The main concern of the parglms package is supporting efficient computations for modeling with dispersed data. The motivating use case is an eQTL analysis as exemplified in geuvStore2. A BatchJobs Registry mediates access to collections of millions of statistics on SNP-transcript association. We want to use statistical modeling to perform inference on features of SNPs contributing to various phenotypic conditions through effects on gene expression.
Formally, let y denote an
N vector with mean function
μ(β) and variance
function V(μ). The
parameter vector of interest, β, is of dimension p, satisfying g(μ) = Xβ,
where X is N × p. In conventional use
of generalized linear models (GLMs), functions μ and V have simple forms and are
programmed in the family elements for
stats::glm. The models are fit by solving equations of the
form DtW[y − μ(β)] = 0
where W is diagonal with
elements prescribed by the reciprocal variance function, and D = ∂μ/∂β.
The key motivation for this package is the recognition that steps towards the solution of the equation can often be arithmetically decomposed in various ways, and neither holistic nor serial computation is required for most of the calculations. We can therefore accomplish the model fitting task in a scalable way, decomposing the data into parts that fit comfortably in memory, and performing operations in parallel among the parts whenever possible.
We use BatchJobs to disperse data into small chunks.
## Loading required package: BBmisc##
## Attaching package: 'BBmisc'## The following object is masked from 'package:base':
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## isFALSE## The development of BatchJobs and BatchExperiments is discontinued.## Consider switching to 'batchtools' for new features and improved stability## Sourced 1 configuration files:## 1: /github/workspace/pkglib/BatchJobs/etc/BatchJobs_global_config.R## BatchJobs configuration:
## cluster functions: Interactive
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## default.resources: list()
## debug: FALSE
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## staged.queries: TRUE
## max.concurrent.jobs: Inf
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## measure.mem: TRUE## Creating dir: /tmp/RtmpeX6QRb/file19b14ad9429a## Saving registry: /tmp/RtmpeX6QRb/file19b14ad9429a/registry.RDatachs = chunk(1:nrow(anorexia), n.chunks=18) # 4 recs/chunk
f = function(x) anorexia[x,]
options(BBmisc.ProgressBar.style="off")
batchMap(myr, f, chs)## Adding 18 jobs to DB.## Status for 18 jobs at 2024-12-21 05:57:40.33284
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## Time: min=NAs avg=NAs max=NAsWe are now poised to rewrite the data.frame contents into chunks.
## Syncing registry ...## Treat Prewt Postwt
## 1 Cont 80.7 80.2
## 2 Cont 89.4 80.1
## 3 Cont 91.8 86.4
## 4 Cont 74.0 86.3The parGLM method will fit the model specified in the
formula. The task of iterating over chunks is left to the BiocParallel
bplapply, and this will implicitly use whatever concurrent
computing approach has been registered.
library(parglms)
pp = parGLM( Postwt ~ Treat + Prewt, myr,
family=gaussian, binit = c(0,0,0,0), maxit=10, tol=.001 )## Warning: executing %dopar% sequentially: no parallel backend registered## [1] "coefficients" "eff.variance" "robust.variance" "s2"
## [5] "niter" "converged" "formula" "N"
## [9] "theCall"## [,1]
## (Intercept) 49.7711090
## TreatCont -4.0970655
## TreatFT 4.5630627
## Prewt 0.4344612In this application we model the probability that a SNP has been identified as a GWAS hit, as a function of aspects of its genomic context and its association with expression as measured using RNA-seq in GEUVADIS.
In the decorate function, we emend the outputs of gQTLstats
cisAssoc after applying storeToFDR and enumerateByFDR, as
serialized in a GRanges (demoEnum) with information on GWAS hit status
and enclosing chromatin state. This is intensive; the
litdec function simply computes GWAS hit status and
chromatin state.
litdec = function(grWithFDR) {
tmp = grWithFDR
library(gQTLstats)
if (!exists("hmm878")) data(hmm878)
seqlevelsStyle(hmm878) = "NCBI"
library(GenomicRanges)
ov = findOverlaps(tmp, hmm878)
states = hmm878$name
states[ which(states %in% c("13_Heterochrom/lo", "14_Repetitive/CNV",
"15_Repetitive/CNV")) ] = "hetrep_1315"
levs = unique(states)
tmp$hmmState = factor(rep("hetrep_1315", length(tmp)),levels=levs)
tmp$hmmState = relevel(tmp$hmmState, "hetrep_1315")
tmp$hmmState[ queryHits(ov) ] = factor(states[ subjectHits(ov) ],
levels=levs)
if (!exists("gwrngs19")) data(gwrngs19)
library(GenomeInfoDb)
seqlevelsStyle(gwrngs19) = "NCBI"
tmp$isGwasHit = 1*(tmp %in% gwrngs19)
tmp
}
decorate = function(grWithFDR) {
#
# the data need a distance/MAF filter
#
library(gQTLstats)
data(filtFDR)
if (!exists("hmm878")) data(hmm878)
library(gwascat)
if (!exists("gwrngs19")) data(gwrngs19)
if (!exists("gwastagger")) data(gwastagger) # will use locations here
library(GenomeInfoDb)
seqlevelsStyle(hmm878) = "NCBI"
seqlevelsStyle(gwrngs19) = "NCBI"
seqlevelsStyle(gwastagger) = "NCBI"
tmp = grWithFDR
tmp$isGwasHit = 1*(tmp %in% gwrngs19)
tmp$isGwasTagger = 1*(tmp %in% gwastagger)
#levs = unique(hmm878$name)
library(GenomicRanges)
ov = findOverlaps(tmp, hmm878)
states = hmm878$name
states[ which(states %in% c("13_Heterochrom/lo", "14_Repetitive/CNV",
"15_Repetitive/CNV")) ] = "hetrep_1315"
levs = unique(states)
tmp$hmmState = factor(rep("hetrep_1315", length(tmp)),levels=levs)
tmp$hmmState = relevel(tmp$hmmState, "hetrep_1315")
tmp$hmmState[ queryHits(ov) ] = factor(states[ subjectHits(ov) ],
levels=levs)
tmp$estFDR = getFDRfunc(filtFDR)( tmp$chisq )
tmp$fdrcat = cut(tmp$estFDR, c(-.01, .01, .05, .1, .25, .5, 1.01))
tmp$fdrcat = relevel(tmp$fdrcat, "(0.5,1.01]")
#tmp$distcat = cut(tmp$mindist, c(-1,0,1000,5000,10000,50000,100000,250000,500001))
tmp$distcat = cut(tmp$mindist, c(-1,0,1000,5000,10000,25000,50001))
#tmp$distcat = relevel(tmp$distcat, "(2.5e+05,5e+05]")
tmp$distcat = relevel(tmp$distcat, "(2.5e+04,5e+04]")
tmp$MAFcat = cut(tmp$MAF, c(.049, .075, .1, .25, .51))
tmp$MAFcat = relevel(tmp$MAFcat, "(0.25,0.51]")
kp = c("seqnames", "start", "probeid", "snp", "estFDR", "fdrcat", "hmmState",
"distcat", "MAFcat", "isGwasHit", "isGwasTagger")
names(tmp) = NULL
as(tmp, "data.frame")[,kp]
}We’ll try this out here:
suppressPackageStartupMessages({
library(geuvStore2)
library(gQTLBase)
library(gQTLstats)
})
prst = g17transRegistry()Now we can fit a very simple model for SNP phenorelevance. We set the extractor component of the registry to the litdec function defined above.