A comprehensive evaluation of module detection methods for gene expression data.

ABSTRACT: A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investi

SUBMITTER: Saelens W 

PROVIDER: S-EPMC5854612 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature