is the ratio of overlap / short peak of the two. When setting at 0, it means as long as overlapped width >=1bp, two peaks are deemed as calling the same region.
-# is.broadpeak: a logical value. If broadpeak is used, set as T; if narrowpeak is used, set as F
-# sig.value: type of significant values, "q.value", "p.value" or "signal.value" (default, i.e. fold of enrichment)
-
-args <- commandArgs(trailingOnly=T)
-
-# consistency between peakfile1 and peakfile2
-#input1.dir <- args[1]
-#input2.dir <- args[2] # directories of the two input files
-peakfile1 <- args[1]
-peakfile2 <- args[2]
-
-if(as.numeric(args[3])==-1){ # enter -1 when using the reported length 
-  half.width <- NULL
-}else{
-  half.width <- as.numeric(args[3])
-}
-
-overlap.ratio <- args[4]
-
-if(args[5] == "T"){
-  is.broadpeak <- T
-}else{
-  is.broadpeak <- F
-}
-
-sig.value <- args[6]
-
-
-#dir1 <- "~/ENCODE/anshul/data/"
-#dir2 <- dir1
-#peakfile1 <- "../data/SPP.YaleRep1Gm12878Cfos.VS.Gm12878Input.PointPeak.narrowPeak"
-#peakfile2 <- "../data/SPP.YaleRep3Gm12878Cfos.VS.Gm12878Input.PointPeak.narrowPeak"
-#half.width <- NULL
-#overlap.ratio <- 0.1
-#sig.value <- "signal.value"
-
-
-source("/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/functions-all-clayton-12-13.r")
-
-# read the length of the chromosomes, which will be used to concatenate chr's
-# chr.file <- "genome_table.txt"
-# args[7] is the gtable
-chr.file <- args[7]
-
-chr.size <- read.table(chr.file)
-
-# setting output files
-r.output <- args[8]
-overlap.output <- args[9]
-npeaks.output <- args[10]
-em.sav.output <- args[11]
-uri.sav.output <- args[12]
-
-# sink(paste(output.prefix, "-Rout.txt", sep=""))
-sink(r.output)
-
-############# process the data
-cat("is.broadpeak", is.broadpeak, "\n")
-# process data, summit: the representation of the location of summit
-rep1 <- process.narrowpeak(paste(peakfile1, sep=""), chr.size, half.width=half.width, summit="offset", broadpeak=is.broadpeak)
-rep2 <- process.narrowpeak(paste(peakfile2, sep=""), chr.size, half.width=half.width, summit="offset", broadpeak=is.broadpeak)
-
-cat(paste("read", peakfile1, ": ", nrow(rep1$data.ori), "peaks\n", nrow(rep1$data.cleaned), "peaks are left after cleaning\n", peakfile2, ": ", nrow(rep2$data.ori), "peaks\n", nrow(rep2$data.cleaned), " peaks are left after cleaning"))
-
-if(args[3]==-1){
-  cat(paste("half.width=", "reported", "\n"))
-}else{
-  cat(paste("half.width=", half.width, "\n"))
-}  
-cat(paste("significant measure=", sig.value, "\n"))
-
-# compute correspondence profile (URI)
-uri.output <- compute.pair.uri(rep1$data.cleaned, rep2$data.cleaned, sig.value1=sig.value, sig.value2=sig.value, overlap.ratio=overlap.ratio)
-
-#uri.output <- compute.pair.uri(rep1$data.cleaned, rep2$data.cleaned)
-
-cat(paste("URI is done\n"))
-
-# save output
-# save(uri.output, file=paste(output.prefix, "-uri.sav", sep=""))
-save(uri.output, file=uri.sav.output)
-cat(paste("URI is saved at: ", uri.sav.output))
-
-
-# EM procedure for inference
-em.output <- fit.em(uri.output$data12.enrich, fix.rho2=T)
-
-#em.output <- fit.2copula.em(uri.output$data12.enrich, fix.rho2=T, "gaussian")
-
-cat(paste("EM is done\n\n"))
-
-save(em.output, file=em.sav.output)
-cat(paste("EM is saved at: ", em.sav.output))
-
-
-# write em output into a file
-
-cat(paste("EM estimation for the following files\n", peakfile1, "\n", peakfile2, "\n", sep=""))
-
-print(em.output$em.fit$para)
-
-# add on 3-29-10
-# output both local idr and IDR
-idr.local <- 1-em.output$em.fit$e.z
-IDR <- c()
-o <- order(idr.local)
-IDR[o] <- cumsum(idr.local[o])/c(1:length(o))
-
-
-write.out.data <- data.frame(chr1=em.output$data.pruned$sample1[, "chr"],
-                    start1=em.output$data.pruned$sample1[, "start.ori"],
-                    stop1=em.output$data.pruned$sample1[, "stop.ori"],
-                    sig.value1=em.output$data.pruned$sample1[, "sig.value"],   
-                    chr2=em.output$data.pruned$sample2[, "chr"],
-                    start2=em.output$data.pruned$sample2[, "start.ori"],
-                    stop2=em.output$data.pruned$sample2[, "stop.ori"],
-                    sig.value2=em.output$data.pruned$sample2[, "sig.value"],
-                    idr.local=1-em.output$em.fit$e.z, IDR=IDR)
-
-# write.table(write.out.data, file=paste(output.prefix, "-overlapped-peaks.txt", sep=""))
-write.table(write.out.data, file=overlap.output)
-cat(paste("Write overlapped peaks and local idr to: ", overlap.output, sep=""))
-
-# number of peaks passing IDR range (0.01-0.25)
-IDR.cutoff <- seq(0.01, 0.25, by=0.01)
-idr.o <- order(write.out.data$idr.local)
-idr.ordered <- write.out.data$idr.local[idr.o]
-IDR.sum <- cumsum(idr.ordered)/c(1:length(idr.ordered))
-
-IDR.count <- c()
-n.cutoff <- length(IDR.cutoff)
-for(i in 1:n.cutoff){
-  IDR.count[i] <- sum(IDR.sum <= IDR.cutoff[i])
-}
-
-
-# write the number of peaks passing various IDR range into a file
-idr.cut <- data.frame(peakfile1, peakfile2, IDR.cutoff=IDR.cutoff, IDR.count=IDR.count)
-write.table(idr.cut, file=npeaks.output, append=T, quote=F, row.names=F, col.names=F)
-cat(paste("Write number of peaks above IDR cutoff [0.01, 0.25]: ","npeaks-aboveIDR.txt\n", sep=""))
-
-mar.mean <- get.mar.mean(em.output$em.fit)
-
-cat(paste("Marginal mean of two components:\n"))
-print(mar.mean)
-
-sink()
-
-
--- a/batch-consistency-plot.r	Thu Jan 17 15:45:48 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,68 +0,0 @@
-# 1-20-10 Qunhua Li
-#
-# This program first plots correspondence curve and IDR threshold plot
-# (i.e. number of selected peaks vs IDR) for each pair of sample
-#
-# usage: 
-# Rscript batch-consistency-plot-merged.r [npairs] [output.dir] [input.file.prefix 1, 2, 3 ...]
-# [npairs]: integer, number of consistency analyses
-#          (e.g. if 2 replicates, npairs=1, if 3 replicates, npairs=3
-# [output.prefix]: output directory and file name prefix for plot eg. /plots/idrPlot
-# [input.file.prefix 1, 2, 3]: prefix for the output from batch-consistency-analysis2. They are the input files for merged analysis see below for examples (i.e. saved.file.prefix). It can be multiple files
-#
-
-args <- commandArgs(trailingOnly=T)
-npair <- args[1] # number of curves to plot on the same figure
-output.file.prefix <- args[2] # file name for plot, generated from script at the outer level
-df.txt <- 10
-ntemp <- as.numeric(npair)
-saved.file.prefix <- list() # identifier of filenames that contain the em and URI results
-source("/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/functions-all-clayton-12-13.r")
-
-uri.list <- list()
-uri.list.match <- list()
-ez.list <- list()
-legend.txt <- c()
-em.output.list <- list()
-uri.output.list <- list()
-
-for(i in 1:npair){
-  saved.file.prefix[i] <- args[2+i]
- 
-  load(paste(saved.file.prefix[i], "-uri.sav", sep=""))
-  load(paste(saved.file.prefix[i], "-em.sav", sep=""))
-
-  uri.output.list[[i]] <- uri.output
-  em.output.list[[i]] <- em.output
-
-  ez.list[[i]] <- get.ez.tt.all(em.output, uri.output.list[[i]]$data12.enrich$merge1,
-                                uri.output.list[[i]]$data12.enrich$merge2) # reverse =T for error rate
-
-  # URI for all peaks
-  uri.list[[i]] <- uri.output$uri.n
-  # URI for matched peaks
-  uri.match <- get.uri.matched(em.output$data.pruned, df=df.txt)
-  uri.list.match[[i]] <- uri.match$uri.n
-
-  file.name <- unlist(strsplit(as.character(saved.file.prefix[i]), "/"))
-  
-  legend.txt[i] <- paste(i, "=", file.name[length(file.name)])
-
-}
-
-plot.uri.file <- paste(output.file.prefix, "-plot.ps", sep="")
-
-############# plot and report output
-# plot correspondence curve for each pair,
-# plot number of selected peaks vs IDR 
-# plot all into 1 file
-postscript(paste(output.file.prefix, "-plot.ps", sep=""))
-par(mfcol=c(2,3), mar=c(5,6,4,2)+0.1)
-plot.uri.group(uri.list, NULL, file.name=NULL, c(1:npair), title.txt="all peaks")
-plot.uri.group(uri.list.match, NULL, file.name=NULL, c(1:npair), title.txt="matched peaks")
-plot.ez.group(ez.list, plot.dir=NULL, file.name=NULL, legend.txt=c(1:npair), y.lim=c(0, 0.6))
-plot(0, 1, type="n", xlim=c(0,1), ylim=c(0,1), xlab="", ylab="", xaxt="n", yaxt="n") # legends
-legend(0, 1, legend.txt, cex=0.6)
-
-dev.off()
-
--- a/functions-all-clayton-12-13.r	Thu Jan 17 15:45:48 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,3099 +0,0 @@
-# revised on 2-20-10
-# - fix error in pass.structure: reverse rank.combined, so that big sig.value
-#  are ranked with small numbers (1, 2, ...)
-# - fix error on get.ez.tt.all: get ez.cutoff from sorted e.z
-
-#
-# modified EM procedure to compute empirical CDF more precisely - 09/2009
-
-
-
-# this file contains the functions for  
-# 1. computing the correspondence profile (upper rank intersection and derivatives)
-# 2. inference of copula mixture model
-#
-# It also has functions for
-# 1. reading peak caller results
-# 2. processing and matching called peaks
-# 3. plotting results
-
-
-################ read peak caller results
-
-# process narrow peak format
-# some peak callers may not report q-values, p-values or fold of enrichment
-# need further process before comparison
-#
-# stop.exclusive: Is the basepair of peak.list$stop exclusive? In narrowpeak and broadpeak format they are exclusive.
-# If it is exclusive, we need subtract peak.list$stop by 1 to avoid the same basepair being both a start and a stop of two 
-# adjacent peaks, which creates trouble for finding correct intersect  
-process.narrowpeak <- function(narrow.file, chr.size, half.width=NULL, summit="offset", stop.exclusive=T, broadpeak=F){
-
-
-  aa <- read.table(narrow.file)
-
-  if(broadpeak){
-    bb.ori <- data.frame(chr=aa$V1, start=aa$V2, stop=aa$V3, signal.value=aa$V7, p.value=aa$V8, q.value=aa$V9)
-  }else{
-    bb.ori <- data.frame(chr=aa$V1, start=aa$V2, stop=aa$V3, signal.value=aa$V7, p.value=aa$V8, q.value=aa$V9, summit=aa$V10)
-  }
-    
-  if(summit=="summit"){
-    bb.ori$summit <- bb.ori$summit-bb.ori$start # change summit to offset to avoid error when concatenating chromosomes
-  }
- 
-  bb <- concatenate.chr(bb.ori, chr.size)
-
-  #bb <- bb.ori
-
-  # remove the peaks that has the same start and stop value
-  bb <- bb[bb$start != bb$stop,]
-
-  if(stop.exclusive==T){
-    bb$stop <- bb$stop-1
-  }
-
-  if(!is.null(half.width)){
-    bb$start.ori <- bb$start    
-    bb$stop.ori <- bb$stop 
-
-    # if peak is narrower than the specified window, stay with its width
-    # otherwise chop wider peaks to specified width
-    width <- bb$stop-bb$start +1
-    is.wider <- width > 2*half.width
-
-    if(summit=="offset" | summit=="summit"){ # if summit is offset from start
-      bb$start[is.wider] <- bb$start.ori[is.wider] + bb$summit[is.wider]-half.width
-      bb$stop[is.wider] <- bb$start.ori[is.wider] + bb$summit[is.wider]+half.width
-    } else { 
-      if(summit=="unknown"){
-        bb$start[is.wider] <- bb$start.ori[is.wider]+round(width[is.wider]/2) - half.width
-        bb$stop[is.wider] <- bb$start.ori[is.wider]+round(width[is.wider]/2) + half.width
-      }
-    }
-  }
-
-  bb <- clean.data(bb)
-  invisible(list(data.ori=bb.ori, data.cleaned=bb))
-}
-
-# clean data 
-# and concatenate chromosomes if needed
-clean.data <- function(adata){
-
-  # remove the peaks that has the same start and stop value
-  adata <- adata[adata$start != adata$stop,]
-
-  # if some stops and starts are the same, need fix them
-  stop.in.start <- is.element(adata$stop, adata$start)
-  n.fix <- sum(stop.in.start)
-  if(n.fix >0){
-    print(paste("Fix", n.fix, "stops\n"))
-    adata$stop[stop.in.start] <- adata$stop[stop.in.start]-1 
-  }  
- 
-  return(adata) 
-}
-
-# concatenate peaks
-# peaks: the dataframe to have all the peaks
-# chr.file: the file to keep the length of each chromosome 
-# chr files should come from the species that the data is from
-#concatenate.chr <- function(peaks, chr.size){
-
- # chr.size <- read.table(chr.file)
-#  chr.o <- order(chr.size[,1])
-#  chr.size <- chr.size[chr.o,]
-#
-#  chr.shift <- cumsum(c(0, chr.size[-nrow(chr.size),2]))
-#  chr.size.cum <- data.frame(chr=chr.size[,1], shift=chr.shift)  
-#
-#  for(i in 1:nrow(chr.size)){
-#    is.in <- as.character(peaks$chr) == as.character(chr.size.cum$chr[i])
-#    if(sum(is.in)>0){
-#      peaks[is.in,]$start <- peaks[is.in,]$start + chr.size.cum$shift[i]
-#      peaks[is.in,]$stop <- peaks[is.in,]$stop + chr.size.cum$shift[i]
-#    }
-#  }
-#
-#  invisible(peaks)
-#}
-
-
-
-
-# concatenate peaks
-# peaks: the dataframe to have all the peaks
-# chr.file: the file to keep the length of each chromosome 
-# chr files should come from the species that the data is from
-concatenate.chr <- function(peaks, chr.size){
-
- # chr.size <- read.table(chr.file)
-  chr.o <- order(chr.size[,1])
-  chr.size <- chr.size[chr.o,]
-
-  chr.shift <- cumsum(c(0, chr.size[-nrow(chr.size),2]))
-  chr.size.cum <- data.frame(chr=chr.size[,1], shift=chr.shift)  
-
-  peaks$start.ori <- peaks$start
-  peaks$stop.ori <- peaks$stop
-  
-  for(i in 1:nrow(chr.size)){
-    is.in <- as.character(peaks$chr) == as.character(chr.size.cum$chr[i])
-    if(sum(is.in)>0){
-      peaks[is.in,]$start <- peaks[is.in,]$start + chr.size.cum$shift[i]
-      peaks[is.in,]$stop <- peaks[is.in,]$stop + chr.size.cum$shift[i]
-    }
-  }
-
-  invisible(peaks)
-}
-
-
-deconcatenate.chr <- function(peaks, chr.size){
-
-  chr.o <- order(chr.size[,1])
-  chr.size <- chr.size[chr.o,]
-
-  chr.shift <- cumsum(c(0, chr.size[-nrow(chr.size),2]))
-  chr.size.cum <- data.frame(chr=chr.size[,1], shift=chr.shift)  
-
-  peaks$chr <- rep(NA, nrow(peaks))
-  
-  for(i in 1:(nrow(chr.size.cum)-1)){
-    is.in <- peaks$start > chr.size.cum[i,2] & peaks$start <= chr.size.cum[i+1, 2]
-    if(sum(is.in)>0){
-      peaks[is.in,]$start <- peaks[is.in,]$start - chr.size.cum[i,2]
-      peaks[is.in,]$stop <- peaks[is.in,]$stop - chr.size.cum[i,2]+1    
-      peaks[is.in,]$chr <- chr.size[i,1]
-    }
-  }
-
-  if(i == nrow(chr.size.cum)){
-    is.in <- peaks$start > chr.size.cum[i, 2]
-    if(sum(is.in)>0){
-      peaks[is.in,]$start <- peaks[is.in,]$start - chr.size.cum[i,2]
-      peaks[is.in,]$stop <- peaks[is.in,]$stop - chr.size.cum[i,2]+1    
-      peaks[is.in,]$chr <- chr.size[i,1]
-    }
-  }
-  
-  invisible(peaks)
-}
-
-################ preprocessing peak calling output
-
-
-# 
-# read two calling results and sort by peak starting locations, 
-# then find overlap between peaks
-# INPUT:
-#   rep1: the 1st replicate
-#   rep2: the 2nd replicate
-# OUTPUT:
-#   id1, id2: the labels for the identified peaks on the replicates
-find.overlap <- function(rep1, rep2){
-
-  o1 <- order(rep1$start)
-  rep1 <- rep1[o1,]
-    
-  o2 <- order(rep2$start)
-  rep2 <- rep2[o2,]
-
-  n1 <- length(o1)
-  n2 <- length(o2)
-  
-  # assign common ID to peaks
-  id1 <- rep(0, n1) # ID assigned on rep1
-  id2 <- rep(0, n2) # ID assigned on rep2
-  id <- 1 # keep track common id's
-  
-  # check if two replicates overlap with each other
-  i <- 1
-  j <- 1
-
-  while(i <= n1|| j <= n2){
-
-    # && (id1[n1] ==0 || id2[n2] ==0)
-    
-    # if one list runs out
-    if(i > n1 && j < n2){
-      
-      j <- j+1
-      id2[j] <- id
-      id <- id +1
-      next
-    } else{
-      if(j > n2 && i < n1){
-        i <- i+1        
-        id1[i] <- id
-        id <- id +1
-        next
-      } else {
-        if(i >= n1 && j >=n2)
-          break
-      }
-    }
-
-    # if not overlap
-
-    if(!(rep1$start[i] <= rep2$stop[j] && rep2$start[j] <= rep1$stop[i])){
-
-      # at the start of loop, when both are not assigned an ID
-      # the one locates in front is assigned first
-      if(id1[i] ==0 && id2[j]==0){
-        if(rep1$stop[i] < rep2$stop[j]){
-          id1[i] <- id
-        } else {
-          id2[j] <- id
-        }
-      } else { # in the middle of the loop, when one is already assigned
-      # The one that has not assigned gets assigned
-      #  if(id1[i] ==0){ # id1[i] is not assigned
-      #    id1[i] <- id
-      #  } else { # id2[i] is not assigned
-      #    id2[j] <- id 
-      #  }
-
-        # order the id according to location
-        if(rep1$stop[i] <= rep2$stop[j]){
-          id1[i] <- max(id2[j], id1[i])
-          id2[j] <- id  
-        } else {
-          if(rep1$stop[i] > rep2$stop[j]){
-            id2[j] <- max(id1[i], id2[j])
-            id1[i] <- id
-          }
-        }
-          
-      }
-      
-      id <- id +1
-      
-    } else { # if overlap
-    
-      if(id1[i] == 0 && id2[j] == 0){ # not assign label yet
-        id1[i] <- id 
-        id2[j] <- id
-        id <- id +1
-      } else { # one peak is already assigned label, the other is 0
-        
-        id1[i] <- max(id1[i], id2[j]) # this is a way to copy the label of the assigned peak without knowing which one is already assigned
-        id2[j] <- id1[i] # syncronize the labels        
-      }
-      
-    }
-    
-    if(rep1$stop[i] < rep2$stop[j]){
-      i <- i+1
-    } else {
-      j <- j+1
-    } 
-    
-  }
-
-  invisible(list(id1=id1, id2=id2))
-  
-}
-
-# Impute the missing significant value for the peaks called only on one replicate.
