RT Journal Article
SR Electronic
T1 PathCORE: visualizing globally co-occurring pathways in large transcriptomic compendia
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 147645
DO 10.1101/147645
A1 Kathleen M. Chen
A1 Jie Tan
A1 Gregory P. Way
A1 Georgia Doing
A1 Deborah A. Hogan
A1 Casey S. Greene
YR 2017
UL http://biorxiv.org/content/early/2017/06/08/147645.abstract
AB Background Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway interactions; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.Results We developed the PathCORE software to predict global pathway-pathway interactions, i.e. those evident across a broad data compendium. PathCORE starts with the results of robust feature construction algorithms, which are now being developed and applied to transcriptomic data. PathCORE identifies pathways grouped together in features more than expected by chance as_functionally co-occurring. We performed example analyses using PathCORE for a microbial compendium for which eADAGE features were already available and a TCGA dataset of 33 cancer types that we analyzed via NMF. PathCORE recapitulated previously described pathway-pathway interactions and suggested additional edges with biological plausibility that still remain to be explored. The software also identifies genes associated with each relationship and includes a user-installable web interface where users can (1) visualize the resulting network and (2) review the expression levels of associated genes in the original data, which helps biologists using the PathCORE software design experiments to test the relationships that were identified.Conclusions PathCORE is a hypothesis generation tool that identifies co-occurring pathways from the results of unsupervised analysis of the growing body of gene expression data. Software that steps beyond within-pathway relationships to between-pathway relationships can reveal levels of organization that have been less frequently considered.