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Question: views on Rle using GRanges object
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4.1 years ago by
Michael Lawrence10.0k
United States
Michael Lawrence10.0k wrote:
Raising this one from the dead: this would be a very nice thing to have. It's tough explaining to users how to bridge GRanges and RangesList for aggregating vectors (RleLists) by GRanges. We could start with rangeSums, rangeMeans, rangeMaxs, etc, that directly summarize and RleList, and then maybe move towards a GenomicViews object. Maybe something for the next release cycle? Michael On Wed, Aug 17, 2011 at 5:55 AM, Michael Lawrence <michafla@gene.com> wrote: > > > 2011/8/16 Hervé Pagès <hpages@fhcrc.org> > >> Michael, >> >> On 11-08-16 10:52 AM, Michael Lawrence wrote: >> >>> >>> >>> 2011/8/16 Hervé Pagès <hpages@fhcrc.org <mailto:hpages@fhcrc.org="">> >>> >>> >>> Hi Michael, >>> >>> >>> On 11-08-15 10:28 PM, Michael Lawrence wrote: >>> >>> On Mon, Aug 15, 2011 at 10:10 PM, Michael >>> Lawrence<michafla@gene.com <mailto:michafla@gene.com="">>__wrote: >>> >>> >>> >>> >>> On Mon, Aug 15, 2011 at 5:25 PM, Janet >>> Young<jayoung@fhcrc.org <mailto:jayoung@fhcrc.org="">> wrote: >>> >>> >>> Hi again, >>> >>> I have another question, in the "I think I need a >>> convoluted workaround, >>> but maybe I'm missing a simple solution" genre (seems >>> like most of my >>> questions are like that). >>> >>> I have an RleList object representing mapability for the >>> whole human >>> genome. I'd like to: >>> (a) calculate viewMeans for various regions of interest >>> that I've been >>> representing as GRanges, and >>> (b) I'd like the underlying code to be smart and match >>> chromosome names up >>> in the RleList and the GRanges object (not rely on >>> chromosomes being ordered >>> the same in the two objects), and >>> (c) I'd like to return the viewMeans results in the same >>> order as the >>> objects in my original GRanges >>> >>> I don't think this is currently implemented without >>> doing several >>> coercions (that introduce their own complications) but >>> I'm not sure. Some >>> code is below that shows what I'm trying to do. It >>> seems like it might be >>> a common kind of way to use viewMeans - I imagine people >>> are gradually >>> switching to use GRanges more and more? but really I >>> don't know. >>> >>> My real world question is that I've read in mapability >>> from a UCSC bigWig >>> file, and made that into an RleList. I have a bunch of >>> other regions I've >>> read in (again from UCSC) using rtracklayer as GRanges, >>> and have annotated >>> those with various things I'm interested in (e.g. number >>> of RNAseq reads in >>> the region). I want to add the average mapability for >>> each region, so that >>> I can later look at how mapability affects those other >>> things I'm >>> annotating. >>> >>> Should I be being more strict with myself about how my >>> GRanges are ordered >>> and making sure they all have the same seqlevels and >>> seqlengths? Perhaps >>> that would help the coercion workarounds go more smoothly. >>> >>> thanks, >>> >>> Janet >>> >>> >>> library(GenomicRanges) >>> >>> ### make an RleList. Just for this example, I starting >>> with a GRanges >>> object and used coverage to get an RleList example. To >>> get my real data I >>> read in a UCSC bigWig file. >>> >>> fakeRegions<- GRanges(seqnames=c("chrA","__chrA", >>> "chrB", "chrC"), >>> ranges=IRanges(start=c(1,51,1,__1), end=c(60,90,20,10) ) >>> ) >>> seqlengths(fakeRegions)<- c(100,100,100) >>> myRleList<- coverage(fakeRegions) >>> rm(fakeRegions) >>> >>> myRleList >>> >>> # SimpleRleList of length 3 >>> # $chrA >>> # 'integer' Rle of length 100 with 4 runs >>> # Lengths: 50 10 30 10 >>> # Values : 1 2 1 0 >>> # >>> #$chrB >>> # 'integer' Rle of length 100 with 2 runs >>> # Lengths: 20 80 >>> # Values : 1 0 >>> # >>> # $chrC >>> # 'integer' Rle of length 100 with 2 runs >>> # Lengths: 10 90 >>> # Values : 1 0 >>> >>> ### make some regions of interest >>> myRegions<- GRanges(seqnames=c("chrB", "chrC", "chrB"), >>> ranges=IRanges(start=c(1,1,5), end=c(20,20,10) ), >>> geneNames=c("g1","g2","g3") ) >>> myRegions >>> # GRanges with 3 ranges and 1 elementMetadata value >>> # seqnames ranges strand | geneNames >>> #<rle> <iranges> <rle> |<character> >>> # [1] chrB [1, 20] * | g1 >>> # [2] chrC [1, 20] * | g2 >>> # [3] chrB [5, 10] * | g3 >>> # >>> # seqlengths >>> # chrB chrC >>> # NA NA >>> >>> ## can't use GRanges directly >>> Views( myRleList, myRegions) >>> # Error in RleViewsList(rleList = subject, rangesList = >>> start) : >>> # 'rangesList' must be a RangesList object >>> >>> ## can't use a simple coercion >>> Views( myRleList, as(myRegions,"RangesList") ) >>> # Error in .Method(..., FUN = FUN, MoreArgs = MoreArgs, >>> SIMPLIFY = >>> SIMPLIFY, : >>> # all object lengths must be multiple of longest >>> object length >>> >>> ### although I can use that coercion if I first give the >>> GRanges object >>> the same levels as the RleList to force the lists to >>> have the same names as >>> each other: >>> seqlevels(myRegions)<- names(myRleList) >>> viewMeans(Views( myRleList, as(myRegions,"RangesList") ) ) >>> # SimpleNumericList of length 3 >>> # [["chrA"]] numeric(0) >>> # [["chrB"]] 1 1 >>> # [["chrC"]] 0.5 >>> >>> >>> These two lines seem pretty concise to me. It makes sense to >>> layer a >>> RangesList on top of an RleList. Storing the result would of >>> course be >>> easier if you had sorted the GRanges by the seqlevels. >>> Having a utility for >>> that would be nice (orderBySeqlevels?). It would also be >>> easy with >>> RangedData, which enforces ordering by space. >>> >>> >>> Just to clarify, here is how the function would work: >>> >>> values(myRegions)$meanCov[__orderBySeqlevels(myRegions)]<- >>> unlist(viewMeans(Views( myRleList, as(myRegions,"RangesList") ) ), >>> use.names=FALSE) >>> >>> So 'orderBySeqlevels' would be a nice bridge back into the flat >>> GRanges >>> world from the Listy world of IRanges. >>> >>> >>> Well I'm just remembering now that we still don't have an "order" >>> method for GRanges objects like we do for IRanges object. The >>> "natural" >>> order for a GRanges object would be to order first by (a) seqlevel, >>> (b) then by strand, (c) then by start, (d) and finally by end. >>> >>> We already do (c) and (d) for IRanges. >>> >>> Also the "reduce" method for GRanges already uses this "natural" >>> order implicitly. >>> >>> So we will add this "order" method and the other basic order- related >>> methods (sort, >=, <=, ==, !=, unique, duplicated, they are all >>> missing) for GRanges objects. That will cover Janet's use case and >>> other user cases (I think someone asked how to find duplicated in >>> a GRanges object a while ago on this list). >>> >>> >>> An "order" method would be great. Note though that the coercion to >>> RangesList only sorts by seqlevels, so we would need something that gave >>> out that order vector. The point here is to avoid forcing the user to >>> modify the order of the original GRanges. >>> >> >> With order() the user will still be able to achieve this with something >> like: >> >> regionMeans <- function(regions, cvg) >> { >> seqlevels(regions) <- names(cvg) >> oo <- order(regions) >> regions <- regions[oo] >> ans <- unlist( >> viewMeans( >> Views(cvg, as(regions, "RangesList")) >> ), use.names=FALSE) >> ans[oo] <- ans # restore original order >> ans >> } >> >> values(myRegions)$meanCov <- regionMeans(myRegions, myRleList) >> >> Tricky :-/ >> >> But maybe we could provide a "mean" method that accepts 2 args: >> a GRanges and an RleList/IntegerList/NumericList. Same with >> min, max, sum etc... they would all return a numeric vector of the >> same length as their first argument (the GRanges object). >> >> > Patrick and I had talked about this. We were calling it the rangeMeans > function. It would go along with rangeMins, rangeMaxs, etc. Basically a way > to perform a windowed computation without having to construct an > intermediate Views. This is a lot easier to design and implement, as you > were saying below. > > There are a lot of other (non-Rle) Vectors that would benefit from > window-based summaries. These include the basic vector types, as well as > ranges themselves. For example, I and others have encountered a need to > find the nearest neighbors of a set of ranges, with the constraint that > they fall within the same gene or exon or whatever. One could come up with > a GRanges with the seqnames corresponding to a gene, but that's a hack and > the current looping implementation of nearest,GRanges is a bit slow. > > Michael > > Another approach could be that we make the Views() constructor more >> flexible so Views(myRleList, myRegions) would just work and return >> a Views object (not a ViewsList) but that means redefining what a >> Views object can be (@subject can now be a named List and @ranges >> a GRanges object). Maybe a cleaner design would be to use a new >> container for this e.g. GViews (for Genomic Views)... >> >> H. >> >> >> >>> Michael >>> >>> Cheers, >>> H. >>> >>> >>> >>> Michael >>> >>> >>> >>> ### getting close to a