-# value 
-# INPUT:
-#   rep1, rep2: the two peak calling output 
-#   id1, id2: the IDs assigned by function find.overlap, vectors
-#        If id1[i]==id2[j], peak i on rep1 overlaps with peak j on rep2
-#   p.value.impute: the significant value to impute for the missing peaks 
-# OUTPUT:   
-#   rep1, rep2: peaks ordered by the start locations with imputed peaks
-#   id1, id2: the IDs with imputed peaks
-fill.missing.peaks <- function(rep1, rep2, id1, id2, p.value.impute){
-
-#   rep1 <- data.frame(chr=rep1$chr, start=rep1$start, stop=rep1$stop, sig.value=rep1$sig.value)
-#   rep2 <- data.frame(chr=rep2$chr, start=rep2$start, stop=rep2$stop, sig.value=rep2$sig.value)   
-   
-   o1 <- order(rep1$start)
-   rep1 <- rep1[o1,]
-    
-   o2 <- order(rep2$start)
-   rep2 <- rep2[o2,]  
-     
-   entry.in1.not2 <- !is.element(id1, id2)
-   entry.in2.not1 <- !is.element(id2, id1)
-
-   if(sum(entry.in1.not2) > 0){
-   
-     temp1 <- rep1[entry.in1.not2, ]
-
-     # impute sig.value
-     temp1$sig.value <- p.value.impute
-     temp1$signal.value <- p.value.impute
-     temp1$p.value <- p.value.impute
-     temp1$q.value <- p.value.impute
-     
-     rep2.filled <- rbind(rep2, temp1)
-     id2.filled <- c(id2, id1[entry.in1.not2])
-   } else {
-     id2.filled <- id2
-     rep2.filled <- rep2
-   }
-
-   if(sum(entry.in2.not1) > 0){
-
-     temp2 <- rep2[entry.in2.not1, ]
-
-     # fill in p.values to 1
-     temp2$sig.value <- p.value.impute
-     temp2$signal.value <- p.value.impute
-     temp2$p.value <- p.value.impute
-     temp2$q.value <- p.value.impute
-   
-
-     # append to the end
-     rep1.filled <- rbind(rep1, temp2)
-
-     id1.filled <- c(id1, id2[entry.in2.not1])
-   } else {
-     id1.filled <- id1
-     rep1.filled <- rep1
-   }
-
-   # sort rep1 and rep2 by the same id
-   o1 <- order(id1.filled)
-   rep1.ordered <- rep1.filled[o1, ]
-
-   o2 <- order(id2.filled)
-   rep2.ordered <- rep2.filled[o2, ]   
-   
-   invisible(list(rep1=rep1.ordered, rep2=rep2.ordered,
-                  id1=id1.filled[o1], id2=id2.filled[o2]))
- }
-
-# Merge peaks with same ID on the same replicates 
-# (They are generated if two peaks on rep1 map to the same peak on rep2)
-# need peak.list have 3 columns: start, stop and sig.value 
-merge.peaks <- function(peak.list, id){
-
-  i <- 1
-  j <- 1
-  dup.index <- c()
-  sig.value <- c()
-  start.new <- c()
-  stop.new <- c()
-  id.new <- c()
-
-  # original data
-  chr <- c()
-  start.ori <- c()
-  stop.ori <- c()
-  
-  signal.value <- c()
-  p.value <- c()
-  q.value <- c()
-
-  while(i < length(id)){
-    
-    if(id[i] == id[i+1]){
-      dup.index <- c(dup.index, i, i+1) # push on dup.index
-    } else {
-      if(length(dup.index)>0){ # pop from dup.index
-        sig.value[j] <- mean(peak.list$sig.value[unique(dup.index)]) # mean of -log(pvalue)
-        start.new[j] <- peak.list$start[min(dup.index)]
-        stop.new[j] <- peak.list$stop[max(dup.index)]
-        id.new[j] <- id[max(dup.index)]
-        
-        signal.value[j] <- mean(peak.list$signal.value[unique(dup.index)]) # mean of -log(pvalue)
-        p.value[j] <- mean(peak.list$p.value[unique(dup.index)]) # mean of -log(pvalue)
-        q.value[j] <- mean(peak.list$q.value[unique(dup.index)]) # mean of -log(pvalue)
-
-        chr[j] <- as.character(peak.list$chr[min(dup.index)])
-        start.ori[j] <- peak.list$start.ori[min(dup.index)]
-        stop.ori[j] <- peak.list$stop.ori[max(dup.index)]
-        
-        dup.index <- c()
-      } else { # nothing to pop
-        sig.value[j] <- peak.list$sig.value[i]
-        start.new[j] <- peak.list$start[i]
-        stop.new[j] <- peak.list$stop[i]
-        id.new[j] <- id[i]
-
-        signal.value[j] <- peak.list$signal.value[i] 
-        p.value[j] <- peak.list$p.value[i] 
-        q.value[j] <- peak.list$q.value[i] 
-
-        chr[j] <- as.character(peak.list$chr[i])
-        start.ori[j] <- peak.list$start.ori[i]
-        stop.ori[j] <- peak.list$stop.ori[i]
-        
-      }
-      j <- j+1
-    }
-    i <- i+1
-  }
-
-  data.new <- data.frame(id=id.new, sig.value=sig.value, start=start.new, stop=stop.new, signal.value=signal.value, p.value=p.value, q.value=q.value, chr=chr, start.ori=start.ori, stop.ori=stop.ori)
-  invisible(data.new)
-}
-
-
-
-
-
-# a wrap function to fill in missing peaks, merge peaks and impute significant values
-# out1 and out2 are two peak calling outputs
-pair.peaks <- function(out1, out2, p.value.impute=0){
-
-  aa <- find.overlap(out1, out2)
-  bb <- fill.missing.peaks(out1, out2, aa$id1, aa$id2, p.value.impute=0)
-
-  cc1 <- merge.peaks(bb$rep1, bb$id1)
-  cc2 <- merge.peaks(bb$rep2, bb$id2)
-
-  invisible(list(merge1=cc1, merge2=cc2))
-}
-
-
-
-# overlap.ratio is a parameter to define the percentage of overlap
-# if overlap.ratio =0, 1 basepair overlap is counted as overlap
-# if overlap.ratio between 0 and 1, it is the minimum proportion of
-# overlap required to be called as a match
-# it is computed as the overlap part/min(peak1.length, peak2.length)
-pair.peaks.filter <- function(out1, out2, p.value.impute=0, overlap.ratio=0){
-
-  aa <- find.overlap(out1, out2)
-  bb <- fill.missing.peaks(out1, out2, aa$id1, aa$id2, p.value.impute=0)
-
-  cc1 <- merge.peaks(bb$rep1, bb$id1)
-  cc2 <- merge.peaks(bb$rep2, bb$id2)
-
-  frag12 <- cbind(cc1$start, cc1$stop, cc2$start, cc2$stop)
-  
-  frag.ratio <- apply(frag12, 1, overlap.middle)
-
-  frag.ratio[cc1$sig.value==p.value.impute | cc2$sig.value==p.value.impute] <- 0
-
-  cc1$frag.ratio <- frag.ratio
-  cc2$frag.ratio <- frag.ratio
-
-  merge1 <- cc1[cc1$frag.ratio >= overlap.ratio,]
-  merge2 <- cc2[cc2$frag.ratio >= overlap.ratio,]
-  
-  invisible(list(merge1=merge1, merge2=merge2))
-}
-
-# x[1], x[2] are the start and end of the first fragment
-# and x[3] and x[4] are the start and end of the 2nd fragment 
-# If there are two fragments, we can find the overlap by ordering the
-# start and stop of all the ends and find the difference between the middle two
-overlap.middle  <- function(x){
-
-  x.o <- x[order(x)]
-  f1 <- x[2]-x[1]
-  f2 <- x[4]-x[3]
-  
-  f.overlap <- abs(x.o[3]-x.o[2])
-  f.overlap.ratio <- f.overlap/min(f1, f2)
-
-  return(f.overlap.ratio)
-}
-
-
-
-#######
-####### compute correspondence profile
-#######
-
-# compute upper rank intersection for one t
-# tv: the upper percentile
-# x is sorted by the order of paired variable
-comp.uri <- function(tv, x){
-  n <- length(x)
-  qt <- quantile(x, prob=1-tv[1]) # tv[1] is t
-#  sum(x[1:ceiling(n*tv[2])] >= qt)/n/tv[2]- tv[1]*tv[2] #tv[2] is v
-  sum(x[1:ceiling(n*tv[2])] >= qt)/n
-
-}
-
-# compute the correspondence profile
-# tt, vv: vector between (0, 1) for percentages
-get.uri.2d <- function(x1, x2, tt, vv, spline.df=NULL){
-
-  o <- order(x1, x2, decreasing=T)
-  
-  # sort x2 by the order of x1
-  x2.ordered <- x2[o]
-  
-  tv <- cbind(tt, vv)
-  ntotal <- length(x1) # number of peaks    
-
-  uri <- apply(tv, 1, comp.uri, x=x2.ordered)
-
-  # compute the derivative of URI vs t using small bins
-  uri.binned <- uri[seq(1, length(uri), by=4)]
-  tt.binned <- tt[seq(1, length(uri), by=4)]
-  uri.slope <- (uri.binned[2:(length(uri.binned))] - uri.binned[1:(length(uri.binned)-1)])/(tt.binned[2:(length(uri.binned))] - tt.binned[1:(length(tt.binned)-1)])
-
-  # smooth uri using spline
-  # first find where the jump is and don't fit the jump
-  # this is the index on the left
-  # jump.left.old  <- which.max(uri[-1]-uri[-length(uri)])
-  short.list.length <- min(sum(x1>0)/length(x1), sum(x2>0)/length(x2))
-
-  if(short.list.length < max(tt)){
-    jump.left <- which(tt>short.list.length)[1]-1
-  } else {
-    jump.left <- which.max(tt)
-  }
-
-#  reversed.index <- seq(length(tt), 1, by=-1)
-#  nequal <- sum(uri[reversed.index]== tt[reversed.index])
-#  temp  <- which(uri[reversed.index]== tt[reversed.index])[nequal]
-#  jump.left <- length(tt)-temp
- 
-  if(jump.left < 6){
-   jump.left <- length(tt)
-  }
-    
- 
-  if(is.null(spline.df))
-    uri.spl <- smooth.spline(tt[1:jump.left], uri[1:jump.left], df=6.4)
-  else{
-    uri.spl <- smooth.spline(tt[1:jump.left], uri[1:jump.left], df=spline.df)
-  }
-  # predict the first derivative
-  uri.der <- predict(uri.spl, tt[1:jump.left], deriv=1)
-
-  invisible(list(tv=tv, uri=uri, 
-                 uri.slope=uri.slope, t.binned=tt.binned[2:length(uri.binned)], 
-                 uri.spl=uri.spl, uri.der=uri.der, jump.left=jump.left,
-                 ntotal=ntotal))
- }
-
-
-# change the scale of uri from based on t (percentage) to n (number of peaks or basepairs)
-# this is for plotting multiple pairwise URI's on the same plot 
-scale.t2n <- function(uri){
-
-  ntotal <- uri$ntotal
-  tv <- uri$tv*uri$ntotal
-  uri.uri <- uri$uri*uri$ntotal
-  jump.left <- uri$jump.left
-  uri.spl <- uri$uri.spl
-  uri.spl$x <- uri$uri.spl$x*uri$ntotal 
-  uri.spl$y <- uri$uri.spl$y*uri$ntotal
-
-  t.binned <- uri$t.binned*uri$ntotal
-  uri.slope <- uri$uri.slope
-  uri.der <- uri$uri.der
-  uri.der$x <- uri$uri.der$x*uri$ntotal
-  uri.der$y <- uri$uri.der$y
-
-  uri.n <- list(tv=tv, uri=uri.uri, t.binned=t.binned, uri.slope=uri.slope, uri.spl=uri.spl, uri.der=uri.der, ntotal=ntotal, jump.left=jump.left)
-  return(uri.n)
-} 
-
-
-
-
-# a wrapper for running URI for peaks from peak calling results
-# both data1 and data2 are calling results in narrowpeak format
-compute.pair.uri <- function(data.1, data.2, sig.value1="signal.value", sig.value2="signal.value", spline.df=NULL, overlap.ratio=0){
-
-  tt <- seq(0.01, 1, by=0.01)
-  vv <- tt
-
-  if(sig.value1=="signal.value"){
-    data.1.enrich <- data.frame(chr=data.1$chr, start.ori=data.1$start.ori, stop.ori=data.1$stop.ori, start=data.1$start, stop=data.1$stop, sig.value=data.1$signal.value, signal.value=data.1$signal.value, p.value=data.1$p.value, q.value=data.1$q.value)
-  } else {
-    if(sig.value1=="p.value"){ 
-      data.1.enrich <- data.frame(chr=data.1$chr, start.ori=data.1$start.ori, stop.ori=data.1$stop.ori, start=data.1$start, stop=data.1$stop, sig.value=data.1$p.value, signal.value=data.1$signal.value, p.value=data.1$p.value, q.value=data.1$q.value)
-    } else {
-      if(sig.value1=="q.value"){
-        data.1.enrich <- data.frame(chr=data.1$chr, start.ori=data.1$start.ori, stop.ori=data.1$stop.ori, start=data.1$start, stop=data.1$stop, sig.value=data.1$q.value, signal.value=data.1$signal.value, p.value=data.1$p.value, q.value=data.1$q.value)
-      }
-    }
-  }
-
-  if(sig.value2=="signal.value"){
-    data.2.enrich <- data.frame(chr=data.2$chr, start.ori=data.2$start.ori, stop.ori=data.2$stop.ori, start=data.2$start, stop=data.2$stop, sig.value=data.2$signal.value, signal.value=data.2$signal.value, p.value=data.2$p.value, q.value=data.2$q.value)
-  } else {
-    if(sig.value2=="p.value"){ 
-      data.2.enrich <- data.frame(chr=data.2$chr, start.ori=data.2$start.ori, stop.ori=data.2$stop.ori, start=data.2$start, stop=data.2$stop, sig.value=data.2$p.value, signal.value=data.2$signal.value, p.value=data.2$p.value, q.value=data.2$q.value)
-    } else {
-      if(sig.value2=="q.value"){
-        data.2.enrich <- data.frame(chr=data.2$chr, start.ori=data.2$start.ori, stop.ori=data.2$stop.ori, start=data.2$start, stop=data.2$stop, sig.value=data.2$q.value, signal.value=data.2$signal.value, p.value=data.2$p.value, q.value=data.2$q.value)
-      }
-    }
-  }
-
-  ### by peaks
-  # data12.enrich <- pair.peaks(data.1.enrich, data.2.enrich)
-  data12.enrich <- pair.peaks.filter(data.1.enrich, data.2.enrich, p.value.impute=0, overlap.ratio)
-  uri <- get.uri.2d(as.numeric(as.character(data12.enrich$merge1$sig.value)), as.numeric(as.character(data12.enrich$merge2$sig.value)), tt, vv, spline.df=spline.df)
-  uri.n <- scale.t2n(uri)
-
-  return(list(uri=uri, uri.n=uri.n, data12.enrich=data12.enrich, sig.value1=sig.value1, sig.value2=sig.value2))
-
-
-}
-
-
-
-# compute uri for matched sample
-get.uri.matched <- function(data12, df=10){
-
-  tt <- seq(0.01, 1, by=0.01)
-  vv <- tt
-  uri <- get.uri.2d(data12$sample1$sig.value, data12$sample2$sig.value, tt, vv, spline.df=df)
-
-  # change scale from t to n
-  uri.n <- scale.t2n(uri)
-
-  return(list(uri=uri, uri.n=uri.n))
-  
-}
-
-# map.uv is a pair of significant values corresponding to specified consistency FDR
-# assuming values in map.uv and qvalue are linearly related
-# data.set is the original data set
-# sig.value is the name of the significant value in map.uv, say enrichment
-# nominal.value is the one we want to map to, say q-value
-# 
-map.sig.value <- function(data.set, map.uv, nominal.value){
-
-  index.nominal <- which(names(data.set$merge1)==nominal.value)
-  nentry <- nrow(map.uv)  
-  map.nominal <- rbind(map.uv[, c("sig.value1", "sig.value2")])
-
-  for(i in 1:nentry){
-
-    map.nominal[i, "sig.value1"] <- data.set$merge1[unique(which.min(abs(data.set$merge1$sig.value-map.uv[i, "sig.value1"]))), index.nominal]
-    map.nominal[i, "sig.value2"] <- data.set$merge2[unique(which.min(abs(data.set$merge2$sig.value-map.uv[i, "sig.value2"]))), index.nominal]
-  }
-
-  invisible(map.nominal)
-}
-
-
-############### plot correspondence profile
-
-# plot multiple comparison wrt one template
-# uri.list contains the total number of peaks
-# plot.missing=F: not plot the missing points on the right 
-plot.uri.group <- function(uri.n.list, plot.dir, file.name=NULL, legend.txt, xlab.txt="num of significant peaks", ylab.txt="num of peaks in common", col.start=0, col.txt=NULL, plot.missing=F, title.txt=NULL){
-
-  if(is.null(col.txt))
-    col.txt <- c("black", "red", "purple", "green", "blue", "cyan", "magenta", "orange", "grey")
-
-  n <- length(uri.n.list)  
-
-  ntotal <- c()
-  for(i in 1:n)
-    ntotal[i] <- uri.n.list[[i]]$ntotal
-
-  jump.left <- c()
-  jump.left.der <- c()
-  ncommon <- c()
-  for(i in 1:n){
-#    jump.left[i]  <- which.max(uri.n.list[[i]]$uri[-1]-uri.n.list[[i]]$uri[-length(uri.n.list[[i]]$uri)])
-#    if(jump.left[i] < 6)
-#      jump.left[i] <- length(uri.n.list[[i]]$uri)
-
-##  reversed.index <- seq(length(uri.n.list[[i]]$tv[,1]), 1, by=-1)
-##  nequal <- sum(uri.n.list[[i]]$uri[reversed.index]== uri.n.list[[i]]$tv[reversed.index,1])
-##  temp  <- which(uri.n.list[[i]]$uri[reversed.index]== uri.n.list[[i]]$tv[reversed.index,1])[nequal]
-##  jump.left[i] <- length(uri.n.list[[i]]$tv[,1])-temp
-##print(uri.n.list[[i]]$uri)
-##print(uri.n.list[[i]]$tv[,1])
-##   jump.left[i] <- uri.n.list[[i]]$jump.left
-
-#    jump.left.der[i] <- sum(uri.n.list[[i]]$t.binned < uri.n.list[[i]]$uri.der$x[length(uri.n.list[[i]]$uri.der$x)])
-
-    jump.left[i] <- uri.n.list[[i]]$jump.left
-    jump.left.der[i] <- jump.left[i]
-    ncommon[i] <- uri.n.list[[i]]$tv[jump.left[i],1]
-  }
-
-
-  if(plot.missing){
-    max.peak <- max(ntotal)
-  } else {
-    max.peak <- max(ncommon)*1.05
-  }
-
-  if(!is.null(file.name)){
-    postscript(paste(plot.dir, "uri.", file.name, sep=""))
-    par(mfrow=c(1,1), mar=c(5,5,4,2))
-  }
-
-  plot(uri.n.list[[1]]$tv[,1], uri.n.list[[1]]$uri, type="n", xlab=xlab.txt, ylab=ylab.txt, xlim=c(0, max.peak), ylim=c(0, max.peak), cex.lab=2)
-
-  for(i in 1:n){
-
-    if(plot.missing){ 
-      points(uri.n.list[[i]]$tv[,1], uri.n.list[[i]]$uri, col=col.txt[i+col.start], cex=0.5 )
-    } else {
-      points(uri.n.list[[i]]$tv[1:jump.left[i],1], uri.n.list[[i]]$uri[1:jump.left[i]], col=col.txt[i+col.start], cex=0.5)
-    }
-    lines(uri.n.list[[i]]$uri.spl, col=col.txt[i+col.start], lwd=4)
-  }
-  abline(coef=c(0,1), lty=3)
-  legend(0, max.peak, legend=legend.txt, col=col.txt[(col.start+1):length(col.txt)], lty=1, lwd=3, cex=2)
-  if(!is.null(title))
-    title(title.txt)
-
-  if(!is.null(file.name)){
-    dev.off()
-  }
-
-  if(!is.null(file.name)){
-    postscript(paste(plot.dir, "duri.", file.name, sep=""))
-    par(mfrow=c(1,1), mar=c(5,5,4,2))
-  } 
-  plot(uri.n.list[[1]]$t.binned, uri.n.list[[1]]$uri.slope, type="n", xlab=xlab.txt, ylab="slope", xlim=c(0, max.peak), ylim=c(0, 1.5), cex.lab=2)
-
-  for(i in 1:n){
-#    if(plot.missing){ 
-#      points(uri.n.list[[i]]$t.binned, uri.n.list[[i]]$uri.slope, col=col.txt[i+col.start], cex=0.5)
-#    } else {
-#      points(uri.n.list[[i]]$t.binned[1:jump.left.der[i]], uri.n.list[[i]]$uri.slope[1:jump.left.der[i]], col=col.txt[i+col.start], cex=0.5)
-#    }
-    lines(uri.n.list[[i]]$uri.der, col=col.txt[i+col.start], lwd=4)
-  }
-  abline(h=1, lty=3)
-  legend(0.5*max.peak, 1.5, legend=legend.txt, col=col.txt[(col.start+1):length(col.txt)], lty=1, lwd=3, cex=2)
-
-  if(!is.null(title))
-    title(title.txt)
-
-  if(!is.null(file.name)){
-    dev.off()
-  }
-  
-}
-
-
-
-#######################
-####################### copula fitting for matched peaks
-#######################
-
-# estimation from mixed copula model 
-
-# 4-5-09
-# A nonparametric estimation of mixed copula model
-
-
-# updated
-
-# c1, c2, f1, f2, g1, g2 are vectors
-# c1*f1*g1 and c2*f2*g2 are copula densities for the two components
-# xd1 and yd1 are the values of marginals for the first component
-# xd2 and yd2 are the values of marginals for the 2nd component
-#
-# ez is the prob for being in the consistent group
-get.ez <- function(p, c1, c2, xd1, yd1, xd2, yd2){
-
-  return(p*c1*xd1*yd1/(p*c1*xd1*yd1 + (1-p)*c2*xd2*yd2))
-}
-
-# checked
-
-# this is C_12 not the copula density function c=C_12 * f1* f2
-# since nonparametric estimation is used here for f1 and f2, which
-# are constant throughout the iterations, we don't need them for optimization
-# 
-# bivariate gaussian copula function
-# t and s are vectors of same length, both are percentiles 
-# return a vector
-gaussian.cop.den <- function(t, s, rho){
-
-  A <- qnorm(t)^2 + qnorm(s)^2
-  B <- qnorm(t)*qnorm(s)
-
-  loglik <-  -log(1-rho^2)/2 - rho/(2*(1-rho^2))*(rho*A-2*B)
-
-  return(exp(loglik))
-}
-
-clayton.cop.den <- function(t, s, rho){
-
-  if(rho > 0)
-    return(exp(log(rho+1)-(rho+1)*(log(t)+log(s))-(2+1/rho)*log(t^(-rho) + s^(-rho)-1)))
-
-  if(rho==0)
-    return(1)
-
-  if(rho<0)
-    stop("Incorrect Clayton copula coefficient")
-  
-}
-
-
-# checked
-# estimate rho from Gaussian copula
-mle.gaussian.copula <- function(t, s, e.z){
-
-  # reparameterize to bound from rho=+-1
-  l.c <- function(rho, t, s, e.z){
-#    cat("rho=", rho, "\n")
-    sum(e.z*log(gaussian.cop.den(t, s, rho)))}
-
-  rho.max <- optimize(f=l.c, c(-0.998, 0.998), maximum=T, tol=0.00001, t=t, s=s, e.z=e.z)
-
-#print(rho.max$m)
-
-#cat("cor=", cor(qnorm(t)*e.z, qnorm(s)*e.z), "\t", "rho.max=", rho.max$m, "\n")
-#  return(sign(rho.max$m)/(1+rho.max$m))
-  return(rho.max$m)
-}
-
-
-# estimate mle from Clayton copula, 
-mle.clayton.copula <- function(t, s, e.z){
-
-  l.c <- function(rho, t, s, e.z){
-    lc <- sum(e.z*log(clayton.cop.den(t, s, rho)))
-#    cat("rho=", rho, "\t", "l.c=", lc, "\n")
-    return(lc)
-  }
-
-  rho.max <- optimize(f=l.c, c(0.1, 20), maximum=T, tol=0.00001, t=t, s=s, e.z=e.z)
-
-  return(rho.max$m)
-}
-
-
-
-# updated
-# mixture likelihood of two gaussian copula
-# nonparametric and ranked transformed
-loglik.2gaussian.copula <- function(x, y, p, rho1, rho2, x.mar, y.mar){
- 
-  px.1 <- get.pdf.cdf(x, x.mar$f1)
-  px.2 <- get.pdf.cdf(x, x.mar$f2)
-  py.1 <- get.pdf.cdf(y, y.mar$f1)
-  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  c1 <- gaussian.cop.den(px.1$cdf, py.1$cdf, rho1)
-  c2 <- gaussian.cop.den(px.2$cdf, py.2$cdf, rho2)
-
-  sum(log(p*c1*px.1$pdf*py.1$pdf + (1-p)*c2*px.2$pdf*py.2$pdf))
-}
-
-loglik.2copula <- function(x, y, p, rho1, rho2, x.mar, y.mar, copula.txt){
-
-  px.1 <- pdf.cdf$px.1
-  px.2 <- pdf.cdf$px.2
-  py.1 <- pdf.cdf$py.1
-  py.2 <- pdf.cdf$py.2
-
-  if(copula.txt=="gaussian"){
-    c1 <- gaussian.cop.den(px.1$cdf, py.1$cdf, rho1)
-    c2 <- gaussian.cop.den(px.2$cdf, py.2$cdf, rho2)
-  } else {
-    if(copula.txt=="clayton"){
-      c1 <- clayton.cop.den(px.1$cdf, py.1$cdf, rho1)
-      c2 <- clayton.cop.den(px.2$cdf, py.2$cdf, rho2)
-    }
-  }  
-  sum(log(p*c1*px.1$pdf*py.1$pdf + (1-p)*c2*px.2$pdf*py.2$pdf))
-}
-
-
-# estimate the marginals of each component using histogram estimator in EM
-# return the density, breaks, and cdf of the histogram estimator 
-est.mar.hist <- function(x, e.z, breaks){
-
-  binwidth <- c()
-  nbin <- length(breaks)-1
-  nx <- length(x) 
-
-  # the histogram
-  x1.pdf <- c()
-  x2.pdf <- c()
-  x1.cdf <- c()
-  x2.cdf <- c()
-
-  # the pdf for each point
-  x1.pdf.value <- rep(NA, nx)
-  x2.pdf.value <- rep(NA, nx)
-
-  x1.cdf.value <- rep(NA, nx)
-  x2.cdf.value <- rep(NA, nx) 
-
-  for(i in 1:nbin){
-
-    binwidth[i] <- breaks[i+1] - breaks[i]
-    if(i < nbin)
-      in.bin <- x>= breaks[i] & x < breaks[i+1]
-    else    # last bin
-      in.bin <- x>= breaks[i] & x <=breaks[i+1]
-
-    # each bin add one observation to avoid empty bins
-    # multiple (nx+nbin)/(nx+nbin+1) to avoid blowup when looking up for
-    # quantiles 
-    x1.pdf[i] <- (sum(e.z[in.bin])+1)/(sum(e.z)+nbin)/binwidth[i]*(nx+nbin)/(nx+nbin+1)        
-    x2.pdf[i] <- (sum(1-e.z[in.bin])+1)/(sum(1-e.z)+nbin)/binwidth[i]*(nx+nbin)/(nx+nbin+1) 
-
-
-#    x1.pdf[i] <- sum(e.z[in.bin])/sum(e.z)/binwidth[i]*nx/(nx+1)        
-#    x2.pdf[i] <- sum(1-e.z[in.bin])/sum(1-e.z)/binwidth[i]*nx/(nx+1) 
-    
-# treat each bin as a value for a discrete variable    
-#    x1.cdf[i] <- sum(x1.pdf[1:i]*binwidth[1:i])
-#    x2.cdf[i] <- sum(x2.pdf[1:i]*binwidth[1:i])
-
-
-    # cumulative density before reaching i
-    if(i>1){
-      x1.cdf[i] <- sum(x1.pdf[1:(i-1)]*binwidth[1:(i-1)])
-      x2.cdf[i] <- sum(x2.pdf[1:(i-1)]*binwidth[1:(i-1)])    
-    } else{
-      x1.cdf[i] <- 0
-      x2.cdf[i] <- 0
-    }
-
-    # make a vector of nx to store the values of pdf and cdf for each x
-    # this will speed up the computation dramatically
-    x1.pdf.value[in.bin] <- x1.pdf[i]
-    x2.pdf.value[in.bin] <- x2.pdf[i]
-
-    x1.cdf.value[in.bin] <- x1.cdf[i] + x1.pdf[i]*(x[in.bin]-breaks[i])
-    x2.cdf.value[in.bin] <- x2.cdf[i] + x2.pdf[i]*(x[in.bin]-breaks[i])      
-  }
-
-#  x1.cdf <- cumsum(x1.pdf*binwidth)
-#  x2.cdf <- cumsum(x2.pdf*binwidth)
-
-  f1 <-list(breaks=breaks, density=x1.pdf, cdf=x1.cdf)
-  f2 <-list(breaks=breaks, density=x2.pdf, cdf=x2.cdf)
-
-  f1.value <- list(pdf=x1.pdf.value, cdf=x1.cdf.value)
-  f2.value <- list(pdf=x2.pdf.value, cdf=x2.cdf.value)
-
-  return(list(f1=f1, f2=f2, f1.value=f1.value, f2.value=f2.value))
-}
-
-# estimate the marginal cdf from rank
-est.cdf.rank <- function(x, conf.z){
-
-  # add 1 to prevent blow up
-  x1.cdf <- rank(x[conf.z==1])/(length(x[conf.z==1])+1)
-
-  x2.cdf <- rank(x[conf.z==0])/(length(x[conf.z==0])+1)
-
-  return(list(cdf1=x1.cdf, cdf2=x2.cdf))
-}
-
-# df is a density function with fields: density, cdf and breaks, x is a scalar
-get.pdf <- function(x, df){
-
-  if(x < df$breaks[1])
-    cat("x is out of the range of df\n")
-
-  index <- which(df$breaks >= x)[1]
-
-  if(index==1)
-    index <- index +1
-  return(df$density[index-1])  
-}
-
-# get cdf from histgram estimator for a single value
-get.cdf <- function(x, df){
-
-  index <- which(df$breaks >= x)[1]
-  if(index==1)
-    index <- index +1
-  return(df$cdf[index-1])   
-}
-
-# df is a density function with fields: density, cdf and breaks
-get.pdf.cdf <- function(x.vec, df){
-
-  x.pdf <- sapply(x.vec, get.pdf, df=df)
-  x.cdf <- sapply(x.vec, get.cdf, df=df) 
-  return(list(cdf=x.cdf, pdf=x.pdf))
-}
-
-# E-step
-# x and y are the original observations or ranks
-# rho1 and rho2 are the parameters of each copula
-# f1, f2, g1, g2 are functions, each is a histogram 
-e.step.2gaussian <- function(x, y, p, rho1, rho2, x.mar, y.mar){
-
-  # get pdf and cdf of each component from functions in the corresponding component 
-  px.1 <- get.pdf.cdf(x, x.mar$f1)
-  px.2 <- get.pdf.cdf(x, x.mar$f2)
-  py.1 <- get.pdf.cdf(y, y.mar$f1)
-  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  c1 <- gaussian.cop.den(px.1$cdf, py.1$cdf, rho1)
-  c2 <- gaussian.cop.den(px.2$cdf, py.2$cdf, rho2)
-  
-  return(get.ez(p, c1, c2, px.1$pdf, py.1$pdf, px.2$pdf, py.2$pdf))
-}
-
-# E-step
-# rho1 and rho2 are the parameters of each copula 
-e.step.2copula <- function(x, y, p, rho1, rho2, x.mar, y.mar, copula.txt){
-
-  # get pdf and cdf of each component from functions in the corresponding component 
-  px.1 <- get.pdf.cdf(x, x.mar$f1)
-  px.2 <- get.pdf.cdf(x, x.mar$f2)
-  py.1 <- get.pdf.cdf(y, y.mar$f1)
-  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  if(copula.txt=="gaussian"){
-    c1 <- gaussian.cop.den(px.1$cdf, py.1$cdf, rho1)
-    c2 <- gaussian.cop.den(px.2$cdf, py.2$cdf, rho2)
-  } else {
-    if(copula.txt=="clayton"){
-      c1 <- clayton.cop.den(px.1$cdf, py.1$cdf, rho1)
-      c2 <- clayton.cop.den(px.2$cdf, py.2$cdf, rho2)
-    } 
-  }
-  return(get.ez(p, c1, c2, px.1$pdf, py.1$pdf, px.2$pdf, py.2$pdf))
-}
-
-
-
-
-# M-step
-m.step.2gaussian <- function(x, y, e.z, breaks){
-
-  # compute f1, f2, g1 and g2
-  x.mar <- est.mar.hist(x, e.z, breaks)
-  y.mar <- est.mar.hist(y, e.z, breaks)  
-
-  px.1 <- get.pdf.cdf(x, x.mar$f1)
-  px.2 <- get.pdf.cdf(x, x.mar$f2)
-  py.1 <- get.pdf.cdf(y, y.mar$f1)
-  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  rho1 <- mle.gaussian.copula(px.1$cdf, py.1$cdf, e.z)  
-  rho2 <- mle.gaussian.copula(px.2$cdf, py.2$cdf, 1-e.z) 
-
-  p <- sum(e.z)/length(e.z) 
-
-  return(list(p=p, rho1=rho1, rho2=rho2, x.mar=x.mar, y.mar=y.mar))
-}
-
-m.step.2copula <- function(x, y, e.z, breaks, copula.txt){
-
-  # compute f1, f2, g1 and g2
-  x.mar <- est.mar.hist(x, e.z, breaks)
-  y.mar <- est.mar.hist(y, e.z, breaks)  
-
-  px.1 <- get.pdf.cdf(x, x.mar$f1)
-  px.2 <- get.pdf.cdf(x, x.mar$f2)
-  py.1 <- get.pdf.cdf(y, y.mar$f1)
-  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  if(copula.txt=="gaussian"){
-    rho1 <- mle.gaussian.copula(px.1$cdf, py.1$cdf, e.z)  
-    rho2 <- mle.gaussian.copula(px.2$cdf, py.2$cdf, 1-e.z) 
-  } else {
-    if(copula.txt=="clayton"){
-      rho1 <- mle.clayton.copula(px.1$cdf, py.1$cdf, e.z)  
-      rho2 <- mle.clayton.copula(px.2$cdf, py.2$cdf, 1-e.z)      
-    }
-  }
-  
-  p <- sum(e.z)/length(e.z) 
-
-  return(list(p=p, rho1=rho1, rho2=rho2, x.mar=x.mar, y.mar=y.mar))
-}
-
-
-
-# E-step: pass values
-# x and y are the original observations or ranks
-# rho1 and rho2 are the parameters of each copula
-# f1, f2, g1, g2 are functions, each is a histogram 
-e.step.2gaussian.value <- function(x, y, p, rho1, rho2, pdf.cdf){
-
-  c1 <- gaussian.cop.den(pdf.cdf$px.1$cdf, pdf.cdf$py.1$cdf, rho1)
-  c2 <- gaussian.cop.den(pdf.cdf$px.2$cdf, pdf.cdf$py.2$cdf, rho2)
-  
-  e.z <- get.ez(p, c1, c2, pdf.cdf$px.1$pdf, pdf.cdf$py.1$pdf, 
-               pdf.cdf$px.2$pdf, pdf.cdf$py.2$pdf)
-  return(e.z)
-}
-
-
-e.step.2copula.value <- function(x, y, p, rho1, rho2, pdf.cdf, copula.txt){
-
-  if(copula.txt =="gaussian"){
-    c1 <- gaussian.cop.den(pdf.cdf$px.1$cdf, pdf.cdf$py.1$cdf, rho1)
-    c2 <- gaussian.cop.den(pdf.cdf$px.2$cdf, pdf.cdf$py.2$cdf, rho2)
-  } else {
-    if(copula.txt =="clayton"){
-      c1 <- clayton.cop.den(pdf.cdf$px.1$cdf, pdf.cdf$py.1$cdf, rho1)
-      c2 <- clayton.cop.den(pdf.cdf$px.2$cdf, pdf.cdf$py.2$cdf, rho2)      
-    }
-  }
-  
-  e.z <- get.ez(p, c1, c2, pdf.cdf$px.1$pdf, pdf.cdf$py.1$pdf, 
-               pdf.cdf$px.2$pdf, pdf.cdf$py.2$pdf)
-  return(e.z)
-}
-
-
-# M-step: pass values
-m.step.2gaussian.value <- function(x, y, e.z, breaks, fix.rho2){
-
-  # compute f1, f2, g1 and g2
-  x.mar <- est.mar.hist(x, e.z, breaks)
-  y.mar <- est.mar.hist(y, e.z, breaks)  
-
-#  px.1 <- get.pdf.cdf(x, x.mar$f1)
-#  px.2 <- get.pdf.cdf(x, x.mar$f2)
-#  py.1 <- get.pdf.cdf(y, y.mar$f1)
-#  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  px.1 <- x.mar$f1.value
-  px.2 <- x.mar$f2.value
-  py.1 <- y.mar$f1.value
-  py.2 <- y.mar$f2.value
-
-  rho1 <- mle.gaussian.copula(px.1$cdf, py.1$cdf, e.z)  
-
-  if(!fix.rho2)
-    rho2 <- mle.gaussian.copula(px.2$cdf, py.2$cdf, 1-e.z) 
-  else
-    rho2 <- 0
-
-  p <- sum(e.z)/length(e.z) 
-
-  pdf.cdf <- list(px.1=px.1, px.2=px.2, py.1=py.1, py.2=py.2)
-
