# Multivariate tests

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 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8
    ##  [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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ##  [1] GSEABase_1.73.0          graph_1.89.1             annotate_1.89.0
    ##  [4] XML_3.99-0.22            AnnotationDbi_1.73.0     IRanges_2.45.0
    ##  [7] S4Vectors_0.49.0         Biobase_2.71.0           BiocGenerics_0.57.0
    ## [10] generics_0.1.4           zenith_1.13.0            tximportData_1.39.0
    ## [13] tximport_1.39.1          readr_2.2.0              edgeR_4.9.4
    ## [16] pander_0.6.6             variancePartition_1.41.5 BiocParallel_1.45.0
    ## [19] limma_3.67.0             ggplot2_4.0.2            knitr_1.51
    ## [22] rmarkdown_2.30
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          sys_3.4.3                   jsonlite_2.0.0
    ##   [4] magrittr_2.0.4              farver_2.1.2                nloptr_2.2.1
    ##   [7] vctrs_0.7.1                 memoise_2.0.1               minqa_1.2.8
    ##  [10] RCurl_1.98-1.17             progress_1.2.3              htmltools_0.5.9
    ##  [13] S4Arrays_1.11.1             curl_7.0.0                  broom_1.0.12
    ##  [16] SparseArray_1.11.11         sass_0.4.10                 KernSmooth_2.23-26
    ##  [19] bslib_0.10.0                pbkrtest_0.5.5              plyr_1.8.9
    ##  [22] cachem_1.1.0                buildtools_1.0.0            lifecycle_1.0.5
    ##  [25] iterators_1.0.14            pkgconfig_2.0.3             Matrix_1.7-4
    ##  [28] R6_2.6.1                    fastmap_1.2.0               rbibutils_2.4.1
    ##  [31] MatrixGenerics_1.23.0       digest_0.6.39               numDeriv_2016.8-1.1
    ##  [34] GenomicRanges_1.63.1        RSQLite_2.4.6               labeling_0.4.3
    ##  [37] httr_1.4.8                  abind_1.4-8                 compiler_4.5.2
    ##  [40] bit64_4.6.0-1               aod_1.3.3                   withr_3.0.2
    ##  [43] S7_0.2.1                    backports_1.5.0             DBI_1.3.0
    ##  [46] gplots_3.3.0                MASS_7.3-65                 DelayedArray_0.37.0
    ##  [49] corpcor_1.6.10              gtools_3.9.5                caTools_1.18.3
    ##  [52] tools_4.5.2                 msigdbr_26.1.0              remaCor_0.0.20
    ##  [55] glue_1.8.0                  nlme_3.1-168                grid_4.5.2
    ##  [58] reshape2_1.4.5              snow_0.4-4                  gtable_0.3.6
    ##  [61] tzdb_0.5.0                  tidyr_1.3.2                 hms_1.1.4
    ##  [64] XVector_0.51.0              pillar_1.11.1               stringr_1.6.0
    ##  [67] babelgene_22.9              vroom_1.7.0                 splines_4.5.2
    ##  [70] dplyr_1.2.0                 lattice_0.22-9              bit_4.6.0
    ##  [73] tidyselect_1.2.1            locfit_1.5-9.12             maketools_1.3.2
    ##  [76] Biostrings_2.79.5           reformulas_0.4.4            Seqinfo_1.1.0
    ##  [79] SummarizedExperiment_1.41.1 RhpcBLASctl_0.23-42         xfun_0.56
    ##  [82] statmod_1.5.1               matrixStats_1.5.0           KEGGgraph_1.71.0
    ##  [85] stringi_1.8.7               yaml_2.3.12                 boot_1.3-32
    ##  [88] evaluate_1.0.5              codetools_0.2-20            tibble_3.3.1
    ##  [91] Rgraphviz_2.55.0            cli_3.6.5                   RcppParallel_5.1.11-2
    ##  [94] xtable_1.8-8                Rdpack_2.6.6                jquerylib_0.1.4
    ##  [97] Rcpp_1.1.1                  zigg_0.0.2                  EnvStats_3.1.0
    ## [100] png_0.1-9                   Rfast_2.1.5.2               parallel_4.5.2
    ## [103] assertthat_0.2.1            blob_1.3.0                  prettyunits_1.2.0
    ## [106] bitops_1.0-9                lme4_2.0-1                  mvtnorm_1.3-6
    ## [109] lmerTest_3.2-1              scales_1.4.0                purrr_1.2.1
    ## [112] crayon_1.5.3                fANCOVA_0.6-1               rlang_1.1.7
    ## [115] EnrichmentBrowser_2.41.0    KEGGREST_1.51.1

<>

# References