GiANT: gene set uncertainty in enrichment analysis

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Item Type:	Article
Title:	GiANT: gene set uncertainty in enrichment analysis
Creators Name:	Schmid, F., Schmid, M., Müssel, C., Sträng, J.E., Buske, C., Bullinger, L., Kraus, J.M. and Kestler, H.A.
Abstract:	SUMMARY: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms. We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization.
Keywords:	Algorithms, Computer Simulation, Gene Regulatory Networks, Neoplasms, Retinoblastoma Genes, Software, Uncertainty, Animals, Mice
Source:	Bioinformatics
ISSN:	1367-4803
Publisher:	Oxford University Press
Volume:	32
Number:	12
Page Range:	1891-1894
Date:	15 June 2016
Official Publication:	https://doi.org/10.1093/bioinformatics/btw030
PubMed:	View item in PubMed

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