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
## M7078_chr2p16      30        0.05  1.450647 0.5610791 0.99513309 0.004866912 0.009733824        Up
## M14982_chr7p13     26        0.05  1.133549 0.5777005 0.97512013 0.024879873 0.049759746        Up
## M7314_chr4p14      25        0.05 -1.134410 0.5825608 0.02575932 0.974240679 0.051518643      Down
## M5824_chr11p13     30        0.05 -1.012037 0.5612285 0.03568377 0.964316230 0.071367539      Down
## M3783_chr2q37      72        0.05  0.879737 0.4936487 0.96262484 0.037375160 0.074750321        Up
## M22119_chrXq24     25        0.05 -1.007362 0.5825934 0.04190548 0.958094523 0.083810954      Down
##                      FDR        Geneset     coef
## M7078_chr2p16  0.9994959  M7078_chr2p16 zenithPR
## M14982_chr7p13 0.9994959 M14982_chr7p13 zenithPR
## M7314_chr4p14  0.9994959  M7314_chr4p14 zenithPR
## M5824_chr11p13 0.9994959 M5824_chr11p13 zenithPR
## M3783_chr2q37  0.9994959  M3783_chr2q37 zenithPR
## M22119_chrXq24 0.9994959 M22119_chrXq24 zenithPR

Session info

## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=en_US.UTF-8     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] org.Hs.eg.db_3.19.1      msigdbr_7.5.1            GSEABase_1.66.0         
##  [4] graph_1.82.0             annotate_1.82.0          XML_3.99-0.16.1         
##  [7] AnnotationDbi_1.66.0     IRanges_2.38.0           S4Vectors_0.42.0        
## [10] Biobase_2.64.0           BiocGenerics_0.50.0      zenith_1.6.0            
## [13] tximportData_1.31.0      tximport_1.32.0          readr_2.1.5             
## [16] edgeR_4.2.0              pander_0.6.5             variancePartition_1.34.0
## [19] BiocParallel_1.38.0      limma_3.60.0             ggplot2_3.5.1           
## [22] knitr_1.46              
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.8              magrittr_2.0.3              farver_2.1.1               
##   [4] nloptr_2.0.3                rmarkdown_2.26              zlibbioc_1.50.0            
##   [7] vctrs_0.6.5                 memoise_2.0.1               minqa_1.2.6                
##  [10] RCurl_1.98-1.14             progress_1.2.3              S4Arrays_1.4.0             
##  [13] htmltools_0.5.8.1           curl_5.2.1                  broom_1.0.5                
##  [16] SparseArray_1.4.0           sass_0.4.9                  KernSmooth_2.23-22         
##  [19] bslib_0.7.0                 pbkrtest_0.5.2              plyr_1.8.9                 
##  [22] cachem_1.0.8                lifecycle_1.0.4             iterators_1.0.14           
##  [25] pkgconfig_2.0.3             Matrix_1.7-0                R6_2.5.1                   
##  [28] fastmap_1.1.1               MatrixGenerics_1.16.0       GenomeInfoDbData_1.2.12    
##  [31] rbibutils_2.2.16            digest_0.6.35               numDeriv_2016.8-1.1        
##  [34] colorspace_2.1-0            GenomicRanges_1.56.0        RSQLite_2.3.6              
##  [37] filelock_1.0.3              labeling_0.4.3              RcppZiggurat_0.1.6         
##  [40] fansi_1.0.6                 abind_1.4-5                 httr_1.4.7                 
##  [43] compiler_4.4.0              bit64_4.0.5                 aod_1.3.3                  
##  [46] withr_3.0.0                 backports_1.4.1             DBI_1.2.2                  
##  [49] highr_0.10                  gplots_3.1.3.1              MASS_7.3-60.2              
##  [52] DelayedArray_0.30.0         corpcor_1.6.10              gtools_3.9.5               
##  [55] caTools_1.18.2              tools_4.4.0                 remaCor_0.0.18             
##  [58] glue_1.7.0                  nlme_3.1-164                grid_4.4.0                 
##  [61] reshape2_1.4.4              generics_0.1.3              snow_0.4-4                 
##  [64] gtable_0.3.5                tzdb_0.4.0                  tidyr_1.3.1                
##  [67] hms_1.1.3                   utf8_1.2.4                  XVector_0.44.0             
##  [70] pillar_1.9.0                stringr_1.5.1               babelgene_22.9             
##  [73] vroom_1.6.5                 splines_4.4.0               dplyr_1.1.4                
##  [76] BiocFileCache_2.12.0        lattice_0.22-6              bit_4.0.5                  
##  [79] tidyselect_1.2.1            locfit_1.5-9.9              Biostrings_2.72.0          
##  [82] SummarizedExperiment_1.34.0 RhpcBLASctl_0.23-42         xfun_0.43                  
##  [85] statmod_1.5.0               matrixStats_1.3.0           KEGGgraph_1.64.0           
##  [88] stringi_1.8.3               UCSC.utils_1.0.0            yaml_2.3.8                 
##  [91] boot_1.3-30                 evaluate_0.23               codetools_0.2-20           
##  [94] archive_1.1.8               tibble_3.2.1                Rgraphviz_2.48.0           
##  [97] cli_3.6.2                   RcppParallel_5.1.7          xtable_1.8-4               
## [100] Rdpack_2.6                  munsell_0.5.1               jquerylib_0.1.4            
## [103] Rcpp_1.0.12                 GenomeInfoDb_1.40.0         EnvStats_2.8.1             
## [106] dbplyr_2.5.0                png_0.1-8                   Rfast_2.1.0                
## [109] parallel_4.4.0              blob_1.2.4                  prettyunits_1.2.0          
## [112] bitops_1.0-7                lme4_1.1-35.3               mvtnorm_1.2-4              
## [115] lmerTest_3.1-3              scales_1.3.0                purrr_1.0.2                
## [118] crayon_1.5.2                fANCOVA_0.6-1               rlang_1.1.3                
## [121] EnrichmentBrowser_2.34.0    KEGGREST_1.44.0

<>

References