Sparse-low rank matrix decomposition framework for identifying potential biomarkers for inflammatory bowel disease

摘要

Inflammatory bowel disease (IBD) is a class of uncured chronic diseases which causes severe discomfort and in some cases could lead to life-threatening complications. Recent studies suggest a relationship between IBD and the gut microbiota. These findings reveal potential for identifying bacterial biomarkers for IBD to enable the detection and further investigation into unknown aspects of the disease. This work presents a novel method for identifying microbial biomarkers using robust principal component analysis (RPCA). Our method uses matrix decomposition to separate bacteria exhibiting a difference in abundance between healthy and diseased samples from the bacteria that have not undergone substantial change in abundance. Our method then ranks and identifies the top bacteria to be used as biomarkers. We contrast the proposed method with three well used state-of-the-art bacterial biomarker detection approaches over two datasets in relation to IBD. Our method outperforms the competing methods on the different evaluation cases.