Block-diagonal discriminant analysis and its bias-corrected rules.

Published

Journal Article

High-throughput expression profiling allows simultaneous measure of tens of thousands of genes at once. These data have motivated the development of reliable biomarkers for disease subtypes identification and diagnosis. Many methods have been developed in the literature for analyzing these data, such as diagonal discriminant analysis, support vector machines, and k-nearest neighbor methods. The diagonal discriminant methods have been shown to perform well for high-dimensional data with small sample sizes. Despite its popularity, the independence assumption is unlikely to be true in practice. Recently, a gene module based linear discriminant analysis strategy has been proposed by utilizing the correlation among genes in discriminant analysis. However, the approach can be underpowered when the samples of the two classes are unbalanced. In this paper, we propose to correct the biases in the discriminant scores of block-diagonal discriminant analysis. In simulation studies, our proposed method outperforms other approaches in various settings. We also illustrate our proposed discriminant analysis method for analyzing microarray data studies.

Full Text

Duke Authors

Cited Authors

  • Pang, H; Tong, T; Ng, M

Published Date

  • June 2013

Published In

Volume / Issue

  • 12 / 3

Start / End Page

  • 347 - 359

PubMed ID

  • 23735433

Pubmed Central ID

  • 23735433

Electronic International Standard Serial Number (EISSN)

  • 1544-6115

Digital Object Identifier (DOI)

  • 10.1515/sagmb-2012-0017

Language

  • eng

Conference Location

  • Germany