The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices; filter, cluster, invert, and decompose sets; perform pre-ranked Correlation Adjusted MEan RAnk (CAMERA-PR) gene set testing via a flexible matrix method (see limma for the original implementation); and create bubble heatmaps to visualize the results of any differential or molecular signatures analysis.
TMSig 1.0.0
Report issues at https://github.com/EMSL-Computing/TMSig
License: GPL (>= 3)
GMT files, such as those available through the Molecular Signatures Database
(MSigDB)(Liberzon et al. 2011, 2015), can be read as
named lists with readGMT:
# Path to GMT file - MSigDB Gene Ontology sets
pathToGMT <- system.file(
"extdata", "c5.go.v2023.2.Hs.symbols.gmt.gz",
package = "TMSig"
)
geneSets <- readGMT(path = pathToGMT)
length(geneSets) # 10461 gene sets
#> [1] 10461
head(names(geneSets)) # first 6 gene set names
#> [1] "GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS"
#> [2] "GOBP_2FE_2S_CLUSTER_ASSEMBLY"
#> [3] "GOBP_2_OXOGLUTARATE_METABOLIC_PROCESS"
#> [4] "GOBP_3_PHOSPHOADENOSINE_5_PHOSPHOSULFATE_METABOLIC_PROCESS"
#> [5] "GOBP_3_UTR_MEDIATED_MRNA_DESTABILIZATION"
#> [6] "GOBP_3_UTR_MEDIATED_MRNA_STABILIZATION"
geneSets[[1]] # genes in first set
#> [1] "AASDHPPT" "ALDH1L1" "ALDH1L2" "MTHFD1" "MTHFD1L" "MTHFD2L"filterSets keeps sets that satisfy minimum and maximum size constraints.
Optionally, sets can be restricted to a smaller background of genes before
filtering by size.
# Filter by size - no background
filt <- filterSets(x = geneSets,
min_size = 10L,
max_size = 500L)
length(filt) # 7096 gene sets remain (down from 10461)
#> [1] 7096Normally, the background is the set of genes quantified in a particular experiment. For the purposes of this example, the top 2000 most common genes will be selected to serve as the background. This ensures the gene sets will maintain some degree of overlap for later steps.
# Top 2000 most common genes
topGenes <- table(unlist(geneSets))
topGenes <- head(sort(topGenes, decreasing = TRUE), 2000L)
head(topGenes)
#>
#> TNF AKT1 WNT5A CTNNB1 TGFB1 NOTCH1
#> 809 675 669 655 609 588
background <- names(topGenes)The gene sets will be restricted to elements of the background before filtering by size.
# Restrict genes sets to background of genes
geneSetsFilt <- filterSets(
x = geneSets,
background = background,
min_size = 10L # min. overlap of each set with background
)
length(geneSetsFilt) # 4629 gene sets pass
#> [1] 4629The proportion of genes in each set that overlap with the background will be calculated and used to further select sets.
# Calculate proportion of overlap with background
setSizes <- lengths(geneSetsFilt)
setSizesOld <- lengths(geneSets)[names(geneSetsFilt)]
overlapProp <- setSizes / setSizesOld
plot(setSizesOld, overlapProp, main = "Set Size vs. Overlap")