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Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
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Donald Geman
Published/Copyright:
August 30, 2004
We present a new approach to molecular classification based on mRNA comparisons. Our method, referred to as the top-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray data for class prediction, for example to detect disease, identify tumors or predict treatment response. Accurate statistical inference from such data is difficult due to the small number of observations, typically tens, relative to the large number of genes, typically thousands. Moreover, conventional methods from machine learning lead to decisions which are usually very difficult to interpret in simple or biologically meaningful terms. In contrast, the TSP classifier provides decision rules which i) involve very few genes and only relative expression values (e.g., comparing the mRNA counts within a single pair of genes); ii) are both accurate and transparent; and iii) provide specific hypotheses for follow-up studies. In particular, the TSP classifier achieves prediction rates with standard cancer data that are as high as those of previous studies which use considerably more genes and complex procedures. Finally, the TSP classifier is parameter-free, thus avoiding the type of over-fitting and inflated estimates of performance that result when all aspects of learning a predictor are not properly cross-validated.
Published Online: 2004-8-30
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
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- Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
- Asymptotic Optimality of Likelihood-Based Cross-Validation
- Using Importance Sampling to Improve Simulation in Linkage Analysis
- Model-Based Assignment and Inference of Protein Backbone Nuclear Magnetic Resonances
- Error-Rate and Decision-Theoretic Methods of Multiple Testing: Which Genes Have High Objective Probabilities of Differential Expression?
- Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma
- Saturation and Quantization Reduction in Microarray Experiments using Two Scans at Different Sensitivities
- Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection
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- Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate
- Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
- Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
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