solution - but I'd have liked to >>> have been able to >>> unlist this and directly add to my GRanges object e.g. >>> values(myRegions)$meanCov<- unlist(viewMeans(Views( >>> myRleList, >>> as(myRegions,"RangesList") ) ), use.names=FALSE) >>> ### but the regions are in a different order to how I >>> started, so the >>> above command would associate the wrong scores with the >>> wrong regions. >>> >>> sessionInfo() >>> >>> R version 2.13.1 (2011-07-08) >>> Platform: i386-apple-darwin9.8.0/i386 (32-bit) >>> >>> locale: >>> [1] en_US.UTF-8/en_US.UTF-8/C/C/__en_US.UTF-8/en_US.UTF-8 >>> >>> attached base packages: >>> [1] stats graphics grDevices utils datasets >>> methods base >>> >>> other attached packages: >>> [1] GenomicRanges_1.4.6 IRanges_1.10.5 >>> >>> _________________________________________________ >>> Bioconductor mailing list >>> Bioconductor@r-project.org >>> <mailto:bioconductor@r-project.org> >>> >>> https://stat.ethz.ch/mailman/__listinfo/bioconductor >>> <https: stat.ethz.ch="" mailman="" listinfo="" bioconductor=""> >>> Search the archives: >>> http://news.gmane.org/gmane.__ >>> science.biology.informatics.__conductor >>> <http: news.gmane.org="" gmane.science.biology.informatics.="">>> conductor> >>> >>> >>> >>> >>> [[alternative HTML version deleted]] >>> >>> >>> _________________________________________________ >>> Bioconductor mailing list >>> Bioconductor@r-project.org <mailto:bioconductor@r-project.org> >>> >>> https://stat.ethz.ch/mailman/__listinfo/bioconductor >>> <https: stat.ethz.ch="" mailman="" listinfo="" bioconductor=""> >>> Search the archives: >>> http://news.gmane.org/gmane.__science.biology.informatics.__ >>> conductor >>> <http: news.gmane.org="" gmane.science.biology.informatics.="">>> conductor> >>> >>> >>> >>> -- >>> 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@fhcrc.org <mailto:hpages@fhcrc.org> >>> Phone: (206) 667-5791 <tel:%28206%29%20667-5791> >>> Fax: (206) 667-1319 <tel:%28206%29%20667-1319> >>> >>> >>> >> >> -- >> 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@fhcrc.org >> Phone: (206) 667-5791 >> Fax: (206) 667-1319 >> > > [[alternative HTML version deleted]]
modified 4.1 years ago by Hervé Pagès ♦♦ 13k • written 4.1 years ago by Michael Lawrence10.0k

Hi there,

I just stumbled over more or less the same issue:

I would like to subset a RleList (as returned from coverage) by a GRanges object. Rather than applying any kind of summary statistics on these data, I would like to store it as it is.

I am fine with using RleList[Rangeslist], I just wondered if there is already any other solution out there..

Any comments will be highly appreciated!

Best,

Stefanie

0
4.1 years ago by
Hervé Pagès ♦♦ 13k
United States
Hervé Pagès ♦♦ 13k wrote:
Hi Michael, On 03/31/2014 05:22 AM, Michael Lawrence wrote: > Raising this one from the dead: this would be a very nice thing to have. > It's tough explaining to users how to bridge GRanges and RangesList for > aggregating vectors (RleLists) by GRanges. We could start with > rangeSums, rangeMeans, rangeMaxs, etc, that directly summarize and > RleList, and then maybe move towards a GenomicViews object. Maybe > something for the next release cycle? Yeah I'd like to start working on something like this. Note that, kind of related to this, I added subsetting a named List by a GRanges subscript recently. But what we really need is a simple container that bundles a GRanges object with a named List subject, I think. H. > > Michael > > > On Wed, Aug 17, 2011 at 5:55 AM, Michael Lawrence <michafla at="" gene.com=""> <mailto:michafla at="" gene.com="">> wrote: > > > > 2011/8/16 Hervé Pagès <hpages at="" fhcrc.org="" <mailto:hpages="" at="" fhcrc.org="">> > > Michael, > > On 11-08-16 10:52 AM, Michael Lawrence wrote: > > > > 2011/8/16 Hervé Pagès <hpages at="" fhcrc.org=""> <mailto:hpages at="" fhcrc.org=""> <mailto:hpages at="" fhcrc.org=""> <mailto:hpages at="" fhcrc.org="">>> > > > Hi Michael, > > > On 11-08-15 10:28 PM, Michael Lawrence wrote: > > On Mon, Aug 15, 2011 at 10:10 PM, Michael > Lawrence<michafla at="" gene.com=""> <mailto:michafla at="" gene.com=""> <mailto:michafla at="" gene.com=""> <mailto:michafla at="" gene.com="">>>____wrote: > > > > > On Mon, Aug 15, 2011 at 5:25 PM, Janet > Young<jayoung at="" fhcrc.org=""> <mailto:jayoung at="" fhcrc.org=""> <mailto:jayoung at="" fhcrc.org=""> <mailto:jayoung at="" fhcrc.org="">>> wrote: > > > Hi again, > > I have another question, in the "I think I > need a > convoluted workaround, > but maybe I'm missing a simple solution" > genre (seems > like most of my > questions