nsdiffToFGSEA {NanoTube} | R Documentation |
Use the fgsea library to run gene set enrichment analysis from the NanoStringDiff analysis results. Genes will be ranked by their log2 fold changes.
nsdiffToFGSEA(deResults, gene.sets, sourceDB = NULL, min.set = 1)
deResults |
Result from NanoStringDiff::glm.LRT. |
gene.sets |
Gene set file name, in .rds (list), .gmt, or .tab format; or a list object containing the gene sets. Gene names must be in the same form as in the ranked.list. |
sourceDB |
Source database to include, only if using a .tab-format geneset.file from CPDB. |
min.set |
Number of genes required to conduct analysis on a given gene set (default = 1). If fewer than this number of genes from limmaResults are included in a gene set, that gene set will be skipped for this analysis. |
A list containing data frames with the fgsea results.
example_data <- system.file("extdata", "GSE117751_RAW", package = "NanoTube") sample_data <- system.file("extdata", "GSE117751_sample_data.csv", package = "NanoTube") datNoNorm <- processNanostringData(nsFiles = example_data, sampleTab = sample_data, groupCol = "Sample_Diagnosis", normalization = "none") # Convert to NanoString Set, retaining 2 samples per group for this example # (will run faster, but still pretty slow) nsDiffSet <- makeNanoStringSetFromEset(datNoNorm[,c(1,2,15,16,29,30)]) # Run NanoStringDiff analysis nsDiffSet <- NanoStringDiff::estNormalizationFactors(nsDiffSet) result <- NanoStringDiff::glm.LRT(nsDiffSet, design.full = as.matrix(pData(nsDiffSet)), contrast = c(1, -1, 0)) #contrast: Autoimmune retinopathy vs. None # FGSEA with example pathways, only for pathways with at least 5 genes # analyzed in NanoString experiment data("ExamplePathways") fgseaResult <- nsdiffToFGSEA(result, gene.sets = ExamplePathways, min.set = 5)