I really like the package Gviz to prepare figures for presentations and publications (I have used it in B with some tidying up in inskape).

It is a fantastic visualization package, but the time and effort that it takes to get the figures just right is a little too much for my daily data inspection/visualization tasks. An example of this is when plotting coverage tracks; by default axis of each panel are scaled independently which makes visualization tricky. So I created a little function that will loop over a list of BigWig tracks, find the maximal coverage in each chromosome, and return the max of all tracks. This value can no be used as ymax in plots with multiple tracks.

  ## calculate the max values
   maxCovBw <- function(gr, myChr) {
      max_cov <- max(gr[seqnames(gr) %in% myChr,]$score)
      return(max_cov)
   }

   maxCovFiles <- function(files, chrs){
      reps_gr <- lapply(files, rtracklayer:::import)
      for(i in seq_along(chrs)){
         myChr = chrs[i]
         print(myChr)
         max_coverage <- c()
         max_cov <- round(
            max(
               sapply(reps_gr, maxCovBw, myChr=myChr)
            )
         , 0)
         max_coverage[myChr] <- max_cov 
      }
      return(data.frame(max_coverage))
   }

   max_cov <- maxCovFiles(list(bw1_file, bw2_file), seqinfo(bw1)$seqnames)

And why chromosomes you ask. Because:

1. Gviz plots are chromosome-centric, that is one needs to specify the chromosome for each track, and all tracks in a plot must be in the same location.
2. The particular plot that I am currently working on is a overview of a full contig, so I don’t zoom in to a particular range in that contig.

A reproducible example might appear in the future. EDIT: it has.