[Bioc-devel] Extending Views to support GRanges (and possibly BigWig files)

[Altuna Akalin]

We have this low level functionality in genomation::ScoreMatrixBin (when
bin.num=1), where users can give a set of regions they are interested in as
GRanges and input data in the form of RleList, GRanges, Bam file or BigWig
file, and get the a summary of scores per region of interest. Currently, it
can do mean,median but not sum. Usually you will need "mean" if the regions
of interest are not equal length.

you probably want this functionality as core functionality, which is a good
idea. In that case, you should be careful when dealing with methylation
data, where no coverage doesn't mean 0 % methylation. This causes problems
when using import.bw() on bigWig files containing methylation data. bases
with no coverage are imported as 0 % methylation.

Best,
Altuna

On Wed, Jun 29, 2016 at 1:45 AM, Hervé Pagès <hpages at fredhutch.org> wrote:

> 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 at 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: hpages at fredhutch.org
> Phone:  (206) 667-5791
> Fax:    (206) 667-1319
>
>
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