Network-based modular latent structure analysis

• Type of Material: Article
• Authors: Tianwei Yu, Emory University; Yun Bai, Philadelphia College of Osteopathic Medicine
• Language: English
• Date: 2014
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2014 Yu and Bai
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/1471-2105-15-S13-S6
• Title of Journal or Parent Work: BMC Bioinformatics
• ISSN: 1471-2105
• Volume: 15
• Issue: Suppl 13
• Start Page: S6
• End Page: S6
• Grant/Funding Information: he funding source to publish the publication cost is NIH grant U19AI090023.; This work was partially supported by NIH grants P20HL113451, P30AI50409 and U19AI090023.
• Keywords: community detection; latent factors; matrix decomposition; modularity,; network
• Research Categories: Health Sciences, Pharmacy; Health Sciences, Public Health

Abstract

Background High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership. Methods Here we describe a method based on community detection in the co-expression network. It consists of inference-based network construction, module detection, and interacting latent factor detection from modules. Results In simulations, the method outperformed projection-based modular latent factor discovery when the input signals were not Gaussian. We also demonstrate the method's value in real data analysis. Conclusions The new method nMLSA (network-based modular latent structure analysis) is effective in detecting latent structures, and is easy to extend to non-linear cases. The method is available as R code at http://web1.sph.emory.edu/users/tyu8/nMLSA/.