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Identifying enterotype in human microbiome by decomposing probabilistic topics into components
Conference proceeding

Identifying enterotype in human microbiome by decomposing probabilistic topics into components

Xingpeng Jiang, J Dushoff, Xin Chen and Xiaohua Hu
2012 IEEE International Conference on Bioinformatics and Biomedicine, pp 1-4
Oct 2012
DOI: https://doi.org/10.1109/BIBM.2012.6392720

Abstract

Biological system modeling Communities Computational modeling Correlation Dimension reduction Diseases Humans metagenomic profile non-negative matrix factorization probability topic model
Discovering the global structures of microbial community using large-scale metagenomes is a significant challenge in the era of post-genomics. Data-driven methods such as dimension reduction have shown to be useful when they applied on a metagenomics profile matrix which summarize the abundance of functional or taxonomic categorizations in metagenomic samples. Analogously, model-driven method such as probability topic model (PTM) has been used to build a generative model to simulate the generating of a microbial community based on metagenomic profiles. Data-driven methods are direct and simple, they provide intuitive visualization and understanding of metagenomic profiles. Model-driven methods are often complicated but give a generative mechanism of microbial community which is helpful in understanding the generating process of complex microbial ecology. However, results from model-driven methods are usually hard to visualize and there is less an intuitive understanding of them. We developed a new computational framework to incorporate the strength of data-driven methods into model-based methods and applied the framework to discover and interpret enterotype in human microbiome.

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