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Classifying Gene Expression Profiles from Pairwise mRNA Comparisons

Author

Listed:
  • Geman Donald

    (Johns Hopkins University)

  • d'Avignon Christian

    (Johns Hopkins University)

  • Naiman Daniel Q.

    (Johns Hopkins University)

  • Winslow Raimond L.

    (Johns Hopkins University)

Abstract

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.

Suggested Citation

  • Geman Donald & d'Avignon Christian & Naiman Daniel Q. & Winslow Raimond L., 2004. "Classifying Gene Expression Profiles from Pairwise mRNA Comparisons," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-22, August.
  • Handle: RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:19
    DOI: 10.2202/1544-6115.1071
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    Citations

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    Cited by:

    1. Pingzhao Hu & Xinchen Wang & Jack J Haitsma & Suleiman Furmli & Hussain Masoom & Mingyao Liu & Yumiko Imai & Arthur S Slutsky & Joseph Beyene & Celia M T Greenwood & Claudia dos Santos, 2012. "Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-17, October.
    2. Parker Hilary S. & Leek Jeffrey T., 2012. "The practical effect of batch on genomic prediction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-22, April.
    3. Yang Sitan & Naiman Daniel Q., 2014. "Multiclass cancer classification based on gene expression comparison," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 477-496, August.
    4. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    5. Yang, Tae Young, 2009. "Simple Bayesian binary framework for discovering significant genes and classifying cancer diagnosis," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1743-1754, March.
    6. Quynh Van Nong & Chi Tim Ng, 2021. "Clustering of subsample means based on pairwise L1 regularized empirical likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 135-174, February.
    7. Armando Fernandes & Susana Vinga, 2016. "Improving Protein Expression Prediction Using Extra Features and Ensemble Averaging," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.

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