Methodological Review: Towards knowledge-based gene expression data mining
Journal of Biomedical Informatics
Formulating and testing hypotheses in functional genomics
Artificial Intelligence in Medicine
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
A stability-based algorithm to validate hierarchical clusters of genes
International Journal of Knowledge Engineering and Soft Data Paradigms
CarGene: Characterisation of sets of genes based on metabolic pathways analysis
International Journal of Data Mining and Bioinformatics
The multi-reference contrast method: Facilitating set enrichment analysis
Computers in Biology and Medicine
δTRIMAX: erxtracting triclusters and analysing coregulation in time series gene expression data
WABI'12 Proceedings of the 12th international conference on Algorithms in Bioinformatics
Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: Functional analyses based on the association of Gene Ontology (GO) terms to genes in a selected gene list are useful bioinformatic tools and the GOstats package has been widely used to perform such computations. In this paper we report significant improvements and extensions such as support for conditional testing. Results: We discuss the capabilities of GOstats, a Bioconductor package written in R, that allows users to test GO terms for over or under-representation using either a classical hypergeometric test or a conditional hypergeometric that uses the relationships among GO terms to decorrelate the results. Availability: GOstats is available as an R package from the Bioconductor project: http://bioconductor.org Contact: sfalcon@fhcrc.org