Logistic regression for disease classification using microarray data: model selection in a large p and small n case

Motivation: Logistic regression is a standard method for building prediction models for a binary outcome and has been extended for disease classification with microarray data by many authors. A feature (gene) selection step, however, must be added to penalized logistic modeling due to a large number...

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Vydáno v:Bioinformatics Ročník 23; číslo 15; s. 1945 - 1951
Hlavní autoři: Liao, J.G., Chin, Khew-Voon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Oxford University Press 01.08.2007
Oxford Publishing Limited (England)
Témata:
Algorithms
Biological and medical sciences
Biomarkers, Tumor - analysis
Data Interpretation, Statistical
Diagnosis, Computer-Assisted - methods
Fundamental and applied biological sciences. Psychology
General aspects
Humans
Leukemia
Logistic Models
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Biological
Neoplasm Proteins - analysis
Neoplasms - classification
Neoplasms - diagnosis
Neoplasms - metabolism
Oligonucleotide Array Sequence Analysis - methods
Prediction models
Regression Analysis
Reproducibility of Results
Sample Size
Sensitivity and Specificity
Error estimation
False positive
Disease
DNA chip
Malignant tumor
Gene expression
Microarray
Original document
Logistic regression
Computer program
Classification
Bootstrap
Models
Bioinformatics
Cancer
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Shrnutí:Motivation: Logistic regression is a standard method for building prediction models for a binary outcome and has been extended for disease classification with microarray data by many authors. A feature (gene) selection step, however, must be added to penalized logistic modeling due to a large number of genes and a small number of subjects. Model selection for this two-step approach requires new statistical tools because prediction error estimation ignoring the feature selection step can be severely downward biased. Generic methods such as cross-validation and non-parametric bootstrap can be very ineffective due to the big variability in the prediction error estimate. Results: We propose a parametric bootstrap model for more accurate estimation of the prediction error that is tailored to the microarray data by borrowing from the extensive research in identifying differentially expressed genes, especially the local false discovery rate. The proposed method provides guidance on the two critical issues in model selection: the number of genes to include in the model and the optimal shrinkage for the penalized logistic regression. We show that selecting more than 20 genes usually helps little in further reducing the prediction error. Application to Golub's leukemia data and our own cervical cancer data leads to highly accurate prediction models. Availability: R library GeneLogit at http://geocities.com/jg_liao Contact: jl544@drexel.edu
Bibliografie:ark:/67375/HXZ-GVFP4455-S
To whom correspondence should be addressed.
Associate Editor: Trey Ideker
istex:A4D248DBC4A4B128B84BDE81E0F874CEBD402849
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btm287