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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  1.4208384 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

Session info

## R version 4.3.0 (2023-04-21)
## Platform: x86_64-apple-darwin22.4.0 (64-bit)
## Running under: macOS 14.2.1
## 
## Matrix products: default
## BLAS:   /Users/gabrielhoffman/prog/R-4.3.0/lib/libRblas.dylib 
## LAPACK: /usr/local/Cellar/r/4.3.0_1/lib/R/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] org.Hs.eg.db_3.17.0       msigdbr_7.5.1             GSEABase_1.62.0          
##  [4] graph_1.78.0              annotate_1.78.0           XML_3.99-0.14            
##  [7] AnnotationDbi_1.62.1      IRanges_2.34.1            S4Vectors_0.38.1         
## [10] Biobase_2.60.0            BiocGenerics_0.46.0       zenith_1.4.1             
## [13] edgeR_3.42.4              variancePartition_1.33.11 BiocParallel_1.34.2      
## [16] limma_3.56.2              ggplot2_3.4.4             tximportData_1.28.0      
## [19] tximport_1.28.0           readr_2.1.4              
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.5              magrittr_2.0.3              nloptr_2.0.3               
##   [4] rmarkdown_2.22              fs_1.6.2                    zlibbioc_1.46.0            
##   [7] ragg_1.2.5                  vctrs_0.6.3                 memoise_2.0.1              
##  [10] minqa_1.2.5                 RCurl_1.98-1.12             progress_1.2.2             
##  [13] htmltools_0.5.5             S4Arrays_1.2.0              curl_5.0.0                 
##  [16] broom_1.0.5                 sass_0.4.6                  KernSmooth_2.23-21         
##  [19] bslib_0.4.2                 desc_1.4.2                  pbkrtest_0.5.2             
##  [22] plyr_1.8.8                  cachem_1.0.8                lifecycle_1.0.3            
##  [25] iterators_1.0.14            pkgconfig_2.0.3             Matrix_1.5-4.1             
##  [28] R6_2.5.1                    fastmap_1.1.1               GenomeInfoDbData_1.2.10    
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##  [52] MASS_7.3-60                 DelayedArray_0.26.3         corpcor_1.6.10             
##  [55] gtools_3.9.4                caTools_1.18.2              tools_4.3.0                
##  [58] remaCor_0.0.17              glue_1.6.2                  nlme_3.1-162               
##  [61] grid_4.3.0                  reshape2_1.4.4              generics_0.1.3             
##  [64] gtable_0.3.3                tzdb_0.4.0                  tidyr_1.3.0                
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## [103] EnvStats_2.7.0              dbplyr_2.3.2                png_0.1-8                  
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## [112] lme4_1.1-34                 mvtnorm_1.2-2               lmerTest_3.1-3             
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## [121] KEGGREST_1.40.0

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References