Matrix Decomposition Methods in Bioinformatics

Author(s): Li-Ping Tian, Lizhi Liu, Fang-Xiang Wu.

Journal Name: Current Bioinformatics

Volume 8 , Issue 2 , 2013

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With advances in biotechnology, a huge amount of high throughput biological data has been and will continuously be produced. The information contained in such data is very useful in understanding the biological process from which such data is collected. Generally, high throughput biological data such as gene expression data is presented in a data matrix. Through matrix decomposition methods, we can often discover some very useful information. In bioinformatics, principal component analysis (PCA), independent component analysis (ICA), nonnegative matrix factorization (NMF) and network component analysis (NCA) are widely used to help understand and utilize high throughput data. They are all matrix decomposition methods, but subject to different constraints. In this paper, each of these methods is introduced and its applications to high throughput biological data are discussed. We also compare these methods and discuss their pros and cons.

Keywords: Bioinformatics, independent component analysis, matrix decomposition, network component analysis, nonnegative matrix factorization, principal component analysis, Independent Component Analysis, Fast ICA Algorithm, Network Component Analysis, transcription factor activity

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Article Details

Year: 2013
Page: [259 - 266]
Pages: 8
DOI: 10.2174/1574893611308020014
Price: $58

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