Pathway Analysis is the process of statistically linking observations on the molecular level to biological processes or pathways on the systems (organism, organ, tissue, cell) level. Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. This can lead to identification of spurious (misleading) pathways as statistically significant, since components are often shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene-pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse data sets and contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories as well as mechanisms for extracting GPS for user supplied repositories.
|Author||Amir B.K. Foroushani, Fiona S.L. Brinkman, David J. Lynn|
|Date of publication||2016-05-11 00:26:37|
|Maintainer||Amir Foroushani <firstname.lastname@example.org>|
genesFromRandomPathways: Function to randomly select genes associated with randomly...
getGenes: List genes involved in present GPS for a specific pathway in...
idmap: Identifier mappings for protein coding genes.
kegH: Pathway GPS data, extracted from KEGG repository (Human).
kegM: Pathway GPS data, extracted from KEGG repository (Mouse).
makeGPS: Create your own Signature Object.
nciTable: NCI human gene-pathway associations.
ora: Traditional Overrepresentation Analysis.
reaH: Pathway GPS data, extracted from the Reactome repository...
reaM: Pathway GPS data, extracted from Reactome repository (Mouse).
sigora: Sigora's main function.
sigora-package: SIGNATURE OVERREPRESENTATION ANALYSIS