Comprehensive evaluation of matrix factorization methods for the analysis of DNA microarray gene expression data

BMC Bioinformatics

Proceedings

Hwa Jeong Seo¹  Ju Han Kim²  Mi Hyeon Kim²  Je-Gun Joung³ 

[1] Medical Informatics, Graduate School of Public Health, Gachon University of Medicine and Science, 40576, Incheon, Korea;Seoul National University Biomedical Informatics (SNUBI), Systems Biomedical Informatics Research Center, and Interdisciplinary Program of Medical Informatics Div. of Biomedical Informatics, Seoul National University College of Medicine, 110799, Seoul, Korea;Seoul National University Biomedical Informatics (SNUBI), Systems Biomedical Informatics Research Center, and Interdisciplinary Program of Medical Informatics Div. of Biomedical Informatics, Seoul National University College of Medicine, 110799, Seoul, Korea;Institute of Endemic Diseases, Seoul National University College of Medicine, 110799, Seoul, Korea;

关键词: Gene Ontology;    Acute Lymphoblastic Leukemia;    Singular Value Decomposition;    Independent Component Analysis;    Acute Myelogenous Leukemia;   

DOI  :  10.1186/1471-2105-12-S13-S8

来源: Springer

【 摘 要 】

BackgroundClustering-based methods on gene-expression analysis have been shown to be useful in biomedical applications such as cancer subtype discovery. Among them, Matrix factorization (MF) is advantageous for clustering gene expression patterns from DNA microarray experiments, as it efficiently reduces the dimension of gene expression data. Although several MF methods have been proposed for clustering gene expression patterns, a systematic evaluation has not been reported yet.ResultsHere we evaluated the clustering performance of orthogonal and non-orthogonal MFs by a total of nine measurements for performance in four gene expression datasets and one well-known dataset for clustering. Specifically, we employed a non-orthogonal MF algorithm, BSNMF (Bi-directional Sparse Non-negative Matrix Factorization), that applies bi-directional sparseness constraints superimposed on non-negative constraints, comprising a few dominantly co-expressed genes and samples together. Non-orthogonal MFs tended to show better clustering-quality and prediction-accuracy indices than orthogonal MFs as well as a traditional method, K-means. Moreover, BSNMF showed improved performance in these measurements. Non-orthogonal MFs including BSNMF showed also good performance in the functional enrichment test using Gene Ontology terms and biological pathways.ConclusionsIn conclusion, the clustering performance of orthogonal and non-orthogonal MFs was appropriately evaluated for clustering microarray data by comprehensive measurements. This study showed that non-orthogonal MFs have better performance than orthogonal MFs and K-means for clustering microarray data.

【 授权许可】

CC BY   
© Kim et al; licensee BioMed Central Ltd. 2011