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
# Default method is "FE.empirical", but use "FE" here to reduce runtime
res <- mvTest(fit, vobj, tx2gene.lst, coef = "conditionB", method = "FE")
# 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 Direction
## M7078_chr2p16 30 0.05 1.4208384 0.5610910 0.99432899 0.005671015 0.01134203 Up
## M14982_chr7p13 26 0.05 1.1335492 0.5777005 0.97512013 0.024879873 0.04975975 Up
## M7314_chr4p14 25 0.05 -1.1344103 0.5825608 0.02575932 0.974240679 0.05151864 Down
## M5824_chr11p13 30 0.05 -1.0120371 0.5612285 0.03568377 0.964316230 0.07136754 Down
## M3783_chr2q37 73 0.05 0.8367603 0.4929617 0.95518099 0.044819012 0.08963802 Up
## M10517_chr4q24 21 0.05 -1.0062435 0.6060832 0.04844305 0.951556955 0.09688609 Down
## FDR
## M7078_chr2p16 0.9992274
## M14982_chr7p13 0.9992274
## M7314_chr4p14 0.9992274
## M5824_chr11p13 0.9992274
## M3783_chr2q37 0.9992274
## M10517_chr4q24 0.9992274## R version 4.3.2 Patched (2023-11-13 r85521)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
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## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
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## other attached packages:
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## [4] graph_1.80.0 annotate_1.80.0 XML_3.99-0.16.1
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