Gene Selection for Classifications Using Multiple PCA with Sparsity  

作　　者：Yanwei Huang Liqing Zhang 

机构地区：[1]Department of Automation, Fuzhou University, Fuzhou 350108, China [2]Department of Computer Science, Virginia Tech, Biacksburg, VA 24061, USA

出　　处：《Tsinghua Science and Technology》2012年第6期659-665,共7页清华大学学报（自然科学版（英文版）

基　　金：Supported by the Doctoral Fund of Chinese Ministry of Education (No.20113514120007);the Nature Science Fund of Fujian Province in China (No.2010J05132);the Science and Technology Fund of Educational Office of Fujian Province, China (No.JA10034)

摘　　要：A gene selection algorithm was developed using Multiple Principal Component Analysis with Sparsity （MSPCA）. The MSPCA algorithm is used to analyze normal and disease gene expression samples and to set these component Ioadings to zero if they are smaller than a threshold for sparse solutions. Next, genes with zero Ioadings across all samples （both normal and disease） are removed before extracting feature genes. Feature genes are genes that contribute differentially to variations in normal and disease samples and, thus, can be used for classification. The MSPCA is applied to three microarray datasets to select feature genes with a linear support vector machine to evaluate its performance. This method is compared with several previous gene selection results to show that this MSPCA gene selection algorithm has good classification accuracy and model stability.A gene selection algorithm was developed using Multiple Principal Component Analysis with Sparsity （MSPCA）. The MSPCA algorithm is used to analyze normal and disease gene expression samples and to set these component Ioadings to zero if they are smaller than a threshold for sparse solutions. Next, genes with zero Ioadings across all samples （both normal and disease） are removed before extracting feature genes. Feature genes are genes that contribute differentially to variations in normal and disease samples and, thus, can be used for classification. The MSPCA is applied to three microarray datasets to select feature genes with a linear support vector machine to evaluate its performance. This method is compared with several previous gene selection results to show that this MSPCA gene selection algorithm has good classification accuracy and model stability.

关 键 词：microarray gene expression gene selection Multiple Principal Component Analysis with Sparsity （MSPCA） sparse 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]