Results from the univariate regressions performed using can be combined in a post-processing step to perform multivariate hypothesis testing. In this example, we fit on transcript-level counts and then perform multivariate hypothesis testing by combining transcripts at the gene-level. This is done with the function.
Read in transcript counts from the package.
library(readr)
library(tximport)
library(tximportData)
# specify directory
path = system.file("extdata", package="tximportData")
# read sample meta-data
samples = read.table(file.path(path,"samples.txt"), header=TRUE)
samples.ext = read.table(file.path(path,"samples_extended.txt"), header=TRUE, sep="\t")
# read assignment of transcripts to genes
# remove genes on the PAR, since these are present twice
tx2gene = read_csv(file.path(path, "tx2gene.gencode.v27.csv"))
tx2gene = tx2gene[grep("PAR_Y", tx2gene$GENEID, invert=TRUE),]
# read transcript-level quatifictions
files = file.path(path, "salmon", samples$run, "quant.sf.gz")
txi = tximport(files, type = "salmon", txOut=TRUE)
# Create metadata simulating two conditions
sampleTable = data.frame(condition = factor(rep(c("A", "B"), each = 3)))
rownames(sampleTable) = paste0("Sample", 1:6)Perform standard analysis at the transcript-level
library(variancePartition)
library(edgeR)
# Prepare transcript-level reads
dge = DGEList(txi$counts)
design <- model.matrix(~condition, data = sampleTable)
isexpr = filterByExpr(dge, design)
dge = dge[isexpr,]
dge = calcNormFactors(dge)
# Estimate precision weights
vobj = voomWithDreamWeights(dge, ~ condition, sampleTable)
# Fit regression model one transcript at a time
fit = dream(vobj, ~ condition, sampleTable)
fit = eBayes(fit)Combine the transcript-level results at the gene-level. The mapping between transcript and gene is stored in as a list.
# Prepare transcript to gene mapping
# keep only transcripts present in vobj
# then convert to list with key GENEID and values TXNAMEs
keep = tx2gene$TXNAME %in% rownames(vobj)
tx2gene.lst = unstack(tx2gene[keep,])
# Run multivariate test on entries in each feature set
res = mvTest(fit, vobj, tx2gene.lst, coef="conditionB")
# truncate gene names since they have version numbers
# ENST00000498289.5 -> ENST00000498289
res$ID.short = gsub("\\..+", "", res$ID)Perform gene set analysis using on the gene-level test statistics.
# must have zenith > v1.0.2
library(zenith)
library(GSEABase)
gs = get_MSigDB("C1", to="ENSEMBL")
df_gsa = zenithPR_gsa( res$stat, res$ID.short, gs, inter.gene.cor=.05)
head(df_gsa)## NGenes Correlation delta se p.less p.greater PValue
## M652_chr5q35 81 0.05 -9558.4102 1689.015 7.737973e-09 1.0000000 1.547595e-08
## M12945_chr12q21 35 0.05 -1506.2074 1897.386 2.136523e-01 0.7863477 4.273046e-01
## M5824_chr11p13 30 0.05 -988.0642 1952.248 3.063911e-01 0.6936089 6.127822e-01
## M1556_chr20q11 92 0.05 775.2733 1678.101 6.779541e-01 0.3220459 6.440917e-01
## M13160_chr16p11 105 0.05 -519.6880 1660.274 3.771373e-01 0.6228627 7.542747e-01
## M6742_chr2q36 20 0.05 -575.4590 2133.016 3.936640e-01 0.6063360 7.873280e-01
## Direction FDR
## M652_chr5q35 Down 3.838035e-06
## M12945_chr12q21 Down 9.970286e-01
## M5824_chr11p13 Down 9.970286e-01
## M1556_chr20q11 Up 9.970286e-01
## M13160_chr16p11 Down 9.970286e-01
## M6742_chr2q36 Down 9.970286e-01## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
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## attached base packages:
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## other attached packages:
## [1] org.Hs.eg.db_3.17.0 msigdbr_7.5.1 GSEABase_1.62.0
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