On 06/28/2016 03:54 AM, Johnston, Jeffrey wrote:
During the BioC developer day, Hervé brought up the idea of possibly extending the concept of GenomicViews to other use cases. I'd like to share how we currently use GenomicRanges, Views and RleLists to perform certain analyses, and hopefully demonstrate that a way to directly use Views with GRanges would be quite useful.As an example, let's say we have 2 ChIP-seq samples (as BigWig files) and a set of genomic ranges we are interested in (as a BED file). We want to find the sum of the ChIP-seq signal found in our regions of interest for each of the two samples. We'd first import the BED file as a GRanges object. Annotating the GRanges with two metadata columns representing the ChIP-seq signal for the two samples seems like a straightforward use for Views (in particular, viewSums), but it is a bit convoluted. The main problem is that Views work with RangesLists, not GRanges. Coercing a GRanges to a RangesList possibly disrupts the order, so we have to first reorder the GRanges to match the order it will be given after coercion (keeping track so we can later revert back to the original order): regions <- import("regions_of_interest.bed") sample1_cov <- import("sample1.bw", as="RleList") sample2_cov <- import("sample2.bw", as="RleList") oo <- order(regions) regions <- regions[oo] Now we can construct a View and use viewSums to obtain our values (unlisting them as they are grouped by seqnames) and add them as a metadata column in our GRanges, restoring the original order of the GRanges when we are done: v <- Views(sample1_cov, as(regions, "RangesList")) mcols(regions)$sample1_signal <- unlist(viewSums(v)) regions[oo] <- regions We then repeat the process to add another metadata column for sample2. To me, having the ability to use a GRanges as a view into an RleList makes a lot more sense. That would allow us to reduce all the above complexity to something like: regions$sample1_signal <- viewSums(Views(sample1_cov, regions)) regions$sample2_signal <- viewSums(Views(sample2_cov, regions)) That alone would be great! But, there's a way to make it even better. Storing these RleLists in memory for each of our samples is quite inefficient, especially since our regions of interest are only a small portion of them. The rtracklayer package already has some very useful functionality for instantiating an RleList with only the data from specific ranges of a BigWig file. Taking advantage of that, we can dramatically reduce our memory usage and increase our performance like so: regions <- import("regions_of_interest.bed") sample1_cov <- import("sample1.bw", as="RleList", which=regions) sample2_cov <- import("sample2.bw", as="RleList", which=regions) regions$sample1_signal <- viewSums(Views(sample1_cov, regions)) regions$sample2_signal <- viewSums(Views(sample2_cov, regions)) But can't this functionality be included in Views? Why not have it accept a BigWig file in place of an RleList and have it selectively load the portions of the BigWig it needs based on the provided GRanges? That would allow this: regions <- import("regions_of_interest.bed") regions$sample1_signal <- viewSums(Views("sample1.bw", regions)) regions$sample2_signal <- viewSums(Views("sample2.bw", regions))
Exactly. Thanks Jeff for taking the time to put this so nicely together. That's what I've been having in mind for years. The above is very intuitive and frees the user of having to deal with a lot of low-level details. The ability to subset with a GRanges subscript already provides some level of convenience but having the ability to formally define views on genomic data goes even further. In particular, as you pointed out, it allows the use of delayed views realization strategies behind the scene which can lead to important performance boosts. H.
The above is quite close to how I use GRanges and BigWigs now, except I have to write and maintain all the hackish code to link BigWig files, GRanges, Views, RangesLists and RleLists together into something that behaves as one would intuitively expect. I’d welcome any thoughts on how people perform similar analyses that involve GRanges and data stored in BigWig files or RleLists, and whether this would also be useful to them. Thanks, Jeff Johnston Zeitlinger Lab Stowers Institute _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
-- Hervé Pagès Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N, M1-B514 P.O. Box 19024 Seattle, WA 98109-1024 E-mail: hpa...@fredhutch.org Phone: (206) 667-5791 Fax: (206) 667-1319 _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
