CLIC (Clustering by Inferred Co-expression)
CLIC is a computational tool for helping users identify new members of a pathway of interest, as well as the RNA expression datasets in which that pathway is relevant. CLIC accepts a gene set and performs two steps. First, CLIC partitions the input set G into disjoint co-expression modules (CEMs) using a Bayesian partition model to simultaneously infer the number of CEMs, gene membership for each CEM, and the datasets that support the expression correlation for each CEM. Second, the algorithm creates a CEM expansion set, CEM+, that includes other genes in the genome that are likely to have arisen under the CEM’s inferred model of co-expression compared to a null model. For any input gene set G, CLIC will output a set of co-expressed modules (CEMs) and additional genes sharing similar expression (CEM+).
Y. Li, A.A. Jourdain, S.E. Calvo, J.S. Liu, and V.K. Mootha (2017) CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets, PloS Computational Biology. Jul 18;13(7):e1005653. doi: 10.1371/journal.pcbi.1005653 Pubmed: 28719601