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Two-Stage Logistic Regression for Cancer Classi cation and Prediction from Copy-Numbe Changes in cDNA Microarray-Based Comparative Genomic Hybridization
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 Title & Authors
Two-Stage Logistic Regression for Cancer Classi cation and Prediction from Copy-Numbe Changes in cDNA Microarray-Based Comparative Genomic Hybridization
Kim, Mi-Jung;
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 Abstract
cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients` classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients` classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients` clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.
 Keywords
cDNA microarray-based comparative genomic hybridization;gene copy-number change;predicted probability;decision tree;factor analysis;logistic regression model;
 Language
English
 Cited by
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