-  return(list(p=p, rho1=rho1, rho2=rho2, x.mar=x.mar, y.mar=y.mar,
-              pdf.cdf=pdf.cdf))
-}
-
-m.step.2gaussian.value2 <- function(x, y, e.z, breaks, fix.rho2, x.mar, y.mar){
-
-  # compute f1, f2, g1 and g2
-#  x.mar <- est.mar.hist(x, e.z, breaks)
-#  y.mar <- est.mar.hist(y, e.z, breaks)  
-
-#  px.1 <- get.pdf.cdf(x, x.mar$f1)
-#  px.2 <- get.pdf.cdf(x, x.mar$f2)
-#  py.1 <- get.pdf.cdf(y, y.mar$f1)
-#  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  px.1 <- x.mar$f1.value
-  px.2 <- x.mar$f2.value
-  py.1 <- y.mar$f1.value
-  py.2 <- y.mar$f2.value
-
-  rho1 <- mle.gaussian.copula(px.1$cdf, py.1$cdf, e.z)  
-
-  if(!fix.rho2)
-    rho2 <- mle.gaussian.copula(px.2$cdf, py.2$cdf, 1-e.z) 
-  else
-    rho2 <- 0
-
-  p <- sum(e.z)/length(e.z) 
-
-  pdf.cdf <- list(px.1=px.1, px.2=px.2, py.1=py.1, py.2=py.2)
-
-  return(list(p=p, rho1=rho1, rho2=rho2, x.mar=x.mar, y.mar=y.mar,
-              pdf.cdf=pdf.cdf))
-}
-
-
-
-m.step.2copula.value <- function(x, y, e.z, breaks, fix.rho2, copula.txt){
-
-  # compute f1, f2, g1 and g2
-  x.mar <- est.mar.hist(x, e.z, breaks)
-  y.mar <- est.mar.hist(y, e.z, breaks)  
-
-#  px.1 <- get.pdf.cdf(x, x.mar$f1)
-#  px.2 <- get.pdf.cdf(x, x.mar$f2)
-#  py.1 <- get.pdf.cdf(y, y.mar$f1)
-#  py.2 <- get.pdf.cdf(y, y.mar$f2)
-
-  px.1 <- x.mar$f1.value
-  px.2 <- x.mar$f2.value
-  py.1 <- y.mar$f1.value
-  py.2 <- y.mar$f2.value
-
-  if(copula.txt=="gaussian"){
-    rho1 <- mle.gaussian.copula(px.1$cdf, py.1$cdf, e.z)  
-    
-    if(!fix.rho2)
-      rho2 <- mle.gaussian.copula(px.2$cdf, py.2$cdf, 1-e.z) 
-    else
-      rho2 <- 0
-  } else {
-
-    if(copula.txt=="clayton"){
-      rho1 <- mle.clayton.copula(px.1$cdf, py.1$cdf, e.z)  
-    
-      if(!fix.rho2)
-        rho2 <- mle.clayton.copula(px.2$cdf, py.2$cdf, 1-e.z) 
-      else
-        rho2 <- 0
-    }    
-  }
-    
-  p <- sum(e.z)/length(e.z) 
-
-  pdf.cdf <- list(px.1=px.1, px.2=px.2, py.1=py.1, py.2=py.2)
-
-  return(list(p=p, rho1=rho1, rho2=rho2, x.mar=x.mar, y.mar=y.mar,
-              pdf.cdf=pdf.cdf))
-}
-
-
-
-
-# updated
-# mixture likelihood of two gaussian copula
-# nonparametric and ranked transformed
-loglik.2gaussian.copula.value <- function(x, y, p, rho1, rho2, pdf.cdf){
-
-  px.1 <- pdf.cdf$px.1
-  px.2 <- pdf.cdf$px.2
-  py.1 <- pdf.cdf$py.1
-  py.2 <- pdf.cdf$py.2
-
-  c1 <- gaussian.cop.den(px.1$cdf, py.1$cdf, rho1)
-  c2 <- gaussian.cop.den(px.2$cdf, py.2$cdf, rho2)
-
-  sum(log(p*c1*px.1$pdf*py.1$pdf + (1-p)*c2*px.2$pdf*py.2$pdf))
-}
-
-
-
-# updated
-# mixture likelihood of two gaussian copula
-# nonparametric and ranked transformed
-loglik.2copula.value <- function(x, y, p, rho1, rho2, pdf.cdf, copula.txt){
-
-  px.1 <- pdf.cdf$px.1
-  px.2 <- pdf.cdf$px.2
-  py.1 <- pdf.cdf$py.1
-  py.2 <- pdf.cdf$py.2
-
-  if(copula.txt=="gaussian"){
-    c1 <- gaussian.cop.den(px.1$cdf, py.1$cdf, rho1)
-    c2 <- gaussian.cop.den(px.2$cdf, py.2$cdf, rho2)
-  } else {
-    if(copula.txt=="clayton"){
-      c1 <- clayton.cop.den(px.1$cdf, py.1$cdf, rho1)
-      c2 <- clayton.cop.den(px.2$cdf, py.2$cdf, rho2)
-    }
-  }
-
-  sum(log(p*c1*px.1$pdf*py.1$pdf + (1-p)*c2*px.2$pdf*py.2$pdf))
-}
-
-
-
-# EM for 2 Gaussian, speed up computation, unfinished
-
-em.2gaussian.quick <- function(x, y, p0, rho1.0, rho2.0, eps, fix.p=F, stoc=T, fix.rho2=T){
-
-  x <- rank(x, tie="random")
-  y <- rank(y, tie="random")
-
-#  x <- rank(x, tie="average")
-#  y <- rank(y, tie="average")
-
-  # nbin=20
-  xy.min <- min(x, y)
-  xy.max <- max(x, y)
-  binwidth <- (xy.max-xy.min)/50
-  breaks <- seq(xy.min-binwidth/100, xy.max+binwidth/100, by=(xy.max-xy.min+binwidth/50)/50)
-#  breaks <- seq(xy.min, xy.max, by=binwidth)
-  
-
-  # initiate marginals 
-  # initialization: first p0 data has 
-#  e.z <- e.step.2gaussian(x, y, p0, rho1.0, rho2.0, x0.mar, y0.mar) # this starting point assumes two components are overlapped
-
-  e.z <- c(rep(0.9, round(length(x)*p0)), rep(0.1, length(x)-round(length(x)*p0)))
-
-  if(!stoc)
-    para <- m.step.2gaussian.value(x, y, e.z, breaks, fix.rho2)
-  else 
-    para <- m.step.2gaussian.stoc.value(x, y, e.z, breaks, fix.rho2)
-
-
-  if(fix.p){
-    p <- p0
-  } else {
-    p <- para$p  
-  }
-
-  if(fix.rho2){
-    rho2 <- rho2.0
-  } else {
-    rho2 <- para$rho2
-  }
-
-#  rho1 <- 0.8
-  rho1 <- para$rho1
-
-  l0 <- loglik.2gaussian.copula.value(x, y, p, rho1, rho2, para$pdf.cdf)
-
-  loglik.trace <- c()
-  loglik.trace[1] <- l0
-#  loglik.trace[2] <- l1
-  to.run <- T
-
-  i <- 2
-
-  # this two lines to remove
-#  x.mar <- est.mar.hist(x, e.z, breaks)
-#  y.mar <- est.mar.hist(y, e.z, breaks)  
-  
-  while(to.run){
-
-    e.z <- e.step.2gaussian.value(x, y, p, rho1, rho2, para$pdf.cdf) 
-    if(!stoc)
-      para <- m.step.2gaussian.value(x, y, e.z, breaks, fix.rho2)
-    else
-      para <- m.step.2gaussian.stoc.value(x, y, e.z, breaks, fix.rho2)
-
-    # fix x.mar and y.mar : to remove
-#    if(!stoc)
-#      para <- m.step.2gaussian.value2(x, y, e.z, breaks, fix.rho2, x.mar, y.mar)
-#    else
-#      para <- m.step.2gaussian.stoc.value(x, y, e.z, breaks, fix.rho2)
-
-    
-    if(fix.p){
-      p <- p0
-    } else {
-      p <- para$p  
-    }
-
-    if(fix.rho2){
-      rho2 <- rho2.0
-    } else {
-      rho2 <- para$rho2
-    }
-
-#    rho1 <- 0.8
-    rho1 <- para$rho1
-
-  #  l0 <- l1
-    l1 <- loglik.2gaussian.copula.value(x, y, p, rho1, rho2, para$pdf.cdf)
-    loglik.trace[i] <- l1
-
-#cat("l1=", l1, "\n") 
-
-    # Aitken acceleration criterion
-    if(i > 2){
-      l.inf <- loglik.trace[i-2] + (loglik.trace[i-1] - loglik.trace[i-2])/(1-(loglik.trace[i]-loglik.trace[i-1])/(loglik.trace[i-1]-loglik.trace[i-2])) 
-      to.run <- abs(l.inf - loglik.trace[i]) > eps 
-#cat("para=", "p=", para$p, " rho1=", rho1, " rho2=", rho2, "\n")
-#cat("l.inf=", l.inf, "\n")
-#cat(l.inf-loglik.trace[i], "\n")     
-    }
-
-    i <- i+1
-  }
-
-  bic <- -2*l1 + (2*(length(breaks)-1+1)+1-fix.p-fix.rho2)*log(length(x)) # parameters
-  return(list(para=list(p=para$p, rho1=rho1, rho2=rho2), 
-              loglik=l1, bic=bic, e.z=e.z, conf.z = para$conf.z, 
-              loglik.trace=loglik.trace, x.mar=para$x.mar, y.mar=para$y.mar,
-              breaks=breaks))
-}
-
-
-
-em.2copula.quick <- function(x, y, p0, rho1.0, rho2.0, eps, fix.p=F, stoc=T, fix.rho2=T, copula.txt, nbin=50){
-
-  x <- rank(x, tie="random")
-  y <- rank(y, tie="random")
-
-#  x <- rank(x, tie="first")
-#  y <- rank(y, tie="first")
-
-  # nbin=50
-  xy.min <- min(x, y)
-  xy.max <- max(x, y)
-  binwidth <- (xy.max-xy.min)/50
-  breaks <- seq(xy.min-binwidth/100, xy.max+binwidth/100, by=(xy.max-xy.min+binwidth/50)/nbin)  
-#  breaks <- seq(xy.min, xy.max, by=binwidth)
-  
-  # initiate marginals 
-  # initialization: first p0 data has 
-#  e.z <- e.step.2gaussian(x, y, p0, rho1.0, rho2.0, x0.mar, y0.mar) # this starting point assumes two components are overlapped
-
-  e.z <- c(rep(0.9, round(length(x)*p0)), rep(0.1, length(x)-round(length(x)*p0)))
-
-
-  if(!stoc)
-    para <- m.step.2copula.value(x, y, e.z, breaks, fix.rho2, copula.txt)
-  else 
-    para <- m.step.2copula.stoc.value(x, y, e.z, breaks, fix.rho2, copula.txt)
-
-  if(fix.p){
-    p <- p0
-  } else {
-    p <- para$p  
-  }
-
-  if(fix.rho2){
-    rho2 <- rho2.0
-  } else {
-    rho2 <- para$rho2
-  }
-
-  l0 <- loglik.2copula.value(x, y, p, para$rho1, rho2, para$pdf.cdf, copula.txt)
-
-  loglik.trace <- c()
-  loglik.trace[1] <- l0
-#  loglik.trace[2] <- l1
-  to.run <- T
-
-  i <- 2
-
-  while(to.run){
-
-    e.z <- e.step.2copula.value(x, y, p, para$rho1, rho2, para$pdf.cdf, copula.txt) 
-    if(!stoc)
-      para <- m.step.2copula.value(x, y, e.z, breaks, fix.rho2, copula.txt)
-    else
-      para <- m.step.2copula.stoc.value(x, y, e.z, breaks, fix.rho2, copula.txt)
-
-    if(fix.p){
-      p <- p0
-    } else {
-      p <- para$p  
-    }
-
-    if(fix.rho2){
-      rho2 <- rho2.0
-    } else {
-      rho2 <- para$rho2
-    }
-
-
-  #  l0 <- l1
-    l1 <- loglik.2copula.value(x, y, p, para$rho1, rho2, para$pdf.cdf, copula.txt)
-    loglik.trace[i] <- l1
-
-cat("l1=", l1, "\n") 
-
-    # Aitken acceleration criterion
-    if(i > 2){
-      l.inf <- loglik.trace[i-2] + (loglik.trace[i-1] - loglik.trace[i-2])/(1-(loglik.trace[i]-loglik.trace[i-1])/(loglik.trace[i-1]-loglik.trace[i-2])) 
-      to.run <- abs(l.inf - loglik.trace[i]) > eps 
-cat("para=", "p=", para$p, " rho1=", para$rho1, " rho2=", rho2, "\n")
-#cat("l.inf=", l.inf, "\n")
-#cat(l.inf-loglik.trace[i], "\n")     
-    }
-
-    i <- i+1
-  }
-
-  bic <- -2*l1 + (2*(length(breaks)-1+1)+1-fix.p-fix.rho2)*log(length(x)) # parameters
-  return(list(para=list(p=para$p, rho1=para$rho1, rho2=rho2), 
-              loglik=l1, bic=bic, e.z=e.z, conf.z = para$conf.z, 
-              loglik.trace=loglik.trace, x.mar=para$x.mar, y.mar=para$y.mar,
-              breaks=breaks))
-}
-
-
-#######################
-####################### fit EM procedure for the matched peaks
-#######################
-
-# remove the unmatched ones
-#rm.unmatch <- function(sample1, sample2, p.value.impute=0){
-#
-#  sample1.prune <- sample1[sample1$sig.value > p.value.impute & sample2$sig.value > p.value.impute,]
-#  sample2.prune <- sample2[sample1$sig.value > p.value.impute & sample2$sig.value > p.value.impute,]
-# 
-#  invisible(list(sample1=sample1.prune$sig.value, sample2=sample2.prune$sig.value))
-#}
-
-
-# fit 2-component model
-#fit.em <- function(sample12, fix.rho2=T){
-#
-#  prune.sample <- rm.unmatch(sample12$merge1, sample12$merge2)
-#
-#  em.fit <- em.2gaussian.quick(-prune.sample$sample1, -prune.sample$sample2,
-# p0=0.5, rho1.0=0.7, rho2.0=0, eps=0.01, fix.p=F, stoc=F, fix.rho2)
-#
-#  invisible(list(em.fit=em.fit, data.pruned=prune.sample))
-#}
-
-
-rm.unmatch <- function(sample1, sample2, p.value.impute=0){
-
-  sample1.prune <- sample1[sample1$sig.value > p.value.impute & sample2$sig.value > p.value.impute,]
-  sample2.prune <- sample2[sample1$sig.value > p.value.impute & sample2$sig.value > p.value.impute,]
- 
-  invisible(list(sample1=sample1.prune, sample2=sample2.prune))
-}
-
-
-# fit 2-component model
-fit.em <- function(sample12, fix.rho2=T){
-
-  prune.sample <- rm.unmatch(sample12$merge1, sample12$merge2)
-
-  em.fit <- em.2gaussian.quick(-prune.sample$sample1$sig.value, -prune.sample$sample2$sig.value,
- p0=0.5, rho1.0=0.7, rho2.0=0, eps=0.01, fix.p=F, stoc=F, fix.rho2)
-
-  invisible(list(em.fit=em.fit, data.pruned=prune.sample))
-}
-
-
-
-fit.2copula.em <- function(sample12, fix.rho2=T, copula.txt){
-
-  prune.sample <- rm.unmatch(sample12$merge1, sample12$merge2)
-
-#  o <- order(prune.sample$sample1)
-#  n <- length(prune.sample$sample1)
-    
-#  para <- init(prune.sample$sample1$sig.value, prune.sample$sample2$sig.value, c(rep(0, round(n/3)), rep(c(0,1), round(n/6)), rep(1, n-round(n/3)-round(n/6))))
-
-#  temp <- init.dist(f0, f1)
-  para <- list()
-  para$rho <- 0.6
-  para$p <- 0.3
-  para$mu <- 2.5
-  para$sigma <- 1
-##  para$mu <- -temp$mu
-##  para$sigma <- temp$sigma
-#cat("mu=", para$mu, "sigma=", para$sigma, "\n")
-  
-#  em.fit <- em.transform.1loop(-prune.sample$sample1, -prune.sample$sample2,
-  cat("EM is running")
-  em.fit <- em.transform(prune.sample$sample1$sig.value, prune.sample$sample2$sig.value, para$mu, para$sigma, para$rho, para$p, eps=0.01)
-
-  invisible(list(em.fit=em.fit, data.pruned=prune.sample))
-}
-
-
-
-
-# fit 1-component model
-fit.1.component <- function(data.pruned, breaks){
-
-#  gaussian.1 <- fit.gaussian.1(-data.pruned$sample1$sig.value, -data.pruned$sample2$sig.value, breaks)
-#  clayton.1 <- fit.clayton.1(-data.pruned$sample1$sig.value, -data.pruned$sample2$sig.value, breaks)
-
-  gaussian.1 <- fit.gaussian.1(-data.pruned$sample1, -data.pruned$sample2, breaks)
-  clayton.1 <- fit.clayton.1(-data.pruned$sample1, -data.pruned$sample2, breaks)
-
-  return(list(gaussian.1=gaussian.1, clayton.1=clayton.1))
-}
-
-
-
-#################
-# Fit a single component  
-#################
-
-# a single gaussian copula
-# if breaks=NULL, use empirical pdf, otherwise use histogram estimate
-fit.gaussian.1 <- function(x, y, breaks=NULL){
-
-  # rank transformed and compute the empirical cdf
-  t <- emp.mar.cdf.rank(x)
-  s <- emp.mar.cdf.rank(y)
-
-  mle.rho <- mle.gaussian.copula(t, s, rep(1, length(t)))
-
-  c1 <- gaussian.cop.den(t, s, mle.rho)
-cat("c1", sum(log(c1)), "\n")
-
-  if(is.null(breaks)){
-    f1 <- emp.mar.pdf.rank(t)
-    f2 <- emp.mar.pdf.rank(s)
-  } else {
-    x.mar <- est.mar.hist(rank(x), rep(1, length(x)), breaks)
-    y.mar <- est.mar.hist(rank(y), rep(1, length(y)), breaks)
-
-    f1 <- x.mar$f1.value$pdf  # only one component
-    f2 <- y.mar$f1.value$pdf
-  }
-
-
-cat("f1", sum(log(f1)), "\n")
-cat("f2", sum(log(f2)), "\n")
-
-  loglik <- sum(log(c1)+log(f1)+log(f2))
-
-  bic <- -2*loglik + log(length(t))*(1+length(breaks)-1)
-
-  return(list(rho=mle.rho, loglik=loglik, bic=bic))
-}
-
-
-# a single Clayton copula
-fit.clayton.1 <- function(x, y, breaks=NULL){
-
-  # rank transformed and compute the empirical cdf
-  t <- emp.mar.cdf.rank(x)
-  s <- emp.mar.cdf.rank(y)
-
-  mle.rho <- mle.clayton.copula(t, s, rep(1, length(t)))
-
-  c1 <- clayton.cop.den(t, s, mle.rho)
-
-  if(is.null(breaks)){
-    f1 <- emp.mar.pdf.rank(t)
-    f2 <- emp.mar.pdf.rank(s)
-  } else {
-    x.mar <- est.mar.hist(rank(x), rep(1, length(x)), breaks)
-    y.mar <- est.mar.hist(rank(y), rep(1, length(y)), breaks)
-
-    f1 <- x.mar$f1.value$pdf  # only one component
-    f2 <- y.mar$f1.value$pdf
-  }
-
-  loglik <- sum(log(c1)+log(f1)+log(f2))
-
-  bic <- -2*loglik + log(length(t))*(1+length(breaks)-1)
-
-  return(list(rho=mle.rho, tau=rho/(rho+2), loglik=loglik, bic=bic)) 
-}
-
-## obsolete function (01-06-2010)
-## compute the average posterior probability to belong to the random component
-## for peaks selected at different cutoffs 
-comp.uri.ez <- function(tt, u, v, e.z){
-
-   u.t <- quantile(u, prob=(1-tt))
-   v.t <- quantile(v, prob=(1-tt))
-
- #  ez <- mean(e.z[u >= u.t & v >=u.t]) Is this wrong?
