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.

Import transcript-level counts

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)

Standard dream analysis

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)

Multivariate analysis

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)

Gene set analysis

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
## chr7p13      28        0.05  7.144240 2.034359 0.999776828 0.0002231723 0.0004463445        Up
## chr11p13     32        0.05 -5.752953 1.982804 0.001859931 0.9981400686 0.0037198628      Down
## chr4p14      25        0.05 -5.077180 2.084132 0.007428521 0.9925714788 0.0148570424      Down
## chr2q37      75        0.05  3.571510 1.758652 0.978855126 0.0211448742 0.0422897483        Up
## chr2q36      21        0.05 -4.130558 2.168488 0.028411437 0.9715885626 0.0568228749      Down
## chr18q22     18        0.05  4.108195 2.252712 0.965889229 0.0341107714 0.0682215427        Up
##                FDR  Geneset     coef
## chr7p13  0.1129252  chr7p13 zenithPR
## chr11p13 0.4705626 chr11p13 zenithPR
## chr4p14  0.9996791  chr4p14 zenithPR
## chr2q37  0.9996791  chr2q37 zenithPR
## chr2q36  0.9996791  chr2q36 zenithPR
## chr18q22 0.9996791 chr18q22 zenithPR

Session info

## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB             
##  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] GSEABase_1.70.0          graph_1.86.0             annotate_1.86.1         
##  [4] XML_3.99-0.18            AnnotationDbi_1.70.0     IRanges_2.42.0          
##  [7] S4Vectors_0.46.0         Biobase_2.68.0           BiocGenerics_0.54.0     
## [10] generics_0.1.4           zenith_1.10.0            tximportData_1.36.0     
## [13] tximport_1.36.1          readr_2.1.5              edgeR_4.6.3             
## [16] pander_0.6.6             variancePartition_1.38.1 BiocParallel_1.42.1     
## [19] limma_3.64.3             ggplot2_3.5.2            knitr_1.50              
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3          jsonlite_2.0.0              magrittr_2.0.3             
##   [4] farver_2.1.2                nloptr_2.2.1                rmarkdown_2.29             
##   [7] vctrs_0.6.5                 memoise_2.0.1               minqa_1.2.8                
##  [10] RCurl_1.98-1.17             progress_1.2.3              htmltools_0.5.8.1          
##  [13] S4Arrays_1.8.1              curl_6.4.0                  broom_1.0.9                
##  [16] SparseArray_1.8.1           sass_0.4.10                 KernSmooth_2.23-26         
##  [19] bslib_0.9.0                 pbkrtest_0.5.5              plyr_1.8.9                 
##  [22] cachem_1.1.0                lifecycle_1.0.4             iterators_1.0.14           
##  [25] pkgconfig_2.0.3             Matrix_1.7-3                R6_2.6.1                   
##  [28] fastmap_1.2.0               GenomeInfoDbData_1.2.14     rbibutils_2.3              
##  [31] MatrixGenerics_1.20.0       digest_0.6.37               numDeriv_2016.8-1.1        
##  [34] GenomicRanges_1.60.0        RSQLite_2.4.2               labeling_0.4.3             
##  [37] abind_1.4-8                 httr_1.4.7                  compiler_4.5.1             
##  [40] bit64_4.6.0-1               aod_1.3.3                   withr_3.0.2                
##  [43] backports_1.5.0             DBI_1.2.3                   gplots_3.2.0               
##  [46] MASS_7.3-65                 DelayedArray_0.34.1         corpcor_1.6.10             
##  [49] gtools_3.9.5                caTools_1.18.3              tools_4.5.1                
##  [52] msigdbr_25.1.1              remaCor_0.0.18              glue_1.8.0                 
##  [55] nlme_3.1-168                grid_4.5.1                  reshape2_1.4.4             
##  [58] snow_0.4-4                  gtable_0.3.6                tzdb_0.5.0                 
##  [61] tidyr_1.3.1                 hms_1.1.3                   XVector_0.48.0             
##  [64] pillar_1.11.0               stringr_1.5.1               babelgene_22.9             
##  [67] vroom_1.6.5                 splines_4.5.1               dplyr_1.1.4                
##  [70] lattice_0.22-7              bit_4.6.0                   tidyselect_1.2.1           
##  [73] locfit_1.5-9.12             Biostrings_2.76.0           reformulas_0.4.1           
##  [76] SummarizedExperiment_1.38.1 RhpcBLASctl_0.23-42         xfun_0.52                  
##  [79] statmod_1.5.0               matrixStats_1.5.0           KEGGgraph_1.68.0           
##  [82] stringi_1.8.7               UCSC.utils_1.4.0            yaml_2.3.10                
##  [85] boot_1.3-31                 evaluate_1.0.4              codetools_0.2-20           
##  [88] archive_1.1.12              tibble_3.3.0                Rgraphviz_2.52.0           
##  [91] cli_3.6.5                   RcppParallel_5.1.10         xtable_1.8-4               
##  [94] Rdpack_2.6.4                jquerylib_0.1.4             dichromat_2.0-0.1          
##  [97] Rcpp_1.1.0                  GenomeInfoDb_1.44.1         zigg_0.0.2                 
## [100] EnvStats_3.1.0              png_0.1-8                   Rfast_2.1.5.1              
## [103] parallel_4.5.1              assertthat_0.2.1            blob_1.2.4                 
## [106] prettyunits_1.2.0           bitops_1.0-9                lme4_1.1-37                
## [109] mvtnorm_1.3-3               lmerTest_3.1-3              scales_1.4.0               
## [112] purrr_1.1.0                 crayon_1.5.3                fANCOVA_0.6-1              
## [115] rlang_1.1.6                 EnrichmentBrowser_2.38.0    KEGGREST_1.48.1

<>

References