An ICA-based strategy for gender effect elimination

Inhalt:
The unavoidable impact of gender differences on metabolomics results was underscored in several independent cohorts. In this paper, we reported a simple and effective data processing strategy to remove gender effect without reducing the number of metabolites or samples. The strategy consists of three steps: a) independent components decomposition, b) gender dependent components selection, and c) data reconstruction by gender independent components. We verified the effectiveness and safety of such method on both simulated and clinical datasets. It is highly recommended to identify and eliminate gender effect before statistical analysis even when samples were gender matched in different groups. A GUI based application was developed and is freely available for academic use.