The linear neuron as marker selector and clinical predictor in cancer gene analysis
Computer Methods and Programs in Biomedicine
Wrapper filtering criteria via linear neuron and kernel approaches
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene network modules-based liner discriminant analysis of microarray gene expression data
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
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Motivation: The nearest shrunken centroids classifier has become a popular algorithm in tumor classification problems using gene expression microarray data. Feature selection is an embedded part of the method to select top-ranking genes based on a univariate distance statistic calculated for each gene individually. The univariate statistics summarize gene expression profiles outside of the gene co-regulation network context, leading to redundant information being included in the selection procedure. Results: We propose an Eigengene-based Linear Discriminant Analysis (ELDA) to address gene selection in a multivariate framework. The algorithm uses a modified rotated Spectral Decomposition (SpD) technique to select 'hub' genes that associate with the most important eigenvectors. Using three benchmark cancer microarray datasets, we show that ELDA selects the most characteristic genes, leading to substantially smaller classifiers than the univariate feature selection based analogues. The resulting de-correlated expression profiles make the gene-wise independence assumption more realistic and applicable for the shrunken centroids classifier and other diagonal linear discriminant type of models. Our algorithm further incorporates a misclassification cost matrix, allowing differential penalization of one type of error over another. In the breast cancer data, we show false negative prognosis can be controlled via a cost-adjusted discriminant function. Availability: R code for the ELDA algorithm is available from author upon request. Contact: zhaoling.meng@sanofi-aventis.com Supplementary information: Supplementary data are available at Bioinformatics online.