Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Life Sciences
Improving Gene Expression Data Interpretation By Finding Latent Factors That Co-Regulate Gene Modules With Clinical Factors, Tianwei Yu, Yun Bai
Improving Gene Expression Data Interpretation By Finding Latent Factors That Co-Regulate Gene Modules With Clinical Factors, Tianwei Yu, Yun Bai
PCOM Scholarly Papers
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 …
Capturing Changes In Gene Expression Dynamics By Gene Set Differential Coordination Analysis, Tianwei Yu, Yun Bai
Capturing Changes In Gene Expression Dynamics By Gene Set Differential Coordination Analysis, Tianwei Yu, Yun Bai
PCOM Scholarly Papers
Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis - finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect …