Improving gene expression data interpretation by finding latent factors that co-regulate gene modules with clinical factors

• Type of Material: Article
• Authors: Tianwei Yu, Emory University; Yun Bai, Philadelphia College of Osteopathic Medicine
• Language: English
• Date: 2011-11-16
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2011 Yu and Bai; licensee BioMed Central Ltd.
• License: Creative Commons Attribution 2.0 Generic
• Final Published Version (URL): http://www.biomedcentral.com/1471-2164/12/563
• Title of Journal or Parent Work: BMC Genomics
• ISSN: 1471-2164
• Volume: 12
• Issue: 563
• Start Page: 1
• End Page: 12
• Grant/Funding Information: This research was partially supported by NIH grants 5P01ES016731, 5U19AI057266 and 1U19AI090023.
• Research Categories: Biology, Biostatistics

Abstract

Background In the analysis of high-throughput data with a clinical outcome, researchers mostly focus on genes/proteins that show first-order relations with the clinical outcome. While this approach yields biomarkers and biological mechanisms that are easily interpretable, it may miss information that is important to the understanding of disease mechanism and/or treatment response. Here we test the hypothesis that unobserved factors can be mobilized by the living system to coordinate the response to the clinical factors. Results We developed a computational method named Guided Latent Factor Discovery (GLFD) to identify hidden factors that act in combination with the observed clinical factors to control gene modules. In simulation studies, the method recovered masked factors effectively. Using real microarray data, we demonstrate that the method identifies latent factors that are biologically relevant, and extracts more information than analyzing only the first-order response to the clinical outcome. Conclusions Finding latent factors using GLFD brings extra insight into the mechanisms of the disease/drug response. The R code of the method is available at http://userwww.service.emory.edu/~tyu8/GLFD webcite.