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

Publication ,  Journal Article
Pang, H; Tong, T; Ng, M
Published in: Stat Appl Genet Mol Biol
June 2013

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.

Duke Scholars

Published In

Stat Appl Genet Mol Biol

DOI

EISSN

1544-6115

Publication Date

June 2013

Volume

12

Issue

3

Start / End Page

347 / 359

Location

Germany

Related Subject Headings

  • Shock, Septic
  • Molecular Diagnostic Techniques
  • Models, Statistical
  • Models, Biological
  • Lymphoma
  • Humans
  • High-Throughput Nucleotide Sequencing
  • Gene Regulatory Networks
  • Gene Expression Profiling
  • Discriminant Analysis
 

Citation

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Pang, H., Tong, T., & Ng, M. (2013). Block-diagonal discriminant analysis and its bias-corrected rules. Stat Appl Genet Mol Biol, 12(3), 347–359. https://doi.org/10.1515/sagmb-2012-0017
Pang, Herbert, Tiejun Tong, and Michael Ng. “Block-diagonal discriminant analysis and its bias-corrected rules.Stat Appl Genet Mol Biol 12, no. 3 (June 2013): 347–59. https://doi.org/10.1515/sagmb-2012-0017.
Pang H, Tong T, Ng M. Block-diagonal discriminant analysis and its bias-corrected rules. Stat Appl Genet Mol Biol. 2013 Jun;12(3):347–59.
Pang, Herbert, et al. “Block-diagonal discriminant analysis and its bias-corrected rules.Stat Appl Genet Mol Biol, vol. 12, no. 3, June 2013, pp. 347–59. Pubmed, doi:10.1515/sagmb-2012-0017.
Pang H, Tong T, Ng M. Block-diagonal discriminant analysis and its bias-corrected rules. Stat Appl Genet Mol Biol. 2013 Jun;12(3):347–359.
Journal cover image

Published In

Stat Appl Genet Mol Biol

DOI

EISSN

1544-6115

Publication Date

June 2013

Volume

12

Issue

3

Start / End Page

347 / 359

Location

Germany

Related Subject Headings

  • Shock, Septic
  • Molecular Diagnostic Techniques
  • Models, Statistical
  • Models, Biological
  • Lymphoma
  • Humans
  • High-Throughput Nucleotide Sequencing
  • Gene Regulatory Networks
  • Gene Expression Profiling
  • Discriminant Analysis