are like that). > > I have an RleList object representing > mapability for the > whole human > genome. I'd like to: > (a) calculate viewMeans for various regions > of interest > that I've been > representing as GRanges, and > (b) I'd like the underlying code to be smart > and match > chromosome names up > in the RleList and the GRanges object (not > rely on > chromosomes being ordered > the same in the two objects), and > (c) I'd like to return the viewMeans results > in the same > order as the > objects in my original GRanges > > I don't think this is currently implemented > without > doing several > coercions (that introduce their own > complications) but > I'm not sure. Some > code is below that shows what I'm trying to > do. It > seems like it might be > a common kind of way to use viewMeans - I > imagine people > are gradually > switching to use GRanges more and more? but > really I > don't know. > > My real world question is that I've read in > mapability > from a UCSC bigWig > file, and made that into an RleList. I have > a bunch of > other regions I've > read in (again from UCSC) using rtracklayer > as GRanges, > and have annotated > those with various things I'm interested in > (e.g. number > of RNAseq reads in > the region). I want to add the average > mapability for > each region, so that > I can later look at how mapability affects > those other > things I'm > annotating. > > Should I be being more strict with myself > about how my > GRanges are ordered > and making sure they all have the same > seqlevels and > seqlengths? Perhaps > that would help the coercion workarounds go > more smoothly. > > thanks, > > Janet > > > library(GenomicRanges) > > ### make an RleList. Just for this example, > I starting > with a GRanges > object and used coverage to get an RleList > example. To > get my real data I > read in a UCSC bigWig file. > > fakeRegions<- > GRanges(seqnames=c("chrA","____chrA", > "chrB", "chrC"), > ranges=IRanges(start=c(1,51,1,____1), > end=c(60,90,20,10) ) ) > seqlengths(fakeRegions)<- c(100,100,100) > myRleList<- coverage(fakeRegions) > rm(fakeRegions) > > myRleList > > # SimpleRleList of length 3 > # $chrA > # 'integer' Rle of length 100 with 4 runs > # Lengths: 50 10 30 10 > # Values : 1 2 1 0 > # > #$chrB > # 'integer' Rle of length 100 with 2 runs > # Lengths: 20 80 > # Values : 1 0 > # > # $chrC > # 'integer' Rle of length 100 with 2 runs > # Lengths: 10 90 > # Values : 1 0 > > ### make some regions of interest > myRegions<- GRanges(seqnames=c("chrB", > "chrC", "chrB"), > ranges=IRanges(start=c(1,1,5), > end=c(20,20,10) ), > geneNames=c("g1","g2","g3") ) > myRegions > # GRanges with 3 ranges and 1 > elementMetadata value > # seqnames ranges strand | geneNames > #<rle> <iranges> <rle> |<character> > # [1] chrB [1, 20] * | g1 > # [2] chrC [1, 20] * | g2 > # [3] chrB [5, 10] * | g3 > # > # seqlengths > # chrB chrC > # NA NA > > ## can't use GRanges directly > Views( myRleList, myRegions) > # Error in RleViewsList(rleList = subject, > rangesList = > start) : > # 'rangesList' must be a RangesList object > > ## can't use a simple coercion > Views( myRleList, as(myRegions,"RangesList") ) > # Error in .Method(..., FUN = FUN, MoreArgs > = MoreArgs, > SIMPLIFY = > SIMPLIFY, : > # all object lengths must be multiple of > longest > object length > > ### although I can use that coercion if I > first give the > GRanges object > the same levels as the RleList to force the > lists to > have the same names as > each other: > seqlevels(myRegions)<- names(myRleList) > viewMeans(Views( myRleList, > as(myRegions,"RangesList") ) ) > # SimpleNumericList of length 3 > # [["chrA"]] numeric(0) > # [["chrB"]] 1 1 > # [["chrC"]] 0.5 > > > These two lines seem pretty concise to me. It > makes sense to > layer a > RangesList on top of an RleList. Storing the > result would of > course be > easier if you had sorted the GRanges by the > seqlevels. > Having a utility for > that would be nice (orderBySeqlevels?). It would > also be > easy with > RangedData, which enforces ordering by space. > > > Just to clarify, here is how the function would work: > > > values(myRegions)$meanCov[____orderBySeqlevels(myRegions)]<- > unlist(viewMeans(Views( myRleList, > as(myRegions,"RangesList") ) ), > use.names=FALSE) > > So 'orderBySeqlevels' would be a nice bridge back > into the flat > GRanges > world from the Listy world of IRanges. > > > Well I'm just remembering now that we still don't have > an "order" > method for GRanges objects like we do for IRanges > object. The "natural" > order for a GRanges object would be to order first by > (a) seqlevel, > (b) then by strand, (c) then by start, (d) and finally > by end. > > We already do (c) and (d) for IRanges. > > Also the "reduce" method for GRanges already uses this > "natural" > order implicitly. > > So we will add this "order" method and the other basic > order-related > methods (sort, >=, <=, ==, !