Question: Operations across Lists of GenomicRanges?
0
3.0 years ago by
nhejazi0
UC Berkeley
nhejazi0 wrote:

I am currently working with GenomicRanges structures stored in a large list (~10,000 entries) produced via output from a foreach loop. These lists all contain similar GenomicRanges structures, and I would like to generate a single GenomicRanges or data.frame structure with all of the unique entries across the many structures in the list (and any relevant information, e.g., seqnames, ranges, strand) as well as a count of how many times each appears across the many structures (perhaps in a metadata column). I assume that the best way to accomplish this would be via ldply from plyr, but am unsure of what a good way to good about this might be? Has anyone solved a similar problem before?

genomicranges plyr lists • 521 views
modified 3.0 years ago by Michael Lawrence11k • written 3.0 years ago by nhejazi0

Please be more specific. How similar are they? What do you mean by unique entry, i.e., what is an entry? Some code (including construction the first few elements in the list) would help.

Example Code:

permute_limma <- foreach (i = 1:nsim) %dopar% {
set.seed(6401^2)
design_2 <- sample(design)
print(paste0("Limma - the ", i, "th iteration is underway."))
fit <- lmFit(y, design_2)
fit <- eBayes(fit)
tt <- topTable(fit, coef = 2,adjust.method = "BH",
sort.by = "none", number = Inf)
return(tt)
}
ADD REPLYlink modified 3.0 years ago by Martin Morgan ♦♦ 23k • written 3.0 years ago by nhejazi0

The premise is simply that a particular data analytic function (e.g., limma) is called on a data set in data.frame or GenomicRanges form. The output is a data structure of genomic sites or regions (e.g., seqnames, positions, etc.). The list returned by foreach contains 1000+ of such output tables. The idea is to look at the frequency with which genomic sites/regions appear in across each of these output tables (contained in the list).

Answer: Operations across Lists of GenomicRanges?
3
3.0 years ago by
United States
Michael Lawrence11k wrote:

Let's say you have a list of GRanges objects, grl, each with the same columns in mcols(). The first step is to mark the list as a list of GRanges, i.e., a GRangesList, because the data are easier to compute on when their semantics are explicit. This can be done with List(). Then unlist() that to concatenate the GRanges together. If you want to then count each unique range, first find the unique ranges, then count how many times they match the original set.

gr <- unlist(List(grl))
gr_levels <- unique(gr)
gr_levels\$count <- countMatches(gr_levels, gr)