R/heatmapfCCAC.R

In fCCAC: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets

heatmapfCCAC <- function(fc){
  
#  M <- c() #store sample names
#  for (i in seq(from=1, to=length(fc$samples), by=1)    ){
#    M <- c(M, strsplit( as.character(fc$samples[i]), split="_vs_")[[1]] )
#  }
#  M <- unique(M)
   
  M <- rep("name", nrow(fc)*2)  #store sample names
  k <- 0
  for (i in seq(from=1, to=length(fc$samples), by=1)    ){
    temp_names <- strsplit( as.character(fc$samples[i]), split="_vs_")[[1]] 
    k<- k +1 ; M[k] <- temp_names[1]
    k<- k +1 ; M[k] <- temp_names[2]
  }
  M <- unique(M)
  rm(k)
  
  #plot heatmap
  Fv <- matrix(NA, nrow=length(M), ncol=length(M))
  for (i in seq(from=1, to=length(M), by=1)  ){
    for (j in seq(from=1, to=length(M), by=1) ){
      o1 <- which(as.character(fc$samples) == paste(M[i], M[j], sep="_vs_" ) )
      o2 <- which(as.character(fc$samples) == paste(M[j], M[i], sep="_vs_" ) )
      if (length(o1)==1){ Fv[i,j] <- fc[o1,2] }  #fc$F[o1]
      if (length(o2)==1){ Fv[i,j] <- fc[o2,2] }  #fc$F[o2]
      if (length(o1)==0 & length(o2)==0) {Fv[j,i] <- 100 }
    }
  }
  colnames(Fv) <- M
  rownames(Fv) <- M

  Heatmap(Fv, name='F (%)')

}