=, unique, duplicated, they > are all > missing) for GRanges objects. That will cover Janet's > use case and > other user cases (I think someone asked how to find > duplicated in > a GRanges object a while ago on this list). > > > An "order" method would be great. Note though that the > coercion to > RangesList only sorts by seqlevels, so we would need > something that gave > out that order vector. The point here is to avoid forcing > the user to > modify the order of the original GRanges. > > > With order() the user will still be able to achieve this with > something > like: > > regionMeans <- function(regions, cvg) > { > seqlevels(regions) <- names(cvg) > oo <- order(regions) > regions <- regions[oo] > ans <- unlist( > viewMeans( > Views(cvg, as(regions, "RangesList")) > ), use.names=FALSE) > ans[oo] <- ans # restore original order > ans > } > > values(myRegions)$meanCov <- regionMeans(myRegions, myRleList) > > Tricky :-/ > > But maybe we could provide a "mean" method that accepts 2 args: > a GRanges and an RleList/IntegerList/__NumericList. Same with > min, max, sum etc... they would all return a numeric vector of the > same length as their first argument (the GRanges object). > > > Patrick and I had talked about this. We were calling it the > rangeMeans function. It would go along with rangeMins, rangeMaxs, > etc. Basically a way to perform a windowed computation without > having to construct an intermediate Views. This is a lot easier to > design and implement, as you were saying below. > > There are a lot of other (non-Rle) Vectors that would benefit from > window-based summaries. These include the basic vector types, as > well as ranges themselves. For example, I and others have > encountered a need to find the nearest neighbors of a set of ranges, > with the constraint that they fall within the same gene or exon or > whatever. One could come up with a GRanges with the seqnames > corresponding to a gene, but that's a hack and the current looping > implementation of nearest,GRanges is a bit slow. > > Michael > > Another approach could be that we make the Views() constructor more > flexible so Views(myRleList, myRegions) would just work and return > a Views object (not a ViewsList) but that means redefining what a > Views object can be (@subject can now be a named List and @ranges > a GRanges object). Maybe a cleaner design would be to use a new > container for this e.g. GViews (for Genomic Views)... > > H. > > > > Michael > > Cheers, > H. > > > > Michael > > > > ### getting close to a solution - but I'd > have liked to > have been able to > unlist this and directly add to my GRanges > object e.g. > values(myRegions)$meanCov<- > unlist(viewMeans(Views( > myRleList, > as(myRegions,"RangesList") ) ), use.names=FALSE) > ### but the regions are in a different order > to how I > started, so the > above command would associate the wrong > scores with the > wrong regions. > > sessionInfo() > > R version 2.13.1 (2011-07-08) > Platform: i386-apple-darwin9.8.0/i386 (32-bit) > > locale: > [1] > en_US.UTF-8/en_US.UTF-8/C/C/____en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] stats graphics grDevices utils > datasets > methods base > > other attached packages: > [1] GenomicRanges_1.4.6 IRanges_1.10.5 > > > ___________________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org <mailto:bioconductor at="" r-project.org=""> > <mailto:bioconductor at="" r-__project.org=""> <mailto:bioconductor at="" r-project.org="">> > > https://stat.ethz.ch/mailman/____listinfo/bioconductor > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> > > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> <https: stat.ethz.ch="" mailman="" listinfo="" bioconductor="">> > Search the archives: > http://news.gmane.org/gmane.____science.biology.informat ics.____conductor > <http: news.gmane.org="" gmane.__science.biology.informati="" cs.__conductor=""> > > <http: news.gmane.org="" gmane.__science.biology.informatics.