CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets.

ABSTRACT: In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of eac

SUBMITTER: Li Y 

PROVIDER: S-EPMC5546725 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature