ArticleStatistical applications in genetics and molecular biology2013
Block-diagonal discriminant analysis and its bias-corrected rules.
Article in Statistical applications in genetics and molecular biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Biomarker identification through spatial proteomics for the characterization of indeterminate thyroid nodules.Endocrine · 2025Article
- Repeated measures discriminant analysis using multivariate generalized estimation equations.Statistical methods in medical research · 2022Article
- Sparse Linear Discriminant Analysis using the Prior-Knowledge-Guided Block Covariance Matrix.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2020Article
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Abstract
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.
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