__conductor="" <http:="" news.gmane.org="" gmane.science.biology.informatics.conductor="">> > > > > > [[alternative HTML version deleted]] > > > ___________________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > <mailto:bioconductor at="" r-project.org=""> > <mailto:bioconductor at="" r-__project.org=""> <mailto:bioconductor at="" r-project.org="">> > > https://stat.ethz.ch/mailman/____listinfo/bioconductor > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> > > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> <https: stat.ethz.ch="" mailman="" listinfo="" bioconductor="">> > Search the archives: > http://news.gmane.org/gmane.____science.biology.informat ics.____conductor > <http: news.gmane.org="" gmane.__science.biology.informati="" cs.__conductor=""> > > <http: news.gmane.org="" gmane.__science.biology.informatics.__conductor="" <http:="" news.gmane.org="" gmane.science.biology.informatics.conductor="">> > > > > -- > 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 fhcrc.org <mailto:hpages at="" fhcrc.org=""> > <mailto:hpages at="" fhcrc.org="" <mailto:hpages="" at="" fhcrc.org="">> > Phone: (206) 667-5791 <tel:%28206%29%20667-5791> > <tel:%28206%29%20667-5791> > Fax: (206) 667-1319 <tel:%28206%29%20667-1319> > <tel:%28206%29%20667-1319> > > > > > -- > 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 fhcrc.org <mailto:hpages at="" fhcrc.org=""> > Phone: (206) 667-5791 <tel:%28206%29%20667-5791> > Fax: (206) 667-1319 <tel:%28206%29%20667-1319> > > > -- 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 fhcrc.org Phone: (206) 667-5791 Fax: (206) 667-1319
0
4.1 years ago by
Hervé Pagès ♦♦ 13k
United States
Hervé Pagès ♦♦ 13k wrote:
Hi Michael, On 03/31/2014 05:22 AM, Michael Lawrence wrote: > Raising this one from the dead: this would be a very nice thing to have. > It's tough explaining to users how to bridge GRanges and RangesList for > aggregating vectors (RleLists) by GRanges. We could start with > rangeSums, rangeMeans, rangeMaxs, etc, that directly summarize and > RleList, and then maybe move towards a GenomicViews object. Maybe > something for the next release cycle? Yeah I'd like to start working on something like this. Note that, kind of related to this, I added subsetting a named List by a GRanges subscript recently. But what we really need is a simple container that bundles a GRanges object with a named List subject, I think. H. > > Michael > > > On Wed, Aug 17, 2011 at 5:55 AM, Michael Lawrence <michafla at="" gene.com=""> <mailto:michafla at="" gene.com="">> wrote: > > > > 2011/8/16 Hervé Pagès <hpages at="" fhcrc.org="" <mailto:hpages="" at="" fhcrc.org="">> > > Michael, > > On 11-08-16 10:52 AM, Michael Lawrence wrote: > > > > 2011/8/16 Hervé Pagès <hpages at="" fhcrc.org=""> <mailto:hpages at="" fhcrc.org=""> <mailto:hpages at="" fhcrc.org=""> <mailto:hpages at="" fhcrc.org="">>> > > > Hi Michael, > > > On 11-08-15 10:28 PM, Michael Lawrence wrote: > > On Mon, Aug 15, 2011 at 10:10 PM, Michael > Lawrence<michafla at="" gene.com=""> <mailto:michafla at="" gene.com=""> <mailto:michafla at="" gene.com=""> <mailto:michafla at="" gene.com="">>>____wrote: > > > > > On Mon, Aug 15, 2011 at 5:25 PM, Janet > Young<jayoung at="" fhcrc.org=""> <mailto:jayoung at="" fhcrc.org=""> <mailto:jayoung at="" fhcrc.org=""> <mailto:jayoung at="" fhcrc.org="">>> wrote: > > > Hi again, > > I have another question, in the "I think I > need a > convoluted workaround, > but maybe I'm missing a simple solution" > genre (seems > like most of my > questions are like that). > > I have an RleList object representing > mapability for the > whole human > genome. I'd like to: > (a) calculate viewMeans for various regions > of interest > that I've been > representing as GRanges, and > (b) I'd like the underlying code to be smart > and match > chromosome names up > in the RleList and the GRanges object (not > rely on > chromosomes being ordered > the same in the two objects), and > (c) I'd like to return the viewMeans results > in the same > order as the > objects in my original GRanges > > I don't think this is currently implemented > without > doing several > coercions (that introduce their own > complications) but > I'm not sure. Some > code is below that shows what I'm trying to > do. It > seems like it might be > a common kind of way to use viewMeans - I > imagine people > are gradually > switching to use GRanges more and more? but > really I > don't know. > > My real world question is that I've read in > mapability > from a UCSC bigWig > file, and made that into an RleList. I have > a bunch of > other regions I've > read in (again from UCSC) using rtracklayer > as GRanges, > and have annotated > those with various things I'm interested in > (e.g. number > of RNAseq reads in > the region). I want to add the average > mapability for > each region, so that > I can later look at how mapability affects > those other > things I'm > annotating. > > Should I be being more strict with myself > about how my > GRanges are ordered > and making sure they all have the same > seqlevels and > seqlengths? Perhaps > that would help the coercion workarounds go > more smoothly. > > thanks, > > Janet > > > library(GenomicRanges) > > ### make an RleList. Just for this example, > I starting > with a GRanges > object and used coverage to get an RleList > example. To > get my real data I > read in a UCSC bigWig file. > > fakeRegions<- > GRanges(seqnames=c("chrA","____chrA", > "chrB", "chrC"), > ranges=IRanges(start=c(1,51,1,____1), > end=c(60,90,20,10) ) ) > seqlengths(fakeRegions)<- c(100,100,100) > myRleList<- coverage(fakeRegions) > rm(fakeRegions) > > myRleList > > # SimpleRleList of length 3 > # $chrA > # 'integer' Rle of length 100 with 4 runs > # Lengths: 50 10 30 10 > # Values : 1 2 1 0 > # > #$chrB > # 'integer' Rle of length 100 with 2 runs > # Lengths: 20 80 > # Values : 1 0 > # > # $chrC > # 'integer' Rle of length 100 with 2 runs > # Lengths: 10 90 > # Values : 1 0 > > ### make some regions of interest > myRegions<- GRanges(seqnames=c("chrB", > "chrC", "chrB"), > ranges=IRanges(start=c(1,1,5), > end=c(20,20,10) ), > geneNames=c("g1","g2","g3") ) > myRegions > # GRanges with 3 ranges and 1 > elementMetadata value > # seqnames ranges strand | geneNames > #<rle> <iranges> <rle> |<character> > # [1] chrB [1, 20] * | g1 > # [2] chrC [1, 20] * | g2 > # [3] chrB [5, 10] * | g3 > # > # seqlengths > # chrB chrC > # NA NA > > ## can't use GRanges directly > Views( myRleList, myRegions) > # Error in RleViewsList(rleList = subject, > rangesList = > start) : > # 'rangesList' must be a RangesList object > > ## can't use a simple coercion > Views( myRleList, as(myRegions,"RangesList") ) > # Error in .Method(..., FUN = FUN, MoreArgs > = MoreArgs, > SIMPLIFY = > SIMPLIFY, : > # all object lengths must be multiple of > longest > object length > > ### although I can use that coercion if I > first give the > GRanges object > the same levels as the RleList to force the > lists to > have the same names as > each other: > seqlevels(myRegions)<- names(myRleList) > viewMeans(Views( myRleList, > as(myRegions,"RangesList") ) ) > # SimpleNumericList of length 3 > # [["chrA"]] numeric(0) > # [["chrB"]] 1 1 > # [["chrC"]] 0.5 > > > These two lines seem pretty concise to me. It > makes sense to > layer a > RangesList on top of an RleList. Storing the > result would of > course be > easier if you had sorted the GRanges by the > seqlevels. > Having a utility for > that would be nice (orderBySeqlevels?). It would > also be > easy with > RangedData, which enforces ordering by space. > > > Just to clarify, here is how the function would work: > > > values(myRegions)$meanCov[____orderBySeqlevels(myRegions)]<- > unlist(viewMeans(Views( myRleList, > as(myRegions,"RangesList") ) ), > use.names=FALSE) > > So 'orderBySeqlevels' would be a nice bridge back > into the flat > GRanges > world from the Listy world of IRanges. > > > Well I'm just remembering now that we still don't have > an "order" > method for GRanges objects like we do for IRanges > object. The "natural" > order for a GRanges object would be to order first by > (a) seqlevel, > (b) then by strand, (c) then by start, (d) and finally > by end. > > We already do (c) and (d) for IRanges. > > Also the "reduce" method for GRanges already uses this > "natural" > order implicitly. > > So we will add this "order" method and the other basic > order-related > methods (sort, >=, <=, ==, !=, unique, duplicated, they > are all > missing) for GRanges objects. That will cover Janet's > use case and > other user cases (I think someone asked how to find > duplicated in > a GRanges object a while ago on this list). > > > An "order" method would be great. Note though that the > coercion to > RangesList only sorts by seqlevels, so we would need > something that gave > out that order vector. The point here is to avoid forcing > the user to > modify the order of the original GRanges. > > > With order() the user will still be able to achieve this with > something > like: > > regionMeans <- function(regions, cvg) > { > seqlevels(regions) <- names(cvg) > oo <- order(regions) > regions <- regions[oo] > ans <- unlist( > viewMeans( > Views(cvg, as(regions, "RangesList")) > ), use.names=FALSE) > ans[oo] <- ans # restore original order > ans > } > > values(myRegions)$meanCov <- regionMeans(myRegions, myRleList) > > Tricky :-/ > > But maybe we could provide a "mean" method that accepts 2 