Adds hyperenrichment analysis results to the output of runDGEmods().
runGSEmods( K2res, genesets = NULL, qthresh = NULL, cthresh = NULL, ntotal = NULL )
| K2res | An object of class K2. The output of runDGEmods(). |
|---|---|
| genesets | A named list of feature IDs |
| qthresh | A numeric value between 0 and 1 of the FDR cuttoff to define feature sets. |
| cthresh | A positive value for the coefficient cuttoff to define |
| ntotal | The total number of genes sampled from. feature sets. |
An object of class K2.
Reed ER, Monti S (2020). “Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data.” Bioinformatics. doi: 10.1101/2020.11.05.370197 , http://biorxiv.org/lookup/doi/10.1101/2020.11.05.370197. Benjamini Y, Hochberg Y (1995). “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289--300. ISSN 00359246, doi: 10.1111/j.2517-6161.1995.tb02031.x , http://doi.wiley.com/10.1111/j.2517-6161.1995.tb02031.x.
## Read in ExpressionSet object library(Biobase) data(sample.ExpressionSet) ## Pre-process and create K2 object K2res <- K2preproc(sample.ExpressionSet) ## Run K2 Taxonomer algorithm K2res <- K2tax(K2res, stabThresh=0.5) ## Run differential analysis on each partition K2res <- runDGEmods(K2res) ## Create dummy set of gene sets DGEtable <- getDGETable(K2res) genes <- unique(DGEtable$gene) genesetsMadeUp <- list( GS1=genes[1:50], GS2=genes[51:100], GS3=genes[101:150]) ## Run gene set hyperenrichment K2res <- runGSEmods(K2res, genesets=genesetsMadeUp, qthresh=0.1)