-   ez <- mean(e.z[u >= u.t & v >=v.t])
-
-   return(ez)
-}
-
-## obsolete function (01-06-2010)
-# compute the largest posterior error probability corresponding to
-# the square centered at the origin and spanned top tt% on both coordinates
-# so the consistent low rank ones are excluded
-# boundary.txt: either "max" or "min", if it is error prob, use "max"
-comp.ez.cutoff <- function(tt, u, v, e.z, boundary.txt){
-
-   u.t <- quantile(u, prob=(1-tt))
-   v.t <- quantile(v, prob=(1-tt))
-
-   if(boundary.txt == "max"){
- #    ez.bound <- max(e.z[u >= u.t & v >=u.t])
-     ez.bound <- max(e.z[u >= u.t & v >=v.t])
-   } else {
- #    ez.bound <- min(e.z[u >= u.t & v >=u.t])
-     ez.bound <- min(e.z[u >= u.t & v >=v.t])     
-   }
-
-   return(ez.bound)
-
-}
-
-# obsolete functions: 01-06-2010
-# compute the error rate
-# u.t and v.t are the quantiles
-# this one is used for the plots generated initially in the brief writeup  
-# and it was used for processing merged data in July before the IDR definition
-# is formalized
-# It does not implement the current definition of IDR
-get.ez.tt.old  <- function(em.fit, reverse=T, fdr.level=c(0.01, 0.05, 0.1)){
-
-  u <- em.fit$data.pruned$sample1
-  v <- em.fit$data.pruned$sample2
-
-  tt <- seq(0.01, 0.99, by=0.01)
-  if(reverse){ 
-    e.z <-  1-em.fit$em.fit$e.z # this is the error prob
-    uri.ez <- sapply(tt, comp.uri.ez, u=u, v=v, e.z=e.z)
-    ez.bound <- sapply(tt, comp.ez.cutoff, u=u, v=v, e.z=e.z, boundary.txt="max") 
-  } else {
-    e.z <-  em.fit$em.fit$e.z
-    uri.ez <- sapply(tt, comp.uri.ez, u=u, v=v, e.z=e.z)
-    ez.bound <- sapply(tt, comp.ez.cutoff, u=u, v=v, e.z=e.z, boundary.txt="min") 
-  }
-
-  u.t <- quantile(u, prob=(1-tt))
-  v.t <- quantile(v, prob=(1-tt))  
-
-  # find the levels on the two replicates
-  sig.value1 <- c()
-  sig.value2 <- c()
-  error.prob.cutoff <- c()
-  n.selected.match <- c()
-
-  for(i in 1:length(fdr.level)){
-
-    # find which uri.ez is closet to fdr.level
-    index <- which.min(abs(uri.ez - fdr.level[i]))
-    sig.value1[i] <- u.t[index]
-    sig.value2[i] <- v.t[index]
-    error.prob.cutoff[i] <- ez.bound[index]  
-    if(reverse){
-      n.selected.match[i] <- sum(e.z<=ez.bound[index])    
-    } else {
-      n.selected.match[i] <- sum(e.z>=ez.bound[index])    
-    }
-  }   
-
-  # output the cutoff of posterior probability, signal values on two replicates
-  map.uv <- cbind(error.prob.cutoff, sig.value1, sig.value2, n.selected.match)
-
-  return(list(n=tt*length(u), uri.ez=uri.ez, u.t=u.t, v.t=v.t, tt=tt, fdr.level=fdr.level,  map.uv=map.uv, e.z=e.z, error.prob.cutoff=error.prob.cutoff))
-}
-
-# created: 01-06-2010
-# Output IDR at various number of selected peaks
-# Find cutoff (idr cutoff, sig.value cutoff on each replicate) for specified IDR level
-# IDR definition is similar to FDR
-get.ez.tt <- function(em.fit, idr.level=c(0.01, 0.05, 0.1)){
-
-#  u <- em.fit$data.pruned$sample1$sig.value
-#  v <- em.fit$data.pruned$sample2$sig.value
-  u <- em.fit$data.pruned$sample1
-  v <- em.fit$data.pruned$sample2
-  
-  e.z <-  1-em.fit$em.fit$e.z # this is the error prob
-  
-  o <- order(e.z)
-  e.z.ordered <- e.z[o]
-  n.select <- c(1:length(e.z))
-  IDR <- cumsum(e.z.ordered)/n.select
-
-  u.o <- u[o]
-  v.o <- v[o]
-
-  n.level <- length(idr.level)
-#  sig.value1 <- rep(NA, n.level)
-#  sig.value2 <- rep(NA, n.level)
-  ez.cutoff <- rep(NA, n.level)
-  n.selected <- rep(NA, n.level)
-  
-  for(i in 1:length(idr.level)){
-
-    # find which uri.ez is closet to fdr.level
-    index <- which.min(abs(IDR - idr.level[i]))
-#    sig.value1[i] <- min(u.o[1:index])
-#    sig.value2[i] <- min(v.o[1:index])
-    ez.cutoff[i] <- e.z[index]      
-    n.selected[i] <- sum(e.z<=ez.cutoff[i])    
-  }   
-
-  # output the cutoff of posterior probability, number of selected overlapped peaks 
-#  map.uv <- cbind(ez.cutoff, sig.value1, sig.value2, n.selected)
-
-  map.uv <- cbind(ez.cutoff, n.selected)
-
-  return(list(n=n.select, IDR=IDR, idr.level=idr.level, map.uv=map.uv))
-}   
-  
-#  return(list(n=tt*length(u), uri.ez=uri.ez,  fdr.level=fdr.level,  map.uv=map.uv, e.z=e.z, error.prob.cutoff=error.prob.cutoff))  
-  
-
-
-
-
-### compute the mean of the marginals
-get.mar.mean <- function(em.out){
-
-  x.f1 <- em.out$x.mar$f1
-  x.f2 <- em.out$x.mar$f2
-
-  y.f1 <- em.out$y.mar$f1
-  y.f2 <- em.out$y.mar$f2
-
-  x.stat1 <- get.hist.mean(x.f1)
-  x.stat2 <- get.hist.mean(x.f2)
-  y.stat1 <- get.hist.mean(y.f1)
-  y.stat2 <- get.hist.mean(y.f2)
-
-  return(list(x.mean1=x.stat1$mean, x.mean2=x.stat2$mean, 
-              y.mean1=y.stat1$mean, y.mean2=y.stat2$mean,
-              x.sd1=x.stat1$sd, x.sd2=x.stat2$sd, 
-              y.sd1=y.stat1$sd, y.sd2=y.stat2$sd
-              ))
-
-}
-
-
-# compute the mean of marginals
-get.hist.mean  <- function(x.f){
-
-  nbreaks <- length(x.f$breaks)
-  x.bin <- x.f$breaks[-1]-x.f$breaks[-nbreaks]
-
-  x.mid <- (x.f$breaks[-nbreaks]+x.f$breaks[-1])/2
-  x.mean <- sum(x.mid*x.f$density*x.bin)
-  x.sd <- sqrt(sum(x.mid*x.mid*x.f$density*x.bin)-x.mean^2)
-  
-  return(list(mean=x.mean, sd=x.sd))
-}
-
-get.hist.var <- function(x.f){
-
-  nbreaks <- length(x.f$breaks)
-  x.bin <- x.f$breaks[-1]-x.f$breaks[-nbreaks]
-
-  x.mid <- (x.f$breaks[-nbreaks]+x.f$breaks[-1])/2
-  x.mean <- sum(x.mid*x.f$density*x.bin)
-
-  return(mean=x.mean)  
-}
-
-# obsolete function (01-06-2010)
-# plot 
-plot.ez.group.old <- function(ez.list, plot.dir, file.name=NULL, legend.txt, y.lim=NULL, xlab.txt="num of significant peaks",  ylab.txt="avg posterior prob of being random", col.txt=NULL, title.txt=NULL){
-
-  if(is.null(col.txt))
-    col.txt <- c("black", "red", "purple", "green", "blue", "cyan", "magenta", "orange", "grey")
-
-  x <- c()
-  y <- c()
-
-  for(i in 1:length(ez.list)){
-    x <- c(x, ez.list[[i]]$n)
-      
-    y <- c(y, ez.list[[i]]$uri.ez)
-  }
-
-  if(is.null(y.lim))
-    y.lim <- c(0, max(y))
-
-  if(!is.null(file.name)){
-    postscript(paste(plot.dir, "ez.", file.name, sep=""))
-    par(mfrow=c(1,1), mar=c(5,5,4,2))
-  }
-
-  plot(x, y, ylim=y.lim, type="n", xlab=xlab.txt, ylab=ylab.txt, lwd=5, cex=5, cex.axis=2, cex.lab=2)
-
-  for(i in 1:length(ez.list)){
-    lines(ez.list[[i]]$n, ez.list[[i]]$uri.ez, col=col.txt[i], cex=2, lwd=5)    
-  }
-
-#   plot(ez.list[[1]]$u.t, y, ylim=y.lim, type="l", xlab="rep-sig", ylab=ylab.txt, lwd=5, cex=5, cex.axis=2, cex.lab=2)
-#   plot(ez.list[[1]]$v.t, y, ylim=y.lim, type="l", xlab="rep-sig", ylab=ylab.txt, lwd=5, cex=5, cex.axis=2, cex.lab=2)
-  
-
-  legend(0, y.lim[2], legend=legend.txt, col=col.txt[1:length(col.txt)], lty=1, lwd=5, cex=2)
-
-  if(!is.null(title))
-    title(title.txt)
-
-  if(!is.null(file.name)){
-    dev.off()
-  }
-  
-}
-
-
-plot.ez.group <- function(ez.list, plot.dir, file.name=NULL, legend.txt, y.lim=NULL, xlab.txt="num of significant peaks",  ylab.txt="IDR", col.txt=NULL, title.txt=NULL){
-
-  if(is.null(col.txt))
-    col.txt <- c("black", "red", "purple", "green", "blue", "cyan", "magenta", "orange", "grey")
-
-  n.entry <- length(ez.list)
-  x <- rep(NA, n.entry)
-  y.max <- rep(NA, n.entry)
-
-  for(i in 1:n.entry){
-    x[i] <- max(ez.list[[i]]$n)
-      
-    y.max[i] <- max(ez.list[[i]]$IDR)
-  
-  }
-
-  if(is.null(y.lim))
-    y.lim <- c(0, max(y.max))
-
-  if(!is.null(file.name)){
-    postscript(paste(plot.dir, "ez.", file.name, sep=""))
-    par(mfrow=c(1,1), mar=c(5,5,4,2))
-  }
-
-
-  
-  plot(c(0, max(x)), y.lim, ylim=y.lim, type="n", xlab=xlab.txt, ylab=ylab.txt, lwd=5, cex=5, cex.axis=2, cex.lab=2)
-
-  q <- seq(0.01, 0.99, by=0.01)
-  
-  for(i in 1:length(ez.list)){
-
-    n.plot <- round(quantile(ez.list[[i]]$n, prob=q))
-    IDR.plot <- ez.list[[i]]$IDR[n.plot]
-    lines(n.plot, IDR.plot, col=col.txt[i], cex=2, lwd=5)    
-  }
-
-
-  legend(0, y.lim[2], legend=legend.txt, col=col.txt[1:length(col.txt)], lty=1, lwd=5, cex=2)
-
-  if(!is.null(title))
-    title(title.txt)
-
-  if(!is.null(file.name)){
-    dev.off()
-  }
-  
-}
-
-
-
-#############################################################################
-#############################################################################
-# statistics about peaks selected on the individual replicates
-#
-# idr.level: the consistency cutoff, say 0.05
-# uri.output: a list of uri.output from consistency analysis generated by batch-consistency-analysis.r
-# ez.list : a list of IDRs computed from get.ez.tt using the same idr.level
-#
-##################
-
-
-# obsolete?
