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

TitleBlock-diagonal discriminant analysis and its bias-corrected rules.
Publication TypeJournal Article
Year of Publication2013
AuthorsPang, Herbert, Tiejun Tong, and Michael Ng
JournalStat Appl Genet Mol Biol
Date Published2013 Jun
KeywordsAlgorithms, Case-Control Studies, Computer Simulation, Data Interpretation, Statistical, Diagnosis, Differential, Discriminant Analysis, Gene Expression Profiling, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Humans, Lymphoma, Models, Biological, Models, Statistical, Molecular Diagnostic Techniques, Shock, Septic

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.

Alternate JournalStat Appl Genet Mol Biol
Original PublicationBlock-diagonal discriminant analysis and its bias-corrected rules.
PubMed ID23735433
PubMed Central IDPMC3856189
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States