args: > a GRanges and an RleList/IntegerList/__NumericList. Same with > min, max, sum etc... they would all return a numeric vector of the > same length as their first argument (the GRanges object). > > > Patrick and I had talked about this. We were calling it the > rangeMeans function. It would go along with rangeMins, rangeMaxs, > etc. Basically a way to perform a windowed computation without > having to construct an intermediate Views. This is a lot easier to > design and implement, as you were saying below. > > There are a lot of other (non-Rle) Vectors that would benefit from > window-based summaries. These include the basic vector types, as > well as ranges themselves. For example, I and others have > encountered a need to find the nearest neighbors of a set of ranges, > with the constraint that they fall within the same gene or exon or > whatever. One could come up with a GRanges with the seqnames > corresponding to a gene, but that's a hack and the current looping > implementation of nearest,GRanges is a bit slow. > > Michael > > Another approach could be that we make the Views() constructor more > flexible so Views(myRleList, myRegions) would just work and return > a Views object (not a ViewsList) but that means redefining what a > Views object can be (@subject can now be a named List and @ranges > a GRanges object). Maybe a cleaner design would be to use a new > container for this e.g. GViews (for Genomic Views)... > > H. > > > > Michael > > Cheers, > H. > > > > Michael > > > > ### getting close to a solution - but I'd > have liked to > have been able to > unlist this and directly add to my GRanges > object e.g. > values(myRegions)$meanCov<- > unlist(viewMeans(Views( > myRleList, > as(myRegions,"RangesList") ) ), use.names=FALSE) > ### but the regions are in a different order > to how I > started, so the > above command would associate the wrong > scores with the > wrong regions. > > sessionInfo() > > R version 2.13.1 (2011-07-08) > Platform: i386-apple-darwin9.8.0/i386 (32-bit) > > locale: > [1] > en_US.UTF-8/en_US.UTF-8/C/C/____en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] stats graphics grDevices utils > datasets > methods base > > other attached packages: > [1] GenomicRanges_1.4.6 IRanges_1.10.5 > > > ___________________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org <mailto:bioconductor at="" r-project.org=""> > <mailto:bioconductor at="" r-__project.org=""> <mailto:bioconductor at="" r-project.org="">> > > https://stat.ethz.ch/mailman/____listinfo/bioconductor > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> > > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> <https: stat.ethz.ch="" mailman="" listinfo="" bioconductor="">> > Search the archives: > http://news.gmane.org/gmane.____science.biology.informat ics.____conductor > <http: news.gmane.org="" gmane.__science.biology.informati="" cs.__conductor=""> > > <http: news.gmane.org="" gmane.__science.biology.informatics.__conductor="" <http:="" news.gmane.org="" gmane.science.biology.informatics.conductor="">> > > > > > [[alternative HTML version deleted]] > > > ___________________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > <mailto:bioconductor at="" r-project.org=""> > <mailto:bioconductor at="" r-__project.org=""> <mailto:bioconductor at="" r-project.org="">> > > https://stat.ethz.ch/mailman/____listinfo/bioconductor > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> > > <https: stat.ethz.ch="" mailman="" __listinfo="" bioconductor=""> <https: stat.ethz.ch="" mailman="" listinfo="" bioconductor="">> > Search the archives: > http://news.gmane.org/gmane.____science.biology.informat ics.____conductor > <http: news.gmane.org="" gmane.__science.biology.informati="" cs.__conductor=""> > > <http: news.gmane.org="" gmane.__science.biology.informatics.__conductor="" <http:="" news.gmane.org="" gmane.science.biology.informatics.conductor="">> > > > > -- > 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 fhcrc.org <mailto:hpages at="" fhcrc.org=""> > <mailto:hpages at="" fhcrc.org="" <mailto:hpages="" at="" fhcrc.org="">> > Phone: (206) 667-5791 <tel:%28206%29%20667-5791> > <tel:%28206%29%20667-5791> > Fax: (206) 667-1319 <tel:%28206%29%20667-1319> > <tel:%28206%29%20667-1319> > > > > > -- > 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 fhcrc.org <mailto:hpages at="" fhcrc.org=""> > Phone: (206) 667-5791 <tel:%28206%29%20667-5791> > Fax: (206) 667-1319 <tel:%28206%29%20667-1319> > > > -- 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 fhcrc.org Phone: (206) 667-5791 Fax: (206) 667-1319