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Partial least squares and logistic regression random-effects estimates for gene selection in supervised
Arief Gusnanto1, Alexander Ploner, Farag Shuweihdi
1Department of Statistics, University of Leeds, Leeds, United Kingdom. a.gusnanto@leeds.ac.uk
Journal of Biomedical Informatics
|June 11, 2013
Summary
Selecting informative genes for classification is crucial. Logistic regression random-effects (RE) estimates are recommended for general gene selection, while partial least squares (PLS) is better for small sample sizes.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Supervised classification of gene expression data aims to identify biological characteristics.
- High-dimensional gene expression data necessitates effective gene selection methods.
Purpose of the Study:
- To propose and evaluate gene selection methods for supervised classification of gene expression data.
- To compare gene selection based on partial least squares (PLS) and logistic regression random-effects (RE) estimates against current practices.
Main Methods:
- Gene selection using logistic regression RE estimates.
- Gene selection using PLS estimates.
- Comparison with two-sample t-statistics and modified t-statistics.
- Evaluation of selected genes in classification models.
Main Results:
- Logistic regression RE estimates are recommended for general gene selection.
- PLS estimates are recommended for datasets with a low number of samples.
- Modified t-statistics perform well with moderate gene variability and group separation.
Conclusions:
- The choice of gene selection method should consider the specific characteristics of the gene expression data.
- Logistic regression RE and PLS offer robust alternatives for gene selection in classification tasks.
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