Network-based modular latent structure analysis

BMC Bioinformatics

Proceedings

Tianwei Yu¹  Yun Bai² 

[1] Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA;Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA;

关键词: matrix decomposition;    modularity;    latent factors;    network;    community detection;   

DOI  :  10.1186/1471-2105-15-S13-S6

来源: Springer

【 摘 要 】

BackgroundHigh-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.MethodsHere 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.ResultsIn 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.ConclusionsThe 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/.

【 授权许可】

CC BY   
© Yu and Bai; licensee BioMed Central Ltd. 2014