title: "gQTLstats: computationally efficient analysis and interpretation of large eQTL, mQTL, etc. archives" author: "Vincent J. Carey, stvjc at channing.harvard.edu" date: "Jun 2015" output: BiocStyle::html_document: highlight: pygments number_sections: yes theme: united toc: yes BiocStyle::pdf_document: toc: yes number_sections: yes
BiocStyle::markdown()
The software in this package aims to support refinements and functional interpretation of members of a collection of association statistics on a family of feature $\times$ genome hypotheses. provide a basis for refinement or functional interpretation.
We take for granted the use of the gQTL* infrastructure
for testing and management of test results. We
use for examples elements of the r Biocexptpkg("geuvPack")
and
r Biocexptpkg("geuvStore2")
packages.
We work with a ciseStore
instance based on a small subset
of transcriptome-wide cis-eQTL tests for GEUVADIS FPKM data.
The overall testing procedure was conducted for all SNP:probe
pairs for which SNP minor allele frequency (MAF) is
at least 1\% and for which the minimum distance between SNP and
either boundary of the gene coding region
for the probe is at most 1 million bp.
suppressPackageStartupMessages({ library(SummarizedExperiment) library(Homo.sapiens) library(org.Hs.eg.db) library(geuvStore2) library(gQTLBase) library(gQTLstats) }) ```r library(geuvStore2) library(gQTLBase) library(gQTLstats) library(parallel) nco = detectCores() library(doParallel) registerDoSEQ() if (.Platform$OS.type != "windows") { registerDoParallel(cores=max(c(1, floor(nco/2)))) } prst = makeGeuvStore2()
Quantile estimation is very memory-efficient, based on a temporary ff representation of the vector of all association test results.
qassoc = storeToQuantiles(prst, field="chisq", probs=c(seq(0,.999,.001), 1-(c(1e-4,1e-5,1e-6)))) tail(qassoc)
Because we compute fixed breaks, contributions to the overall histogram can be assembled in parallel, with small footprint. This is a tremendous reduction of data.
hh = storeToHist( prst, breaks= c(0,qassoc,1e9) ) tail(hh$counts)
FDR computation is post-hoc relative to filtering
that need not be specified prior to testing. For
illustration, we survey the results in r Biocexptpkg("geuvStore2")
to obtain FDRs for each SNP:probe
pair in two forms. First, we obtain FDR without
any filtering. Second, we compute an
a FDR for those SNP:probe pairs separated
by at most 500kb, and for which the MAF for the SNP
is at least 5 per cent.
rawFDR = storeToFDR(prst, xprobs=c(seq(.05,.95,.05),.975,.990,.995,.9975,.999, .9995, .9999, .99999) ) ```r dmfilt = function(x) # define the filtering function x[ which(x$MAF >= 0.05 & x$mindist <= 500000) ] ```r filtFDR = storeToFDR(prst, xprobs=c(seq(.05,.95,.05),.975,.990,.995,.9975,.999, .9995, .9999, .99999), filter = dmfilt )
rawFDR filtFDR
The filtering leads to a lower FDR for a given strength of association. This is an inspiration for sensitivity analysis. Even with 5 million observations there is an effect of histogram bin selection in summarizing the permutation distribution of association. This can be seen fairly clearly in the wiggliness of the trace over the unfiltered association score:FDR plot.
rawtab = getTab(rawFDR) filttab = getTab(filtFDR) plot(rawtab[-(1:10),"assoc"], -log10(rawtab[-(1:10),"fdr"]+1e-6), log="x", axes=FALSE, xlab="Observed association", ylab="-log10 plugin FDR") axis(1, at=c(seq(0,10,1),100,200)) axis(2) points(filttab[-(1:10),1], -log10(filttab[-(1:10),2]+1e-6), pch=2) legend(1, 5, pch=c(1,2), legend=c("all loci", "MAF >= 0.05 & dist <= 500k"))
We'll address this below by fitting smooth functions for the score:FDR relationship.
The storeToFDRByProbe
FDR function examines the maximal association score
by gene, for observed and permuted measures.
