plot,gsaresults-method: GSA plots
In bernau/survHDExtra: Addtitional Features for package survHD

Description Usage Arguments Details Value Author(s) References See Also

Plots for the visualization of GSA results.

## S4 method for signature 'gsaresults,missing'
plot(x, type="overlap", geneset.id1,
geneset.id2, show.es=TRUE, title=NULL, ...)
## S4 method for signature 'gsagenesets,missing'
plot(x, type="overlap", ... )

`x`	An object of class `gsaresults` or `gsagenesets`.
`type`	The type of plot. Can be "overlap" for an overlap plot, and "barcode" for an GSEA-like barcode plot (the latter is only available for `gsaresults`). Defaults to "overlap", which shows the similarity of (significant) gene sets.
`geneset.id1`	For "barcode", the id of the geneset in `x` to be visualized.
`geneset.id2`	For "barcode", optional second id, useful when `geneset.id1` are up-regulated genes and `geneset.id2` down-regulated genes.
`show.es`	For "barcode", show the running Kolmogorov-Smirnov statistic.
`title`	For "barcode", print this title.
`...`	Further graphical options passed to `levelplot` for "overlap" and `barcodeplot` for "barcode".

Provides two kinds of plots for GSA results. The default is a heatmap that visualizes the overlap of significant gene sets. The second "barcode" plot is very similar to the GSEA (Subramanian 2005) plots. The first panel of the plot shows an enrichment score, which is the running deviation from the expected uniformly distributed ranking of genes. More precisely, if m is the gene set size and n the total number of genes in the ranking, the enrichment score at rank i is increased by 1-m/n if gene i is in the gene set, otherwise it is decreased by m/n. The enrichment score is scaled, so that +1 and -1 correspond to 95 percent confidence intervals. The second plot shows the barcode plot from the limma package, in which the bars visualize the position of the genes. It is possible to visualize two gene sets in one plot, which makes sense for gene sets that are divided in up- and down-regulated genes. This makes it possible to not only check for non-randomness, but also for consistency of the expression direction.

No return.

Markus Riester markus@jimmy.harvard.edu, Levi Waldron lwaldron@hsph.harvard.edu, Christoph Bernau bernau@ibe.med.uni-muenchen.de

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA, 102, 15545-15550.

gsaresults, gsagenesets

bernau/survHDExtra documentation built on May 12, 2019, 4:22 p.m.