-# compute the error rate
-# u.t and v.t are the quantiles
-# 
-# map back to all peaks and report the number of peaks selected
-get.ez.tt.all.old  <- function(em.fit, all.data1, all.data2, idr.level){
-
-  u <- em.fit$data.pruned$sample1
-  v <- em.fit$data.pruned$sample2
-
-  tt <- seq(0.01, 0.99, by=0.01)
-#  if(reverse){ 
-    e.z <-  1-em.fit$em.fit$e.z # this is the error prob
-    uri.ez <- sapply(tt, comp.uri.ez, u=u, v=v, e.z=e.z)
-    ez.bound <- sapply(tt, comp.ez.cutoff, u=u, v=v, e.z=e.z, boundary.txt="max") 
-#  } else {
-#    e.z <-  em.fit$em.fit$e.z
-#    uri.ez <- sapply(tt, comp.uri.ez, u=u, v=v, e.z=e.z)
-#    ez.bound <- sapply(tt, comp.ez.cutoff, u=u, v=v, e.z=e.z, boundary.txt="min") 
-#  }
-
-  u.t <- quantile(u, prob=(1-tt))
-  v.t <- quantile(v, prob=(1-tt))  
-
-  # find the levels on the two replicates
-  sig.value1 <- c()
-  sig.value2 <- c()
-  error.prob.cutoff <- c()
-  n.selected.match <- c()
-  npeak.rep1 <- c()
-  npeak.rep2 <- c()
-
-  for(i in 1:length(idr.level)){
-
-    # find which uri.ez is closet to idr.level
-    index <- which.min(abs(uri.ez - as.numeric(idr.level[i])))
-
-    sig.value1[i] <- u.t[index]
-    sig.value2[i] <- v.t[index]
-    error.prob.cutoff[i] <- ez.bound[index]  
-    n.selected.match[i] <- sum(u>= u.t[index] & v>=v.t[index])
-
-    npeak.rep1[i] <- sum(all.data1["sig.value"] >= sig.value1[i])
-    npeak.rep2[i] <- sum(all.data2["sig.value"] >= sig.value2[i])    
-  }   
-
-
-  # output the cutoff of posterior probability, signal values on two replicates
-  map.uv <- cbind(error.prob.cutoff, sig.value1, sig.value2, n.selected.match, npeak.rep1, npeak.rep2)
-
-  return(list(n=tt*length(u), uri.ez=uri.ez, u.t=u.t, v.t=v.t, tt=tt, idr.level=idr.level,  map.uv=map.uv, e.z=e.z, error.prob.cutoff=error.prob.cutoff))
-}
-
-
-get.ez.tt.all <- function(em.fit, all.data1, all.data2, idr.level=c(0.01, 0.05, 0.1)){
-
-  u <- em.fit$data.pruned$sample1$sig.value
-  v <- em.fit$data.pruned$sample2$sig.value
-#  u <- em.fit$data.pruned$sample1
-#  v <- em.fit$data.pruned$sample2
-  
-  e.z <-  1-em.fit$em.fit$e.z # this is the error prob
-  
-  o <- order(e.z)
-  e.z.ordered <- e.z[o]
-  n.select <- c(1:length(e.z))
-  IDR <- cumsum(e.z.ordered)/n.select
-
-  u.o <- u[o]
-  v.o <- v[o]
-
-  n.level <- length(idr.level)
-#  sig.value1 <- rep(NA, n.level)
-#  sig.value2 <- rep(NA, n.level)
-  ez.cutoff <- rep(NA, n.level)
-  n.selected <- rep(NA, n.level)
-  npeak.rep1 <- rep(NA, n.level)
-  npeak.rep2 <- rep(NA, n.level)
-  
-  for(i in 1:length(idr.level)){
-
-    # find which uri.ez is closet to fdr.level
-    index <- which.min(abs(IDR - idr.level[i]))
-#    sig.value1[i] <- min(u.o[1:index])
-#    sig.value2[i] <- min(v.o[1:index])
-    ez.cutoff[i] <- e.z.ordered[index]      # fixed on 02/20/10
-    n.selected[i] <- sum(e.z<=ez.cutoff[i])
-#    npeak.rep1[i] <- sum(all.data1["sig.value"] >= sig.value1[i])
-#    npeak.rep2[i] <- sum(all.data2["sig.value"] >= sig.value2[i])     
-  }   
-
-  # output the cutoff of posterior probability, number of selected overlapped peaks 
-  map.uv <- cbind(ez.cutoff, n.selected)
-
-  return(list(n=n.select, IDR=IDR, idr.level=idr.level, map.uv=map.uv))
-}   
-  
-#  return(list(n=tt*length(u), uri.ez=uri.ez,  fdr.level=fdr.level,  map.uv=map.uv, e.z=e.z, error.prob.cutoff=error.prob.cutoff))  
-  
-
-
-
-
-
-####### the following is for determining thresholds for merged dataset
-
-############# select peaks above a given threshold
-#
-# pass.threshold: a simple method, passing the threshold on the threshold on the individual replicate to the pooled sample 
-#                 
-# sig.map.list: a list of matrix to include all the cutoff values, each row corresponds to a cutoff. The first column is idr.level
-#          the 2nd column is the cutoff of ez, the rest of columns are consistency analysis for other replicates 
-# sig.value.name: the name of the sig.value column
-# combined: combined dataset
-# nrep: number of pairs of comparisons
-#
-# Procedure:
-# 1. Find the significant threshold corresponding to the idr cutoff on the matched peaks. 
-# 2. Each time we will get two or more (if >2 replicates) cutoffs and will report the most stringent and the least stringent
-#    cutoff and the number of peaks selected at those two cutoffs
-#############
-
-pass.threshold <- function(sig.map.list, sig.value.name, combined, idr.level, nrep, chr.size){
-
-  sig.map <- c()
-
-  # choose idr.level
-  idr.index <- which(rbind(sig.map.list[[1]])[,1] == idr.level)
-  if(length(i) ==0){
-    print("no level matches specified idr.level")
-    return(-1)
-  }
-
-  for(i in 1:length(sig.map.list))
-    sig.map <- c(sig.map, rbind(sig.map.list[[i]])[idr.index, c("sig.value1", "sig.value2")])
-  
-  
-  npeak.tight <- c()  
-  npeak.loose <- c()
-
-
-  max.sig <- max(sig.map)
-  min.sig <- min(sig.map)
-  selected.sig.tight <- combined[combined[,sig.value.name]>=max.sig, ]
-  selected.sig.loose <- combined[combined[,sig.value.name]>=min.sig, ]
-
-  selected.sig.tight <- deconcatenate.chr(selected.sig.tight, chr.size)[,c("chr", "start", "stop", "signal.value", "p.value", "q.value")]
-  selected.sig.loose <- deconcatenate.chr(selected.sig.loose, chr.size)[,c("chr", "start", "stop", "signal.value", "p.value", "q.value")]
-  
-  npeak.tight <- nrow(selected.sig.tight)
-  npeak.loose <- nrow(selected.sig.loose)
-  
-  
-  npeak.stat <- list(idr.level=idr.level, max.sig=max.sig, min.sig=min.sig, npeak.tight=npeak.tight, npeak.loose=npeak.loose)
-
-  invisible(list(npeak.stat=npeak.stat, combined.selected.tight=selected.sig.tight, combined.selected.loose=selected.sig.loose))  
-}
-
-#################
-# pass the regions selected from consistency analysis to combined data
-# Threshold is determined on the replicates, the regions above the threshold are selected
-# then peaks on the combined data are selected from the selected regions
-#
-# To avoid being too stringent, regions satisfying the following conditions are selected
-# 1. regions above the significant threshold determined by consistency analysis on either replicate
-# 2. regions that have consistent low peaks, i.e. posterior prob > threshold but not passing the significant threshold
-#
-# This method doesn't make a difference when using different thresholds
-#################
-
-pass.region <- function(sig.map.list, uri.output, ez.list, em.output, combined, idr.level, sig.value.impute=0, chr.size){
- 
-  combined <- combined[, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-  npair <- length(uri.output) # number of pairs of consistency analysis
-  combined.region <- c()
-
-  # choose idr.level
-  idr.index <- which(rbind(sig.map.list[[1]])[,1] == idr.level)
-  if(length(idr.index) ==0){
-    print("no level matches specified idr.level")
-    return(-1)
-  }
-
-    for(j in 1:npair){
-      # select peaks from individual replicates using individual cutoff
-      above.1 <- uri.output[[j]]$data12.enrich$merge1["sig.value"] >= ez.list[[j]]$map.uv[idr.index,"sig.value1"]
-      above.2 <- uri.output[[j]]$data12.enrich$merge1["sig.value"] >= ez.list[[j]]$map.uv[idr.index,"sig.value2"]
-      selected.sig.rep1 <- uri.output[[j]]$data12.enrich$merge1[above.1, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-      selected.sig.rep2 <- uri.output[[j]]$data12.enrich$merge2[above.2, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")] 
-      
-      # find the peaks that are overlapped with reliable peaks in the individual replicates
-      overlap.1 <- pair.peaks(selected.sig.rep1, combined)$merge2
-      overlap.2 <- pair.peaks(selected.sig.rep2, combined)$merge2
-
-      # choose the ones with significant value > 0, which are the overlapped ones
-
-      combined.in1 <- overlap.1[overlap.1$sig.value > sig.value.impute, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-      combined.in2 <- overlap.2[overlap.2$sig.value > sig.value.impute, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-
-      ## consistent low significant ones
-      ## first find consistenct ones, ie. high posterior prob
-      # is.consistent <- ez.list[[j]]$e.z < ez.list[[j]]$ez.cutoff 
-
-      # data.matched <- keep.match(uri.output[[j]]$data12.enrich$merge1[!above.1, ], uri.output[[j]]$data12.enrich$merge2[!above.2, ], sig.value.impute=0)
-      # data.matched$sample1 <- data.matched$sample1[, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-      # data.matched$sample2 <- data.matched$sample2[, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-
-      # consistent.in1 <- data.matched$sample1[is.consistent, ]
-      # consistent.in2 <- data.matched$sample2[is.consistent, ]
-
-      # overlap.consistent.1 <- pair.peaks(consistent.in1, combined)$merge2
-      # overlap.consistent.2 <- pair.peaks(consistent.in2, combined)$merge2
-
-      ## choose the ones with significant value > 0, which are the overlapped ones
-
-      # combined.consistent.in1 <- overlap.consistent.1[overlap.consistent.1$sig.value > sig.value.impute, ]
-      # combined.consistent.in2 <- overlap.consistent.2[overlap.consistent.2$sig.value > sig.value.impute, ]
-
-      # combined.region <- rbind(combined.region, combined.in1, combined.in2, combined.consistent.in1, combined.consistent.in2)
-
-       combined.region <- rbind(combined.region, combined.in1, combined.in2)
-
-      is.repeated <- duplicated(combined.region$start)
-      combined.region <- combined.region[!is.repeated, c("start", "stop", "sig.value", "signal.value", "p.value", "q.value")]
-      
-    }
-    npeak <- nrow(combined.region)
-  
-   sig.combined <- c(min(combined.region[,"sig.value"], na.rm=T), max(combined.region[,"sig.value"], na.rm=T))
-
-  # idr.combined <- c(min(combined.region[,"q.value"], na.rm=T), max(combined.region[,"q.value"], na.rm=T))
-
-   npeak.stat <- list(idr.level=idr.level, npeak=npeak)
-
-   combined.region <- deconcatenate.chr(combined.region, chr.size)[,c("chr", "start", "stop", "signal.value", "p.value", "q.value")]
-
-  invisible(list(npeak.stat=npeak.stat, combined.selected=combined.region, sig.combined=sig.combined))
-}
-
-################
-# pass structure: this method does another round of inference on the combined data
-#
-# To make the mixture structure comparable on the replicates and the combined data, the 2nd inference is done on the peaks
-# at the reliable regions on the combined data, using rank transformed significant values. The mixture structure is estimated using my consistency analysis, which 
-# estimates marginal distributions of ranks using nonparametric ways. Then the significant values are found out.
-# There are several advantages to do it this way:  
-# 1. The premise of passing structure is that the means and variance (i.e. distribution) of two replicates should be the same
-#    The significant values on the two replicates clearly have different distributions. The structure estimated from consistency
-#    analysis will generate similar rank distribution on two replicates by its setup (i.e. same number of peaks are paired up).  
-# 2. Because pooled sample is a black box, the structure is more likely to be followed in the matched regions than other locations,
-#    after all, we don't know what other things are. If even the structure doesn't hold on the matched regions, 
-#    which is possible, let alone the other regions. Focusing on the reliable regions helps to get rid of those unknown noises.
-#  
-# 
-# modified on 2-20-10: reverse rank.combined, make big sig.value with small
-# ranks, to be consistent with f1 and f2
-################ 
-
-pass.structure <- function(uri.output, em.output, combined, idr.level, sig.value.impute, chr.size, overlap.ratio=0){
-
-  columns.keep <- c("sig.value", "start", "stop", "signal.value", "p.value", "q.value", "chr", "start.ori", "stop.ori")
-  combined <- combined[, columns.keep]
-  combined.selected.all <- c()
-
-  for(j in 1:npair){
-
-    sample1 <- uri.output[[j]]$data12.enrich$merge1[, columns.keep]
-    sample2 <- uri.output[[j]]$data12.enrich$merge2[, columns.keep]
-        
-    # find peaks on the matched region on the combined one
-    data.matched <- keep.match(sample1, sample2, sig.value.impute=sig.value.impute)
-
-    data.matched$sample1 <- data.matched$sample1[, columns.keep]
-    data.matched$sample2 <- data.matched$sample2[, columns.keep]
-
-    overlap.1 <- pair.peaks.filter(data.matched$sample1, combined, p.value.impute=sig.value.impute, overlap.ratio)$merge2
-    overlap.2 <- pair.peaks.filter(data.matched$sample2, combined, p.value.impute=sig.value.impute, overlap.ratio)$merge2
-
-    # choose the ones with significant value > sig.value.impute, which are the overlapped ones
-
-    combined.in1 <- overlap.1[overlap.1$sig.value > sig.value.impute, ]
-    combined.in2 <- overlap.2[overlap.2$sig.value > sig.value.impute, ]
-
-    combined.region <- rbind(combined.in1, combined.in2)
-  
-    is.repeated <- duplicated(combined.region$start)
-    combined.region <- combined.region[!is.repeated,]
-
-    # now rank the peaks in matched region
-    rank.combined <- rank(-combined.region$sig.value)
-    
-    # now transform the parameters estimated into the new scale
-    npeaks.overlap <- nrow(combined.region)
-    npeaks.consistent <- nrow(cbind(em.output[[j]]$data.pruned$sample1))
-
-    
-    # the breaks are the same for x and y
-    f1 <- list(breaks=em.output[[j]]$em.fit$x.mar$f1$breaks*npeaks.overlap/npeaks.consistent, density=(em.output[[j]]$em.fit$x.mar$f1$density+em.output[[j]]$em.fit$y.mar$f1$density)/2)
-    # the first break boundary goes up when changing scale, need set it back to be a bit smaller than 1
-    f1$breaks[1] <- min(f1$breaks[1], 0.95)
-    
-    f2 <- list(breaks=em.output[[j]]$em.fit$x.mar$f2$breaks*npeaks.overlap/npeaks.consistent, density=(em.output[[j]]$em.fit$x.mar$f2$density+em.output[[j]]$em.fit$y.mar$f2$density)/2)
-    # the first break boundary goes up when changing scale, need set it back to be a bit smaller than 1
-    f2$breaks[1] <- min(f2$breaks[1], 0.95)
-    
-    p <- em.output[[j]]$em.fit$para$p
- 
-    # find the posterior probability
-    errorprob.combined <- get.comp2.prob(rank.combined, p, f1, f2)
-
-    # compute the FDR and find cutoff of posterior prob and the sig value
-    o <- order(errorprob.combined)
-    idr <- cumsum(errorprob.combined[o])/c(1:length(o))
-    idr.index <- which(idr > idr.level)[1]
-    errorprob.cutoff <- errorprob.combined[o][idr.index]
-
-    # find the minimum significant measure among selected peaks
-    sig.value <- min(combined.region$sig.value[o][1:idr.index])
-  #  sig.value <- quantile(combined.region$sig.value[o][1:idr.index], prob=0.05)
-#sig.value <- quantile(combined.region$sig.value[errorprob.combined<=errorprob.cutoff], prob=0.05)
-    
-    # apply the significant value on the whole pooled list
-    combined.selected <- combined[combined$sig.value >= sig.value,]
-
-    combined.selected.all <- rbind(combined.selected.all, combined.selected)
-  }
-
-  is.repeated <- duplicated(combined.selected.all$start)
-  combined.selected.all <- combined.selected.all[!is.repeated,]
-
-  npeak <- nrow(combined.selected.all)  
-  
-  npeak.stat <- list(idr.level=idr.level, npeak=npeak)
-
-  sig.combined <- c(min(combined.selected.all[,"sig.value"], na.rm=T), max(combined.selected.all[,"sig.value"], na.rm=T))
-
- #  idr.combined <- c(min(combined.selected.all[,"q.value"], na.rm=T), max(combined.selected.all[,"q.value"], na.rm=T))
- # combined.selected.all <- deconcatenate.chr(combined.selected.all, chr.size)[,c("chr", "start", "stop", "signal.value", "p.value", "q.value")]
-
-  combined.selected.all <- combined.selected.all[,  c("chr", "start.ori", "stop.ori", "signal.value", "p.value", "q.value")]
-  colnames(combined.selected.all) <- c("chr", "start", "stop", "signal.value", "p.value", "q.value")
-  
-  invisible(list(npeak.stat=npeak.stat, combined.selected=combined.selected.all, sig.combined=sig.combined))
-}
-
-
-
-# get the posterior probability of the 2nd component
-get.comp2.prob <- function(x, p, f1, f2){
-
-  # get pdf and cdf of each component from functions in the corresponding component 
-  px.1 <- sapply(x, get.pdf, df=f1)
-  px.2 <- sapply(x, get.pdf, df=f2)
-
-  comp2prob <- 1 - p*px.1/(p*px.1+(1-p)*px.2)
-  
-  return(comp2prob)
-}
-
-keep.match <- function(sample1, sample2, sig.value.impute=0){
-
-  sample1.prune <- sample1[sample1$sig.value > sig.value.impute & sample2$sig.value > sig.value.impute,]
-  sample2.prune <- sample2[sample1$sig.value > sig.value.impute & sample2$sig.value > sig.value.impute,]
- 
-  invisible(list(sample1=sample1.prune, sample2=sample2.prune))
-}
-
-
-##############################################
-#
-# The following is for simulation
-#
-##############################################
-
-
-# simulate gaussian copula
-# u is the uniform random variable and rho is correlation coefficient 
-simu.gaussian.copula <- function(u, rho){
-
-  n <- length(u)
-
-  # simulate y given x=qnorm(u)
-  y <- qnorm(u)*rho + rnorm(n)*sqrt(1-rho^2)
-
-  v <- pnorm(y)
-
-  invisible(v)
-}
-
-## simulate Clayton copula from its generating function
-## Genest and MacKay (1986)
-
-phi.ori <- function(t, s){
-
- (t^(-s) -1)/s
-}
-
-
-phi.inv <- function(y, s){
-
-  exp(-log(s*y+1)/s)
-}
-
-phi.der <- function(t, s){
-
-  -t^(-s-1)
-}
-
-phi.der.inv <- function(y, s){
-
-  exp(log(-y)/(-s-1))
-}
-
-get.w <- function(u, t, s){
-
-  phi.der.inv(phi.der(u, s)/t, s)
-} 
-
-get.v <- function(w, u, s){
-
-  phi.inv(phi.ori(w, s) - phi.ori(u, s), s) 
-}
-
-# u is a uniform random variable, s is the association parameter
-simu.clayton.copula <- function(u, s){
-
-  t <- runif(length(u))
-
-  if(s>0){
-    w <- get.w(u, t, s)
-    v <- get.v(w, u, s)
-    return(v)    
-  }
-
-  if(s==0){
-    return(t)
-  }
-
-  if(s <0){
-    print("Invalid association parameters for clayton copula")
-  }
-  
-}
-
-
-
-###### 09-09-09
-
-# simulate a two-component copula mixture:
-# - marginal distributions for the two variables in each component are both 
-#   normal and with the same parameters 
-# p is the mixing proportion of component 1
-# n is the total sample size
-simu.copula.2mix <- function(s1, s2, p, n, mu1, mu2, sd1, sd2, copula.txt){
-
-  n1 <- round(n*p)
-  n2 <- n-n1
-
-  u1 <- runif(n1)
-  
-  if(copula.txt =="clayton")
-    v1 <- simu.clayton.copula(u1, s1)
-  else{
-    if(copula.txt =="gaussian")
-      v1 <- simu.gaussian.copula(u1, s1)
-  }
-
-  u2 <- runif(n2)
-
-  if(copula.txt =="clayton")
-    v2 <- simu.clayton.copula(u2, s2)
-  else{
-    if(copula.txt =="gaussian")
-      v2 <- simu.gaussian.copula(u2, s2)
-  }
-
-  # generate test statistics
-  sample1.1 <- qnorm(u1, mu1, sd1)
-  sample1.2 <- qnorm(v1, mu1, sd1)
-
-  sample2.1 <- qnorm(u2, mu2, sd2)
-  sample2.2 <- qnorm(v2, mu2, sd2)
-
-  return(list(u=c(u1, u2), v=c(v1, v2), 
-              u.inv=c(sample1.1, sample2.1), v.inv=c(sample1.2, sample2.2),
-              label=c(rep(1, n1), rep(2, n2))))
-}
-
-# using inverse of the cdf to generate original observations 
-
-simu.copula.2mix.inv <- function(s1, s2, p, n, cdf1.x, cdf1.y, cdf2.x, cdf2.y, copula.txt){
-
-  n1 <- round(n*p)
-  n2 <- n-n1
-
-  u1 <- runif(n1)
-  
-  if(copula.txt =="clayton")
-    v1 <- simu.clayton.copula(u1, s1)
-  else{
-    if(copula.txt =="gaussian")
-      v1 <- simu.gaussian.copula(u1, s1)
-  }
-
-  u2 <- runif(n2)
-
-  if(copula.txt =="clayton")
-    v2 <- simu.clayton.copula(u2, s2)
-  else{
-    if(copula.txt =="gaussian")
-      v2 <- simu.gaussian.copula(u2, s2)
-  }
-
-  # generate test statistics
-#  sample1.1 <- qnorm(u1, mu1, sd1)
-#  sample1.2 <- qnorm(v1, mu1, sd1)
-
-#  sample2.1 <- qnorm(u2, mu2, sd2)
-#  sample2.2 <- qnorm(v2, mu2, sd2)
-  
-  sample1.x <- inv.cdf.vec(u1, cdf1.x)
-  sample1.y <- inv.cdf.vec(v1, cdf1.y)
-
-  sample2.x <- inv.cdf.vec(u2, cdf2.x)
-  sample2.y <- inv.cdf.vec(v2, cdf2.y)
-  
-  
-  return(list(u=c(u1, u2), v=c(v1, v2), 
-              u.inv=c(sample1.x, sample2.x), v.inv=c(sample1.y, sample2.y),
-              label=c(rep(1, n1), rep(2, n2))))
-}
-
-# obtain original observation by converting cdf into quantiles
-# u is one cdf
-# u.cdf is a cdf (assuming it is a histogram) and has the break points (cdf$cdf and cdf$breaks)
-# the smallest value of cdf=0 and the largest =1 
-inv.cdf <- function(u, u.cdf){
-
-  # which bin it falls into
-  i <- which(u.cdf$cdf> u)[1]
-  q.u  <- (u - u.cdf$cdf[i-1])/(u.cdf$cdf[i] - u.cdf$cdf[i-1])* (u.cdf$breaks[i]-u.cdf$breaks[i-1]) + u.cdf$breaks[i-1]
-
-  return(q.u)
-}
-
-inv.cdf.vec <- function(u, u.cdf){
-
-  # check if cdf has the right range (0, 1)  
-  ncdf <- length(u.cdf$cdf)
-  nbreaks <- length(u.cdf$breaks)
-  
-  if(ncdf == nbreaks-1 & u.cdf$cdf[ncdf]< 1)
-    u.cdf[ncdf] <- 1
-    
-  q.u <- sapply(u, inv.cdf, u.cdf)
-
-  return(q.u) 
-}
-
-# here we simulate a likely real situation
-# the test statistics from two normal distributions
-# according to their labels, then convert them into p-values w.r.t H0 using
-# one-sided test.
