Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer
Motivation Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method We present a novel approach for combining microarray data across institutions...
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doaj-393b58dd3f1b401796059fd291749a802020-11-25T03:45:05ZengSAGE PublishingCancer Informatics1176-93512006-01-01210.1177/117693510600200009Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate CancerJing Wang0Kim Anh Do1Sijin Wen2Spyros Tsavachidis3Timothy J. Mcdonnell4Christopher J. Logothetis5Kevin R. Coombes6Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Department of Molecular Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Department of Genitourinary Medical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.Motivation Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. Results We combined two prostate cancer microarray data sets, confirmed the appropriateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%. Availability Affymetrix U95Av2 array data are available at http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi . The cDNA microarray data are available through the Stanford Microarray Database ( http://cmgm.stanford.edu/pbrown/ ). GeneLink software is freely available at http://bioinformatics.mdanderson.org/GeneLink/ . DNA-Chip Analyzer software is publicly available at http://biosun1.harvard.edu/complab/dchip/ .https://doi.org/10.1177/117693510600200009 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jing Wang Kim Anh Do Sijin Wen Spyros Tsavachidis Timothy J. Mcdonnell Christopher J. Logothetis Kevin R. Coombes |
spellingShingle |
Jing Wang Kim Anh Do Sijin Wen Spyros Tsavachidis Timothy J. Mcdonnell Christopher J. Logothetis Kevin R. Coombes Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer Cancer Informatics |
author_facet |
Jing Wang Kim Anh Do Sijin Wen Spyros Tsavachidis Timothy J. Mcdonnell Christopher J. Logothetis Kevin R. Coombes |
author_sort |
Jing Wang |
title |
Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer |
title_short |
Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer |
title_full |
Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer |
title_fullStr |
Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer |
title_full_unstemmed |
Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer |
title_sort |
merging microarray data, robust feature selection, and predicting prognosis in prostate cancer |
publisher |
SAGE Publishing |
series |
Cancer Informatics |
issn |
1176-9351 |
publishDate |
2006-01-01 |
description |
Motivation Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. Results We combined two prostate cancer microarray data sets, confirmed the appropriateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%. Availability Affymetrix U95Av2 array data are available at http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi . The cDNA microarray data are available through the Stanford Microarray Database ( http://cmgm.stanford.edu/pbrown/ ). GeneLink software is freely available at http://bioinformatics.mdanderson.org/GeneLink/ . DNA-Chip Analyzer software is publicly available at http://biosun1.harvard.edu/complab/dchip/ . |
url |
https://doi.org/10.1177/117693510600200009 |
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