Good performance of this procedure is obtained by using
group_by
and summarize
utilities of r CRANpkg("dplyr")
.
Iteration employs r CRANpkg("foreach")
.
fdbp = storeToFDRByProbe( prst, xprobs=c(seq(.025,.975,.025),.99)) tail(getTab(fdbp),5)
fdAtM05bp = storeToFDRByProbe( prst, filter=function(x) x[which(x$MAF > .05)], xprobs=c(seq(.025,.975,.025),.99)) tail(getTab(fdAtM05bp),5)
We'll focus here on all-pairs analysis, with and without filtering.
Especially in this small example there will be some wiggling or
even non-monotonicity in the trace of empirical
FDR against association. We want to be able to compute
the approximate FDR quickly and with minimal assumptions
and pathology. To accomplish this, we will
bind an interpolating model to the
FDR estimates that we have.
Interpolation will be accomplished with scatterplot smoothing in
the r CRANpkg("mgcv")
framework.
The code that is used to fit the interpolating model is
fdrmod = gam(-log10(fdr+fudge)~s(assoc,bs="tp"), data=..., subset=assoc<(1.1*maxch))
where fudge defaults to 1e-6 and maxch defaults to 30
library(mgcv) rawFDR = setFDRfunc(rawFDR) filtFDR = setFDRfunc(filtFDR) par(mfrow=c(2,2)) txsPlot(rawFDR) txsPlot(filtFDR) directPlot(rawFDR) directPlot(filtFDR)
More work is needed on assessing tolerability of relative error in FDR interpolation.
Recall that dmfilt
is a function that obtains the
SNP-probe pairs for which SNP has MAF at least five percent
and SNP-probe distance at most 500kbp.
We use the FDRsupp
instances with ciseStore
to list the SNP-probe pairs with FDR lying
beneath a given upper bound.
Unfiltered pairs:
rawEnum = enumerateByFDR(prst, rawFDR, threshold=.05) rawEnum[order(rawEnum$chisq,decreasing=TRUE)[1:3]] length(rawEnum)
A small quantity of metadata is bound into the resulting
GRanges
instance.
names(metadata(rawEnum))
Pairs meeting MAF and distance conditions are obtained with
a filter
setting to the enumerating function.
filtEnum = enumerateByFDR(prst, filtFDR, threshold=.05, filter=dmfilt) filtEnum[order(filtEnum$chisq,decreasing=TRUE)[1:3]] length(filtEnum)
The yield of an enumeration procedure depends on filtering based on SNP-gene distance and SNP MAF. This can be illustrated as follows, with minimal computational effort owing to the retention of genome-scale permutations and the use of the plug-in FDR algorithm.
data(sensByProbe) # see example(senstab) for construction approach tab = senstab( sensByProbe ) plot(tab)
If we wish to maximize the yield of eQTL enumeration at FDR at most 0.05, we can apply a filter to the store.
flens = storeApply( prst, function(x) { length(x[ which(x$MAF >= .08 & x$mindist <= 25000), ] ) }) ```r sum(unlist(flens))
This is a count of gene-snp pairs satisfying structural and genetic criteria.
In the case of geuFPKM
there is some relevant metadata
in the rowRanges
element. We will bind that into the
collection of significant findings.
library(geuvPack) data(geuFPKM) basic = mcols(rowRanges(geuFPKM))[, c("gene_id", "gene_status", "gene_type", "gene_name")] rownames(basic) = basic$gene_id extr = basic[ filtEnum$probeid, ] mcols(filtEnum) = cbind(mcols(filtEnum), extr) stopifnot(all.equal(filtEnum$probeid, filtEnum$gene_id)) filtEnum[1:3]
We have a utility to create an annotated Manhattan plot for a search cis to a gene. The basic ingredients are
ciseStore
instance for basic location and association informationFDRsupp
instance that includes the function that maps from association scores to FDR, and the filter employed during FDR estimationhmm878
GRanges instance in gQTLstats/data.It is important to recognize that, given
an FDRsupp
instance we can compute the FDR for any
association score, but validity of the
FDR attribution requires that we refrain
from computing it for any locus excluded by filtering.
the manhWngr
executes the FDRsupp
-resident filter
by default.
data(hmm878) library(geuvStore2) prst = makeGeuvStore2() myg = "ENSG00000183814.10" # LIN9 data(filtFDR) library(ggplot2) manhWngr( store = prst, probeid = myg, sym="LIN9", fdrsupp=filtFDR, namedGR=hmm878 )
For a dynamic visualization procedure, see the vjcitn/gQTLbrowse github archive.
