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
## M14982_chr7p13 25 0.05 7.906128 2.082903 0.999926118 7.388155e-05 0.0001477631
## M5824_chr11p13 30 0.05 -6.028272 2.007216 0.001337443 9.986626e-01 0.0026748867
## M7314_chr4p14 25 0.05 -5.077180 2.084132 0.007428521 9.925715e-01 0.0148570424
## M3783_chr2q37 71 0.05 3.749809 1.768579 0.982999226 1.700077e-02 0.0340015489
## M6742_chr2q36 20 0.05 -4.344151 2.194043 0.023861861 9.761381e-01 0.0477237227
## M14543_chr18q22 17 0.05 4.325680 2.286441 0.970737601 2.926240e-02 0.0585247982
## Direction FDR Geneset coef
## M14982_chr7p13 Up 0.03664525 M14982_chr7p13 zenithPR
## M5824_chr11p13 Down 0.33168596 M5824_chr11p13 zenithPR
## M7314_chr4p14 Down 0.99873228 M7314_chr4p14 zenithPR
## M3783_chr2q37 Up 0.99873228 M3783_chr2q37 zenithPR
## M6742_chr2q36 Down 0.99873228 M6742_chr2q36 zenithPR
## M14543_chr18q22 Up 0.99873228 M14543_chr18q22 zenithPR## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] org.Hs.eg.db_3.20.0 msigdbr_7.5.1 GSEABase_1.69.0
## [4] graph_1.85.0 annotate_1.85.0 XML_3.99-0.17
## [7] AnnotationDbi_1.69.0 IRanges_2.41.0 S4Vectors_0.45.1
## [10] Biobase_2.67.0 BiocGenerics_0.53.2 generics_0.1.3
## [13] zenith_1.9.0 tximportData_1.34.0 tximport_1.35.0
## [16] readr_2.1.5 edgeR_4.5.0 pander_0.6.5
## [19] variancePartition_1.37.1 BiocParallel_1.41.0 limma_3.63.2
## [22] ggplot2_3.5.1 knitr_1.49 rmarkdown_2.29
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## loaded via a namespace (and not attached):
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## [4] farver_2.1.2 nloptr_2.1.1 zlibbioc_1.52.0
## [7] vctrs_0.6.5 memoise_2.0.1 minqa_1.2.8
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## [13] htmltools_0.5.8.1 curl_6.0.0 broom_1.0.7
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## [25] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.7-1
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