-# The test statistics are correlated for the signal component and independent
-# for the noise component
-# For the signal component, Y = X + eps, where eps ~ N(0, sigma^2)
-simu.test.stat <- function(p, n, mu1, sd1, mu0, sd0, sd.e){
-
-  # first component - signal
-  n.signal <- round(n*p)
-  n.noise <- n - n.signal
-
-  # labels
-  labels <- c(rep(1, n.signal), rep(0, n.noise))
-  
-  # test statistics for signal and noise
-  mu.signal <- rnorm(n.signal, mu1, sd1)
-  x.signal <- mu.signal + rnorm(n.signal, 0, sd.e)
-  x.noise <- rnorm(n.noise, mu0, sd0) + rnorm(n.noise, 0, sd.e)
-
-  y.signal <- mu.signal + rnorm(n.signal, 0, sd.e)
-                                        # sd.e can be dependent on signal
-  y.noise <- rnorm(n.noise, mu0, sd0) + rnorm(n.noise, 0, sd.e)
-
-  # concatenate
-  x <- c(x.signal, x.noise)
-  y <- c(y.signal, y.noise)
-  
-  # convert to p-values based on H0
-  p.x <- 1-pnorm(x, mu0, sqrt(sd0^2+sd.e^2))
-  p.y <- 1-pnorm(y, mu0, sqrt(sd0^2+sd.e^2))
-
-  return(list(p.x=p.x, p.y=p.y, x=x, y=y, labels=labels))
-  
-}
-
-# compute the tradeoff and calibration
-forward.decoy.tradeoff.ndecoy <- function(xx, labels, ndecoy){
-
-  xx <- round(xx, 5) 
-  o <- order(xx, decreasing=T)
-
-  rand <- 1-labels # if rand==0, consistent
-  # order the random indicator in the same order
-  rand.o <- rand[o]
-
-  if(sum(rand.o) > ndecoy){
-    index.decoy <- which(cumsum(rand.o)==ndecoy)
-  } else {
-    index.decoy <- which(cumsum(rand.o)==sum(rand.o))
-  }
-  
-  cutoff.decoy <- xx[o][index.decoy]
-    
-  # only consider the unique ones
-  cutoff.unique <- unique(xx[o])
-
-  cutoff <- cutoff.unique[cutoff.unique >= cutoff.decoy[length(cutoff.decoy)]]
-
-   get.decoy.count <- function(cut.off){
-     above <- rep(0, length(xx))
-     above[xx >= cut.off] <- 1
-     decoy.count <- sum(above==1 & rand==1)     
-     return(decoy.count)
-   }
-
-   get.forward.count <- function(cut.off){
-     above <- rep(0, length(xx))
-     above[xx >= cut.off] <- 1
-     forward.count <- sum(above==1 & rand==0)
-     return(forward.count)
-   }
-
-   get.est.fdr <- function(cut.off){
-     above <- rep(0, length(xx))
-     above[xx >= cut.off] <- 1
-     est.fdr <- 1-mean(xx[above==1])
-     return(est.fdr)
-   }
-
-  # assuming rand=0 is right
-   get.false.neg.count <- function(cut.off){
-     below <- rep(0, length(xx))
-     below[xx < cut.off] <- 1
-     false.neg.count <- sum(below==1 & rand==0)
-     return(false.neg.count)
-   }
-
-  get.false.pos.count <- function(cut.off){
-     above <- rep(0, length(xx))
-     above[xx >= cut.off] <- 1
-     false.pos.count <- sum(above==1 & rand==1)
-     return(false.pos.count)
-   } 
-
-   decoy <- sapply(cutoff, get.decoy.count)
-   forward <- sapply(cutoff, get.forward.count)
-
-   est.fdr <- sapply(cutoff, get.est.fdr)
-   emp.fdr <- decoy/(decoy+forward)
-  
-   # compute specificity and sensitivity
-   # assuming rand=1 is wrong and rand=0 is right
-   false.neg <- sapply(cutoff, get.false.neg.count)
-   false.pos <- sapply(cutoff, get.false.pos.count)
-  
-   true.pos <- sum(rand==0)-false.neg
-   true.neg <- sum(rand==1)-false.pos
-  
-   sensitivity <- true.pos/(true.pos+false.neg)
-   specificity <- true.neg/(true.neg+false.pos)
-  
-   return(list(decoy=decoy, forward=forward, cutoff=cutoff, est.fdr=est.fdr, emp.fdr=emp.fdr, sensitivity=sensitivity, specificity=specificity))  
-}
-
-
-# compute the em for jackknife and all data, and find FDR
-get.emp.jack <- function(a, p0){
-
-  nobs <- length(a$labels)
-  est <- list()
-  est.all <- list()
-
-  temp.all <- em.transform(-a$p.x, -a$p.y, mu=1.5, sigma=1.4, rho=0.4, p=0.7, eps=0.01)
-#  temp.all <- em.2copula.quick(a$p.x, a$p.y, p0=p0, rho1.0=0.7,
-#      rho2.0=0, eps=0.01, fix.p=T, stoc=F, fix.rho2=T, "gaussian")
-
-  est.all$p <- temp.all$para$p
-  est.all$rho1 <- temp.all$para$rho1
-  est.all$FDR <- get.FDR(temp.all$e.z)
-
-  FDR <- list()
-  p <- c()
-  rho1 <- c()
-
-
-  for(i in 1:nobs){
-
-    temp <- em.transform(-a$p.x[-i], -a$p.y[-i], mu=1.5, sigma=1.4, rho=0.4, p=0.7, eps=0.01)    
-#    temp <- em.2copula.quick(a$p.x[-i], a$p.y[-i], p0=p0, rho1.0=0.7,
-#      rho2.0=0, eps=0.01, fix.p=T, stoc=F, fix.rho2=T, "gaussian")
-
-    est[[i]] <- list(p=temp$para$p, rho1=temp$para$rho1, FDR=get.FDR(temp$e.z))
-
-    FDR[[i]] <- est[[i]]$FDR # this is the FDR for top n peaks
-    p[i] <- est[[i]]$p
-    rho1[i] <- est[[i]]$rho1 
-  }
-
-  est.jack <- list(FDR=FDR, p=p, rho1=rho1) 
-  return(list(est.jack=est.jack, est.all=est.all))
-}
-
-
-# get the npeaks corresponding to the nominal FDR estimated from the sample
-# and find the corresponding FDR from the entire data
-get.FDR.jack <- function(est, FDR.nominal){
-  
-  nobs <- length(est$est.jack$FDR)
-  FDR.all <- c()  
-  top.n <- c()
-  
-  for(i in 1:nobs){
-    top.n[i] <- max(which(est$est.jack$FDR[[i]] <= FDR.nominal))
-    FDR.all[i] <- est$est.all$FDR[top.n[i]]
-  }
-  
-  invisible(list(FDR.all=FDR.all, top.n=top.n))
-}
-
-# compute Jackknife peudonumber
-# a is the dataset
-get.emp.IF <- function(a, p0){
-
-  nobs <- length(a$labels)
-  est <- list()
-  est.all <- list()
-
-  temp.all <- em.2copula.quick(a$p.x, a$p.y, p0=p0, rho1.0=0.7,
-      rho2.0=0, eps=0.01, fix.p=T, stoc=F, fix.rho2=T, "gaussian")
-
-  est.all$p <- temp.all$para$p
-  est.all$rho1 <- temp.all$para$rho1
-  est.all$FDR <- get.FDR(temp.all$e.z)
-
-  IF.FDR <- list()
-  IF.p <- c()
-  IF.rho1 <- c()
-  
-  for(i in 1:nobs){
-    
-    temp <- em.2copula.quick(a$p.x[-i], a$p.y[-i], p0=p0, rho1.0=0.7,
-      rho2.0=0, eps=0.01, fix.p=T, stoc=F, fix.rho2=T, "gaussian")
-
-    est[[i]] <- list(p=temp$para$p, rho1=temp$para$rho1, FDR=get.FDR(temp$e.z))
-
-    IF.FDR[[i]] <- (nobs-1)*(est.all$FDR[-nobs] - est[[i]]$FDR) # this is the FDR for top n peaks
-    IF.p[i] <- (nobs-1)*(est.all$p - est[[i]]$p)
-    IF.rho1[i] <- (nobs-1)*(est.all$rho1 - est[[i]]$rho1) 
-  }
-
-  emp.IF <- list(FDR=IF.FDR, p=IF.p, rho1=IF.rho1) 
-
-  invisible(list(emp.IF=emp.IF, est.all=est.all, est=est))
-}
-
-# e.z is the posterior probability of being in signal component
-get.FDR <- function(e.z){
-
-  e.z.o <- order(1-e.z)
-  FDR <- cumsum(1-e.z[e.z.o])/c(1:length(e.z.o))
-
-  invisible(FDR)
-}
-
-# get the FDR of selecting the top n peaks
-# IF.est is the sample influence function
-# top.n
-get.IF.FDR <- function(IF.est, top.n){
-
-  nobs <- length(IF.est$emp.IF$FDR)
-  FDR <- c()  
-  
-  # influence function of p
-  for(i in 1:nobs)
-    FDR[i] <- IF.est$emp.IF$FDR[[i]][top.n]
-  
-  invisible(FDR)
-}
-
-# get the sample influence function for FDR at a given FDR size
-# 1. find the number of peaks selected at a given FDR computed from all obs
-# 2. use the number to find the sample influence function for FDR
-# IF.est$est.all is the FDR with all peaks
-get.IF.FDR.all <- function(IF.est, FDR.size){
-
-  top.n <- which.min(abs(IF.est$est.all$FDR -FDR.size))
-  nobs <- length(IF.est$est.all$FDR)
-  FDR <- c()  
-  
-  # influence function of p
-  for(i in 1:nobs)
-    FDR[i] <- IF.est$emp.IF$FDR[[i]][top.n]
-  
-  invisible(list(FDR=FDR, top.n=top.n))
-}
-
-plot.simu.uri <- function(x, y){
-
-  tt <- seq(0.01, 0.99, by=0.01)
-  uri <- sapply(tt, comp.uri.prob, u=x, v=y)
-  uri.thin <- uri[seq(1, length(tt), by=3)]
-  tt.thin <- tt[seq(1, length(tt), by=3)]
-  duri <- (uri.thin[-1]-uri.thin[-length(uri.thin)])/(tt.thin[-1]-tt.thin[-length(tt.thin)])
-  uri.spl <- smooth.spline(tt, uri, df=6.4)
-  uri.der <- predict(uri.spl, tt, deriv=1)
-
-  par(mfrow=c(2,2))
-  plot(x[1:n0], y[1:n0])
-  points(x[(n0+1):n], y[(n0+1):n], col=2)
-  plot(rank(-x)[1:n0], rank(-y)[1:n0])
-  points(rank(-x)[(1+n0):n], rank(-y)[(1+n0):n])
-  plot(tt, uri)
-  lines(c(0,1), c(0,1), lty=2)
-  title(paste("rho1=", rho1, " rho2=", rho2, "p=", p, sep=""))
-  plot(tt.thin[-1], duri)
-  lines(uri.der)
-  abline(h=1)
-  invisible(list(x=x, y=y, uri=uri, tt=tt, duri=duri, tt.thin=tt.thin, uri.der=uri.der))
-
-}
-
-
-###### new fitting procedure
-
-
-
-
-# 1. rank pairs
-
-# 2. initialization
-# 3. convert to pseudo-number
-
-# 4. EM
-
-# need plugin and test
-# find the middle point between the bins
-get.pseudo.mix <- function(x, mu, sigma, rho, p){
-
-  
-  # first compute cdf for points on the grid
-  # generate 200 points between [-3, mu+3*sigma]
-  nw <- 1000
-  w <- seq(min(-3, mu-3*sigma), max(mu+3*sigma, 3), length=nw) 
-  w.cdf <- p*pnorm(w, mean=mu, sd=sigma) + (1-p)*pnorm(w, mean=0, sd=1)
-
-  i <- 1
-
-  quan.x <- rep(NA, length(x))
-
-  for(i in c(1:nw)){
-    index <- which(x >= w.cdf[i] & x < w.cdf[i+1])
-    quan.x[index] <- (x[index]-w.cdf[i])*(w[i+1]-w[i])/(w.cdf[i+1]-w.cdf[i]) +w[i]
-  }
-
-  index <- which(x < w.cdf[1])
-  if(length(index)>0)
-    quan.x[index] <- w[1]
-
-  index <- which(x > w.cdf[nw])
-  if(length(index)>0)
-    quan.x[index] <- w[nw]  
-  
-#  linear.ext <- function(x, w, w.cdf){
-  # linear interpolation
-#    index.up <- which(w.cdf>= x)[1]
-#    left.index <- which(w.cdf <=x)
-#    index.down <- left.index[length(left.index)]
-#    quan.x <- (w[index.up] + w[index.down])/2  
-#  }
-  
-#  x.pseudo <- sapply(x, linear.ext, w=w, w.cdf=w.cdf)
-
-#  invisible(x.pseudo)
-  invisible(quan.x)
-}
-
-
-# EM to compute the latent structure
-# steps:
-# 1. raw values are first transformed into pseudovalues
-# 2. EM is used to compute the underlining structure, which is a mixture
-#    of two normals
-em.transform <- function(x, y, mu, sigma, rho, p, eps){
-  
-  x.cdf.func <- ecdf(x)
-  y.cdf.func <- ecdf(y)
-  afactor <- length(x)/(length(x)+1)
-  x.cdf <- x.cdf.func(x)*afactor
-  y.cdf <- y.cdf.func(y)*afactor
-  
-  # initialization
-  para <- list()
-  para$mu <- mu
-  para$sigma <- sigma
-  para$rho <- rho
-  para$p <- p  
-
-  j <- 1
-  to.run <- T
-  loglik.trace <- c()
-  loglik.inner.trace <- c()
-  
-  #to.run.inner <- T
-  z.1 <- get.pseudo.mix(x.cdf, para$mu, para$sigma, para$rho, para$p)
-  z.2 <- get.pseudo.mix(y.cdf, para$mu, para$sigma, para$rho, para$p)
-
-#  cat("length(z1)", length(z.1), "\n")
-  while(to.run){
-    
-    # get pseudo value in each cycle
-#    z.1 <- get.pseudo.mix(x.cdf, para$mu, para$sigma, para$rho, para$p)
-#    z.2 <- get.pseudo.mix(y.cdf, para$mu, para$sigma, para$rho, para$p)
-
-    i <- 1
-    while(to.run){
-      
-      # EM for latent structure
-      e.z <- e.step.2normal(z.1, z.2, para$mu, para$sigma, para$rho, para$p)
-      para <- m.step.2normal(z.1, z.2, e.z)
-#para$rho <- rho
-#para$p <- p    
-#para$mu <- mu
-#para$sigma <- sigma    
-      if(i > 1)
-        l.old <- l.new
-    
-      # this is just the mixture likelihood of two-component Gaussian
-      l.new <- loglik.2binormal(z.1, z.2, para$mu, para$sigma, para$rho, para$p)
-
-      loglik.inner.trace[i] <- l.new 
-
-      if(i > 1){
-        to.run <- loglik.inner.trace[i]-loglik.inner.trace[i-1]>eps         
-      }
-        
-    
-#      if(i > 2){
-#        l.inf <- loglik.inner.trace[i-2] + (loglik.inner.trace[i-1] - loglik.inner.trace[i-2])/(1-(loglik.inner.trace[i]-loglik.inner.trace[i-1])/(loglik.inner.trace[i-1]-loglik.inner.trace[i-2]))
-
-#        if(loglik.inner.trace[i-1]!=loglik.inner.trace[i-2])
-#          to.run <- abs(l.inf - loglik.inner.trace[i]) > eps
-#        else
-#          to.run <- F
-          
-#      }
-
-      cat("loglik.inner.trace[", i, "]=", loglik.inner.trace[i], "\n")
-    cat("mu=", para$mu, "sigma=", para$sigma, "p=", para$p, "rho=", para$rho, "\n\n")
-      
-      i <- i+1
-    }
-    
-
-    # get pseudo value in each cycle
-    z.1 <- get.pseudo.mix(x.cdf, para$mu, para$sigma, para$rho, para$p)
-    z.2 <- get.pseudo.mix(y.cdf, para$mu, para$sigma, para$rho, para$p)
-
-    if(j > 1)
-      l.old.outer <- l.new.outer
-
-    l.new.outer <- loglik.2binormal(z.1, z.2, para$mu, para$sigma, para$rho, para$p)
-
-    loglik.trace[j] <- l.new.outer
-    
-    if(j == 1)
-      to.run <- T
-    else{ # stop when iteration>100
-      if(j > 100)
-        to.run <- F
-      else
-        to.run <- l.new.outer - l.old.outer > eps
-    }
-
-#    if(j %% 10==0)
-      cat("loglik.trace[", j, "]=", loglik.trace[j], "\n")
-    cat("mu=", para$mu, "sigma=", para$sigma, "p=", para$p, "rho=", para$rho, "\n")
-    
-    j <- j+1
-  }
-
-  bic <- -2*l.new + 4*log(length(z.1))
-  
-  return(list(para=list(p=para$p, rho=para$rho, mu=para$mu, sigma=para$sigma),
-              loglik=l.new, bic=bic, e.z=e.z, loglik.trace=loglik.trace))
-}  
-
-
-
-
-# compute log-likelihood for mixture of two bivariate normals
-loglik.2binormal <- function(z.1, z.2, mu, sigma, rho, p){
-
-  l.m <- sum(d.binormal(z.1, z.2, 0, 1, 0)+log(p*exp(d.binormal(z.1, z.2, mu, sigma, rho)-d.binormal(z.1, z.2, 0, 1, 0))+(1-p)))
-  
-#  l.m <- sum((p*d.binormal(z.1, z.2, mu, sigma, rho) + (1-p)*d.binormal(z.1, z.2, 0, 1, 0)))
-  return(l.m) 
-}
-
-# check this when rho=1
-
-# density of binomial distribution with equal mean and sigma on both dimensions
-d.binormal <- function(z.1, z.2, mu, sigma, rho){
-
-  loglik <- (-log(2)-log(pi)-2*log(sigma) - log(1-rho^2)/2 - (0.5/(1-rho^2)/sigma^2)*((z.1-mu)^2 -2*rho*(z.1-mu)*(z.2-mu) + (z.2-mu)^2))
-
-  return(loglik)
-}
-
-# E-step for computing the latent strucutre
-# e.z is the prob to be in the consistent group
-# e.step for estimating posterior prob
-# z.1 and z.2 can be vectors or scalars
-e.step.2normal <- function(z.1, z.2, mu, sigma, rho, p){
-
-  e.z <- p/((1-p)*exp(d.binormal(z.1, z.2, 0, 1, 0)-d.binormal(z.1, z.2, mu, sigma, rho))+ p)
-  
-  invisible(e.z)
-}
-
-# M-step for computing the latent structure
-# m.step for estimating proportion, mean, sd and correlation coefficient
-m.step.2normal <- function(z.1, z.2, e.z){
-
-  p <- mean(e.z)
-  mu <- sum((z.1+z.2)*e.z)/2/sum(e.z) 
-  sigma <- sqrt(sum(e.z*((z.1-mu)^2+(z.2-mu)^2))/2/sum(e.z))
-  rho <- 2*sum(e.z*(z.1-mu)*(z.2-mu))/(sum(e.z*((z.1-mu)^2+(z.2-mu)^2)))
-
-  return(list(p=p, mu=mu, sigma=sigma, rho=rho))
-}
-
-
-# assume top p percent of observations are true
-# x and y are ranks, estimate
-init <- function(x, y, x.label){
-  
-  x.o <- order(x)
-
-  x.ordered <- x[x.o]
-  y.ordered <- y[x.o]
-  x.label.ordered <- x.label[x.o]
-  
-  n <- length(x)
-  p <- sum(x.label)/n
-  
-  rho <- cor(x.ordered[1:ceiling(p*n)], y.ordered[1:ceiling(p*n)])
-
-  temp <- find.mu.sigma(x.ordered, x.label.ordered)
-  mu <- temp$mu
-  sigma <- temp$sigma
-  
-  invisible(list(mu=mu, sigma=sigma, rho=rho, p=p))
-
-}
-
-# find mu and sigma if the distributions of marginal ranks are known
-# take the medians of the two dist and map back to the original
-init.dist <- function(f0, f1){
-
-  # take the median in f0
-  index.median.0 <- which(f0$cdf>0.5)[1]
-  q.0.small <- f0$cdf[index.median.0] # because f0 and f1 have the same bins
-  q.1.small <- f1$cdf[index.median.0]
-
-  # take the median in f1
-  index.median.1 <- which(f1$cdf>0.5)[1]
-  q.0.big <- f0$cdf[index.median.1] # because f0 and f1 have the same bins
-  q.1.big <- f1$cdf[index.median.1]
-
-  # find pseudo value for x.middle[1] on normal(0,1) 
-  pseudo.small.0 <- qnorm(q.0.small, mean=0, sd=1)
-  pseudo.small.1 <- qnorm(q.1.small, mean=0, sd=1)
-
-  # find pseudo value for x.middle[2] on normal(0,1) 
-  pseudo.big.0 <- qnorm(q.0.big, mean=0, sd=1)
-  pseudo.big.1 <- qnorm(q.1.big, mean=0, sd=1)
-
-  mu <- (pseudo.small.0*pseudo.big.1 - pseudo.small.1*pseudo.big.0)/(pseudo.big.1-pseudo.small.1) 
-
-  sigma <- (pseudo.small.0-mu)/pseudo.small.1
-
-  return(list(mu=mu, sigma=sigma))  
-}
-
-# generate labels
-
-# find the part of data with overlap
-
-# find the percentile on noise and signal
-
-# Suppose there are signal and noise components, with mean=0 and sd=1 for noise
-# x and x.label are the rank of the observations and their labels,
-# find the mean and sd of the other component
-# x.label takes values of 0 and 1
-find.mu.sigma <- function(x, x.label){
-
-  x.0 <- x[x.label==0]
-  x.1 <- x[x.label==1]
-
-  n.x0 <- length(x.0)
-  n.x1 <- length(x.1)
-
-  x.end <- c(min(x.0), min(x.1), max(x.0), max(x.1))
-  o <- order(x.end)
-  x.middle <- x.end[o][c(2,3)]
-
-  # the smaller end of the overlap
-  q.1.small <- mean(x.1 <= x.middle[1])*n.x1/(n.x1+1)
-  q.0.small <- mean(x.0 <= x.middle[1])*n.x0/(n.x0+1)
-
-  # the bigger end of the overlap
-  q.1.big <- mean(x.1 <= x.middle[2])*n.x1/(n.x1+1)
-  q.0.big <- mean(x.0 <= x.middle[2])*n.x0/(n.x0+1)
-
-  # find pseudo value for x.middle[1] on normal(0,1) 
-  pseudo.small.0 <- qnorm(q.0.small, mean=0, sd=1)
-  pseudo.small.1 <- qnorm(q.1.small, mean=0, sd=1)
-
-  # find pseudo value for x.middle[2] on normal(0,1) 
-  pseudo.big.0 <- qnorm(q.0.big, mean=0, sd=1)
-  pseudo.big.1 <- qnorm(q.1.big, mean=0, sd=1)
-
-  mu <- (pseudo.small.0*pseudo.big.1 - pseudo.small.1*pseudo.big.0)/(pseudo.big.1-pseudo.small.1) 
-
-  sigma <- (pseudo.small.0-mu)/pseudo.small.1
-
-  return(list(mu=mu, sigma=sigma))
-}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/genome_tables/genome_table.dmel.r5.32.txt	Thu Jan 17 15:57:35 2013 -0500
@@ -0,0 +1,15 @@
+YHet	347038
+dmel_mitochondrion_genome	19517
+2L	23011544
+X	22422827
+3L	24543557
+4	1351857
+2R	21146708
+3R	27905053
+Uextra	29004656
+2RHet	3288761
+2LHet	368872
+3LHet	2555491
+3RHet	2517507
+U	10049037
+XHet	204112
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/genome_tables/genome_table.human.hg18.txt	Thu Jan 17 15:57:35 2013 -0500
@@ -0,0 +1,25 @@
+chr1 247249719
+chr2 242951149
+chr3 199501827
+chr4 191273063
+chr5 180857866
+chr6 170899992
+chr7 158821424
+chr8 146274826
+chr9 140273252
+chr10 135374737
+chr11 134452384
+chr12 132349534
+chr13 114142980
+chr14 106368585
+chr15 100338915
+chr16 88827254
+chr17 78774742
+chr18 76117153
+chr19 63811651
+chr20 62435964
+chr21 46944323
+chr22 49691432
+chrX 154913754
+chrY 57772954
+chrM 16571
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/genome_tables/genome_table.human.hg19.txt	Thu Jan 17 15:57:35 2013 -0500
@@ -0,0 +1,25 @@
+chr1 249250621
+chr2 243199373
+chr3 198022430
+chr4 191154276
+chr5 180915260
+chr6 171115067
+chr7 159138663
+chr8 146364022
+chr9 141213431
+chr10 135534747
+chr11 135006516
+chr12 133851895
+chr13 115169878
+chr14 107349540
+chr15 102531392
+chr16 90354753
+chr17 81195210
+chr18 78077248
+chr19 59128983
+chr20 63025520
+chr21 48129895
+chr22 51304566
+chrX 155270560
+chrY 59373566
+chrM 16571
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/genome_tables/genome_table.mm9.txt	Thu Jan 17 15:57:35 2013 -0500
@@ -0,0 +1,22 @@
+chr1 197195432
+chr2 181748087
+chr3 159599783
+chr4 155630120
+chr5 152537259
+chr6 149517037
+chr7 152524553
+chr8 131738871
+chr9 124076172
+chr10 129993255
+chr11 121843856
+chr12 121257530
+chr13 120284312
+chr14 125194864
+chr15 103494974
+chr16 98319150
+chr17 95272651
+chr18 90772031
+chr19 61342430
+chrX 166650296
+chrY 15902555
+chrM 16299
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/genome_tables/genome_table.worm.ws220.txt	Thu Jan 17 15:57:35 2013 -0500
@@ -0,0 +1,7 @@
+I 15072423
+II 15279345
+III 13783700
+IV 17493793
+V 20924149
+X 17718866
+MtDNA 13794
\ No newline at end of file
--- a/idrPlotDef.xml	Thu Jan 17 15:45:48 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,31 +0,0 @@
-<!--
-Usage of THIS SCRIPT: ./idrPlotWrapper.sh em uri outputfile 
--->
-
-<tool id="batch_consistency_plot" name="IDR-Plot">
-  <description>Plot Consistency Analysis on IDR output files</description>
-  <command interpreter="bash">idrPlotWrapper.sh $em $uri $outputfile</command>
-  <inputs>
-    <param name="uri" type="data" label="uri.sav file (output from IDR consistency analysis)"/>
-    <param name="em" type="data" label="em.sav file (output from IDR consistency analysis)"/>
-  </inputs>
-  <outputs>
-    <data format="pdf" name="outputfile" label="IDR-plot.pdf"/>
-  </outputs>
-
-  <tests>
-    <test>
-<!--
-      <param name="input" value="fa_gc_content_input.fa"/>
-      <output name="out_file1" file="fa_gc_content_output.txt"/>
--->
-    </test>
-  </tests>
-
-  <help>
-Plots the correspondence curve and IDR threshold (i.e. number of selected peaks vs IDR) for each pair of samples.
-Uses the output of IDR consistency analysis and produces a downloadable PDF file containing the graphical analysis.
-This is a part of the IDR package.  For more information about IDR, see https://sites.google.com/site/anshulkundaje/projects/idr.
-  </help>
-
-</tool>
--- a/idrPlotWrapper.sh	Thu Jan 17 15:45:48 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,39 +0,0 @@
-#!/bin/bash
-
-# idrPlotWrapper.sh
-# OICR: Kar Ming Chu
-# July 2012
-
-# BASH wrapper for batch-consistency-plot.r (part of the IDR package)
-# For use with Galaxy 
-
-# Usage of batch-consistency-plot.r: Rscript batch-consistency-plot-merged.r [npairs] [output.dir] [input.file.prefix 1, 2, 3 ...]
-#	npairs - will be a constant, since Galaxy requires explicit control over input and output files
-
-# Usage of THIS SCRIPT: ./idrPlotWrapper.sh em uri outputfile
-#	em - em.sav file provided by Galaxy
-#	uri - uri.sav file provided by Galaxy
-#	outputfile - output file name specified by Galaxy
-
-main() {
-	EM="${1}" 		# absolute file path to em.sav file, provided by Galaxy
-	URI="${2}"		# absolute file parth to uri.sav file, provided by Galaxy
-	OUTFILE="${3}"		# name of desired output file
-
-	cp "${EM}" ./idrPlot-em.sav	# cp to this directory and rename so they can be found by idrPlot
-	cp "${URI}" ./idrPlot-uri.sav
-	
-	Rscript /mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/batch-consistency-plot.r 1 ./idrPlot idrPlot
-
-	# convert post script to pdf file
- 	ps2pdf ./idrPlot-plot.ps ./idrPlot-plot.pdf
-
-	# rename to output file name
-	mv ./idrPlot-plot.pdf "${OUTFILE}"
-
-	# clean up
-	rm idrPlot-em.sav
-	rm idrPlot-uri.sav
-}
-
-main "${@}"
--- a/idrToolDef.xml	Thu Jan 17 15:45:48 2013 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,61 +0,0 @@
-<!--
-
-Script Usage:
-Rscript batch-consistency-analysis.r
-../3066_rep1_VS_input0.macs14.out.regionPeak
-../3066_rep2_VS_input0.macs14.out.regionPeak
-1000
-3066_rep1_VS_rep2
-0
-F
-p.value
-genome_table.txt [ drop down to select ]
--->
-
-<tool id="batch_consistency_analysis_2" name="IDR">
-  <description>Consistency Analysis on a pair of narrowPeak files</description>
-  <command interpreter="Rscript">batch-consistency-analysis.r $input1 $input2 $halfwidth $overlap $option $sigvalue $gtable $rout $aboveIDR $ratio $emSav $uriSav</command>
-  <inputs>
-    <param format="narrowPeak" name="input1" type="data" label="First NarrowPeak File"/>
-    <param format="narrowPeak" name="input2" type="data" label="Second NarrowPeak File"/>
-    <param name="halfwidth" size="4" type="integer" value="1000" label="Half-Width" help="-1 if using reported peak width"/>
-<!--    <param name="outputprefix" type="text" size="50" label="Output Prefix" value="3066_rep1_VS_rep2"/> -->
-    <param name="option" type="select" label="File Type" value="F">
-      <option value="F">Narrow Peak</option>
-      <option value="T">Broad Peak</option>
-    </param>
-    <param name="overlap" size="4" type="float" value="0" label="Over-Lap Ratio" help="Between 0 and 1, inclusively" min="0" max="1"/>
-    <param name="sigvalue" type="select" label="Significant Value" value="p.value" help="Select p-value if the input peak files are generated by MAC. Select q-value if the input peak files are generated by SPP.">
-      <option value="p.value">p-value</option>
-      <option value="q.value">q-value</option>
-      <option value="signal.value">Significant Value</option>
-    </param>
-    <param name="gtable" type="select" label="Genome Table" value="/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/genome_tables/genome_table.worm.ws220.txt">
-      <option value="/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/genome_tables/genome_table.human.hg19.txt">human hg19</option>
-      <option value="/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/genome_tables/genome_table.mm9.txt">mouse mm9</option>
-      <option value="/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/genome_tables/genome_table.worm.ws220.txt">worm ws220</option>
-      <option value="/mnt/galaxyTools/galaxy-central/tools/modENCODE_DCC_tools/idr/genome_tables/genome_table.dmel.r5.32.txt">dmel r5.32</option>
-    </param>
-  </inputs>
-  <outputs>
-    <data format="txt" name="rout" label="IDR.Rout.txt"/>
-    <data format="txt" name="aboveIDR" label="IDR.npeaks-aboveIDR.txt"/>
-    <data format="txt" name="ratio" label="IDR.overlapped-peaks.txt"/>
-    <data format="txt" name="emSav" label="IDR.em.sav"/>
-    <data format="txt" name="uriSav" label="IDR.uri.sav"/>
-  </outputs>
-
-  <tests>
-    <test>
-<!--
-      <param name="input" value="fa_gc_content_input.fa"/>
-      <output name="out_file1" file="fa_gc_content_output.txt"/>
--->
-    </test>
-  </tests>
-
-  <help>
-Reproducibility is essential to reliable scientific discovery in high-throughput experiments. The IDR (Irreproducible Discovery Rate) framework is a unified approach to measure the reproducibility of findings identified from replicate experiments and provide highly stable thresholds based on reproducibility. Unlike the usual scalar measures of reproducibility, the IDR approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. In layman's terms, the IDR method compares a pair of ranked lists of identifications (such as ChIP-seq peaks). These ranked lists should not be pre-thresholded i.e. they should provide identifications across the entire spectrum of high confidence/enrichment (signal) and low confidence/enrichment (noise). The IDR method then fits the bivariate rank distributions over the replicates in order to separate signal from noise based on a defined confidence of rank consistency and reproducibility of identifications i.e the IDR threshold. For more information on IDR, see https://sites.google.com/site/anshulkundaje/projects/idr
-  </help>
-
-</tool>