Characteristic Gene Selection via Weighting Principal Components by Singular Values

Abstract

Conventional gene selection methods based on principal component analysis (PCA) use only the first principal component (PC) of PCA or sparse PCA to select characteristic genes. These methods indeed assume that the first PC plays a dominant role in gene selection. However, in a number of cases this assumption is not satisfied, so the conventional PCA-based methods usually provide poor selection results. In order to improve the performance of the PCA-based gene selection method, we put forward the gene selection method via weighting PCs by singular values (WPCS). Because different PCs have different importance, the singular values are exploited as the weights to represent the influence on gene selection of different PCs. The ROC curves and AUC statistics on artificial data show that our method outperforms the state-of-the-art methods. Moreover, experimental results on real gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.

Block-Constraint Robust Principal Component Analysis and its Application to Integrated Analysis of TCGA Data

“…For instance, Liu et al proposed a weighting principal component method to select characteristic genes [12]. A class-Information-based sparse component analysis method was proposed to identify differentially expressed genes [13].…”

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

“…Up to now, to find a group of genes which are relevant to a biological process from gene expression data, various feature extraction methods have been proposed for recognizing differentially expression genes. For example, Liu et al selected characteristic genes by utilizing weight principal components by singular values [ 3 ]; the differential gene pathways were identified via principal component analysis by Ma et al [ 4 ]; Zheng et al selected feature genes using nonnegative matrix factorization and sparse nonnegative matrix factorization [ 5 ]. Many extraction methods, especially sparse methods, are always taking advantage of norm, and different methods using different norm.…”

A Class-Information-Based Penalized Matrix Decomposition for Identifying Plants Core Genes Responding to Abiotic Stresses

“…In addition, many sparse methods have already been chosen to deal with the gene expression data. Liu et al used the first principal component (PC) of SPCA for extracting plants core genes [22] . Yin et al identified differential gene pathways with SPCA [23] .…”