CMSgsa: CMS Gene Set Analysis

In peterawe/CMScaller: CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models

Description

camera gene set analysis (GSA) against 15 pre-selected CMS-informative gene sets (geneSets.CMS).

Usage

CMSgsa(emat, class, RNAseq = FALSE, ...)

Arguments

emat	a numeric expression matrix with sample columns, gene rows and Entrez rownames. Microarray data should be normalized and log2-transformed. For RNA-seq data, raw counts or RSEM values could be used directly by setting RNAseq=TRUE.
class	a factor vector specifying sample classes.
RNAseq	a logical, set to TRUE if emat is untransformed, non-normalized sequencing counts or RSEM values.
...	additional arguments passed to subCamera.

Details

See subCamera for output details.

Value

a heatmap and camera output (list, invisible). In heatmap, red and blue indicates relative up- and down-regulation respectively. Color saturation reflects significance. Nominal 'camera' p-values are used as input for visualization.

References

Guinney J, Dienstmann R, Wang X, de Reynies A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350-6.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 2015;gkv007.

Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology. 2014;15:R29.

Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012;gks461.

See Also

camera, subCamera, geneSets.CMS

Examples

cam <- CMSgsa(emat=crcTCGAsubset, class=crcTCGAsubset$CMS, RNAseq=TRUE)
lapply(cam, head)

peterawe/CMScaller documentation built on June 13, 2020, 4:49 a.m.