Extract table of enrichment results from 'K2' object

Source: R/getEnrichmentTable.R

Create table hyper- and single-sample enrichment results from 'K2' object.

getEnrichmentTable(K2res)

Arguments

K2res

An object of class K2 or K2results().

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_hyper: The p-value of the hyperenrichment test comparing the subgroup-assigned gene set to the user-specified gene set

  • fdr_hyper: The multiple hypothesis corrected FDR (Benjamini-Hochberg) p-value of hyperenrichment, 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

  • B: The B-statistic estimated by the `limma` R package

References

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. 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.

Examples

## 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)

## Run GSVA on genesets
K2res <- runGSVAmods(K2res,
                    ssGSEAalg='gsva',
                    ssGSEAcores=1,
                    verbose=FALSE)

## Run differential analysis on GSVA results
K2res <- runDSSEmods(K2res)

head(getEnrichmentTable(K2res))

#>   category node edge direction   pval_hyper    fdr_hyper nhits ndrawn ncats
#> 1      GS1    E    2        up 8.289318e-40 2.984154e-38    17     26    50
#> 2      GS1    A    2        up 1.388507e-36 2.499313e-35    15     20    50
#> 3      GS2    A    1        up 2.616509e-28 3.139810e-27    12     18    50
#> 4      GS2    E    2        up 5.386442e-18 4.847798e-17     9     26    50
#> 5      GS2    C    2        up 8.187727e-14 5.895164e-13     6     11    50
#> 6      GS1    A    1        up 3.246781e-12 1.948068e-11     6     18    50
#>    ntot   pval_limma    fdr_limma      coef          mean        t         B
#> 1 20000 0.0167604129 0.2011249549 0.4158121  0.0981111115 2.550020 -2.855639
#> 2 20000 0.0000102174 0.0002452175 0.3421021  0.0009537613 4.969924  3.240044
#> 3 20000 0.1016447486 0.3484962808 0.1183511  0.0112973465 1.671177 -5.225981
#> 4 20000 0.0255329069 0.2042632552 0.3854695 -0.0930926814 2.363940 -3.274470
#> 5 20000 0.0493914221 0.2370788260 0.2036941 -0.0387742566 2.028366 -3.769405
#> 6 20000           NA           NA        NA            NA       NA        NA
#>                                                                                                                                                                                         hits
#> 1 31397_at,31589_at,AFFX-YEL021w/URA3_at,AFFX-BioC-3_st,31564_at,31425_g_at,AFFX-MurIL2_at,31502_at,31507_at,31726_at,31556_at,31574_i_at,31466_at,31571_at,31554_at,AFFX-PheX-M_at,31379_at
#> 2                                                   31583_at,31546_at,31573_at,31511_at,31568_at,31527_at,31545_at,31708_at,31330_at,31492_at,31509_at,31385_at,31538_at,31697_s_at,31722_at
#> 3                                                               AFFX-TrpnX-5_at,31694_at,31471_at,31618_at,AFFX-MurIL4_at,31537_at,31345_at,31465_g_at,31335_at,31428_at,31566_at,31419_r_at
#> 4                                                                                AFFX-HUMGAPDH/M33197_3_at,31417_at,31404_at,31586_f_at,31478_at,31364_i_at,AFFX-CreX-5_at,31406_at,31569_at
#> 5                                                                                                   31579_at,31441_at,AFFX-YEL024w/RIP1_at,AFFX-BioB-5_at,AFFX-HUMGAPDH/M33197_3_st,31560_at
#> 6                                                                                                                                  31396_r_at,31649_at,31580_at,31535_i_at,31461_at,31529_at