Robust PCA based method for discovering differentially expressed genes
TL;DR: A novel method to discover differentially expressed genes based on robust principal component analysis (RPCA) based on perturbation signals S and low-rank matrix A is proposed and results show that the method is efficient and effective.
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Abstract: How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.
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References
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TL;DR: In this paper, the authors prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm.
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GO: :TermFinder---open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes
Elizabeth I. Boyle,Shuai Weng,Jeremy Gollub,Heng Jin,David Botstein,J. Michael Cherry,Gavin Sherlock +6 more
TL;DR: GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology information and evaluating and visualizing the collective annotation of a list of genes to GO terms, which can be used to draw conclusions from microarray and other biological data.