Clustering threshold gradient descent regularization: with applications to microarray studies

• Published in: Bioinformatics Vol. 23; no. 4; pp. 466 - 472
• Main Authors: Ma, Shuangge, Huang, Jian
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.02.2007; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bioinformatics; Biological and medical sciences; Biomarkers, Tumor - metabolism; Cluster Analysis; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Humans; Lymphoma; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Multigene Family; Neoplasm Proteins - metabolism; Neoplasms - diagnosis; Neoplasms - metabolism; Numerical Analysis, Computer-Assisted; Oligonucleotide Array Sequence Analysis - methods; Statistical models; Disease; DNA chip; K means algorithm; Gene expression; Microarray; Clusterin; Gradient descent; Original document; Regulation(control); Computer program; Classification; Bioinformatics; Comparative study
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btl632

Motivation: An important goal of microarray studies is to discover genes that are associated with clinical outcomes, such as disease status and patient survival. While a typical experiment surveys gene expressions on a global scale, there may be only a small number of genes that have significant influence on a clinical outcome. Moreover, expression data have cluster structures and the genes within a cluster have correlated expressions and coordinated functions, but the effects of individual genes in the same cluster may be different. Accordingly, we seek to build statistical models with the following properties. First, the model is sparse in the sense that only a subset of the parameter vector is non-zero. Second, the cluster structures of gene expressions are properly accounted for. Results: For gene expression data without pathway information, we divide genes into clusters using commonly used methods, such as K-means or hierarchical approaches. The optimal number of clusters is determined using the Gap statistic. We propose a clustering threshold gradient descent regularization (CTGDR) method, for simultaneous cluster selection and within cluster gene selection. We apply this method to binary classification and censored survival analysis. Compared to the standard TGDR and other regularization methods, the CTGDR takes into account the cluster structure and carries out feature selection at both the cluster level and within-cluster gene level. We demonstrate the CTGDR on two studies of cancer classification and two studies correlating survival of lymphoma patients with microarray expressions. Availability: R code is available upon request. Contact:shuangge.ma@yale.edu Supplementary information: Supplementary data are available at Bioinformatics online.