We can use r Biocpkg("VariantAnnotation")
to establish
basic structural characteristics for all filtered variants.
This code is blocked owing to changes to seqlevelsStyle that
need to be reconciled at a deeper level. Please notify stvjc
at channing.harvard.edu if there is a need to run this code.
suppressPackageStartupMessages({ library(VariantAnnotation) library(TxDb.Hsapiens.UCSC.hg19.knownGene) }) txdb = TxDb.Hsapiens.UCSC.hg19.knownGene seqlevelsStyle(filtEnum) = "UCSC" #seqinfo(filtEnum) = seqinfo(txdb) seqlengths(filtEnum)[paste0("chr", c(1:22,"M"))] = seqlengths(txdb)[paste0("chr", c(1:22,"M"))] filtEnum = keepStandardChromosomes(filtEnum) suppressWarnings({ allv = locateVariants(filtEnum, txdb, AllVariants()) # multiple recs per eQTL }) table(allv$LOCATION) hits = findOverlaps( filtEnum, allv ) filtEex = filtEnum[ queryHits(hits) ] mcols(filtEex) = cbind(mcols(filtEex), mcols(allv[subjectHits(hits)])[,1:7]) filtEex[1:3]
The resulting table is SNP:transcript specific, and will likely need further processing.
The following tasks need to be addressed in the modeling of phenorelevance
We will make a temporary reconstruction of geuvStore2 contents with the enhanced information.
The workhorse function is AllAssoc. The interface is
args(AllAssoc)
This differs from cisAssoc through the addition of a
variantRange
argument.
The basic operation will be as follows. For a given
RangedSummarizedExperiment instance summex
, all features will
be tested for association with all SNP in the variantRange
restriction of the VCF identified in vcf.tf
. The basic
iteration strategy is
a) tile the genome to obtain chunks of SNPs
b) decompose the SE into chunks of transcriptome (or other 'ome)
c) for each chunk of SNPs, for each chunk of transcriptome, seek associations and retain the top K in a buffering structure
Management of this buffering structure needs work.
require(GenomeInfoDb) require(geuvPack) require(Rsamtools) data(geuFPKM) # get a ranged summarized expt lgeu = geuFPKM[ which(seqnames(geuFPKM)=="chr20"), ] # limit to chr20 seqlevelsStyle(lgeu) = "NCBI" tf20 = TabixFile(system.file("vcf/c20exch.vcf.gz", package="gQTLstats")) if (require(VariantAnnotation)) scanVcfHeader(tf20) set.seed(1234) mysr = GRanges("20", IRanges(33.099e6, 33.52e6)) lita = AllAssoc(geuFPKM[1:10,], tf20, mysr) names(mcols(lita))
The trans search for this segment of chr20 proceeds by obtaining additional association scores for additional genes.
litb = AllAssoc(geuFPKM[11:20,], tf20, mysr) litc = AllAssoc(geuFPKM[21:30,], tf20, mysr)
Now we want to reduce this information by collecting the strongest associations over the 30 genes tested.
buf = gQTLstats:::collapseToBuf(lita, litb, frag="_obs") buf buf = gQTLstats:::collapseToBuf(buf, litc, frag="_obs") buf
Let's do the same buffering process for the first permutation.
pbuf = gQTLstats:::collapseToBuf(lita, litb, frag="_permScore_1") pbuf = gQTLstats:::collapseToBuf(pbuf, litc, frag="_permScore_1") pbuf
We can compare the distributions of maximal association per SNP as observed or under permutation.
plot(density(buf$scorebuf[,1])) lines(density(pbuf$scorebuf[,1]), lty=2)
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