Pathway Analysis is statistically linking observations on the molecular level to biological processes or pathways on the systems(i.e., 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. However, this can lead to identifying spurious 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 datasets. In addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories and mechanisms for extracting GPS for user-supplied repositories.
|Author||Amir Foroushani [aut] (<https://orcid.org/0000-0003-2748-3009>), Fiona Brinkman [aut], David Lynn [aut], Witold Wolski [cre] (<https://orcid.org/0000-0002-6468-120X>)|
|Bioconductor views||GO GeneSetEnrichment KEGG Pathways Software|
|Maintainer||Witold Wolski <email@example.com>|
|Package repository||View on CRAN|
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