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+).