XGSA: A statistical method for cross-species gene set analysis

In this study, we show that not accounting for the complex homology structure when comparing gene sets in two species can lead to false positive discoveries, especially when comparing gene sets that have complex gene homology relationships. To overcome this bias, we propose a straightforward statistical approach, called XGSA, that explicitly takes the cross-species homology mapping into consideration when doing gene set analysis. 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 further demonstrate the effectiveness of XGSA with two real-life case studies that aim to discover conserved or species-specific molecular pathways involved in social challenge and vertebrate appendage regeneration. Availability and Implementation: The R source code for XGSA is available under a GNU General Public License at http://github.com/VCCRI/XGSA Contact: jho@victorchang.edu.au
Source: Bioinformatics - Category: Bioinformatics Authors: Tags: GENES Source Type: research