Proceeding talk – Theme: Genes.
Abstract
Gene set analysis (GSA) is a powerful tool for determining whether a set of genes is enriched for genes in known pathways or gene ontology terms. Current GSA methods do not facilitate comparing gene sets across different organisms as they do not explicitly deal with homology mapping between species. There lacks a systematic investigation about the effect of complex gene homology on cross-species GSA. In this work, we show that not accounting for complex homology when performing cross-species GSA leads to false positive biases. We propose XGSA, that explicitly takes homology mapping into consideration when doing cross-species GSA. Simulation experiments confirm that XGSA can avoid false positive discoveries, while maintaining good statistical power compared to other ad-hoc approaches for cross-species gene set analysis. We demonstrate the effectiveness of XGSA with case studies that aim to discover conserved or species-specific molecular pathways involved in social challenge and vertebrate organ regeneration.
Authors
Djordje Djordjevic, Victor Chang Cardiac Research Institute, Australia
Kenro Kusumi, Arizona State University, United States
Joshua Ho, Victor Chang Cardiac Research Institute, Australia