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Extract table of enrichment results from 'K2' object

Source: R/getEnrichmentTable.R
getEnrichmentTable.Rd

Create table fisher- and gene set scoring enrichment results from 'K2' object.

Usage

getEnrichmentTable(K2res)

Arguments

K2res

A K2 class object.

Value

A data.frame object with the following columns:

  • category: The user-specified gene set names

  • node: The identifier of the partition

  • edge: Indication of which subgroup the gene was assigned at a given partition

  • direction: The direction of coefficient for the assigned gene set

  • pval_fisher: The p-value of the enrichment test comparing the subgroup-assigned gene set to the user-specified gene set

  • fdr_fisher: The multiple hypothesis corrected FDR (Benjamini-Hochberg) p-value of enrichment, adjusted across all partitions

  • nhits: The intersection of subgroup-assigned genes and the user-specified gene set

  • ndrawn: The number of subgroup-assigned genes

  • ncats: The number of genes in the user-specified gene set

  • ntot: The background population of possible genes

  • pval_limma: The p-value estimated by the `limma` R package

  • fdr_limma: The multiple hypothesis corrected FDR (Benjamini-Hochberg) p-value of differential analysis, adjusted across all partitions

  • coef: The difference between the means of each subgroup at a given partition

  • mean: The mean across all observations at the given partition

  • t: The test statistic estimated by the `limma` R package

  • hits: A comma deliminated string of intersection gene identifers

References

Reed ER, Monti S (2021). “Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data.” Nucleic Acids Research. doi:10.1093/nar/gkab552 , https://pubmed.ncbi.nlm.nih.gov/34226941/. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47--e47. ISSN 1362-4962, 0305-1048, doi:10.1093/nar/gkv007 , http://academic.oup.com/nar/article/43/7/e47/2414268/limma-powers-differential-expression-analyses-for. 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. Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14(1), 7. ISSN 1471-2105, doi:10.1186/1471-2105-14-7 , http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7.