title: A robust rerank approach for feature selection and its application to pooling-based GWA studies.
abstract: Large-p-small-n datasets are commonly encountered in modern biomedical studies. To detect the difference between two groups, conventional methods would fail to apply due to the instability in estimating variances in t-test and a high proportion of tied values in AUC (area under the receiver operating characteristic curve) estimates. The significance analysis of microarrays (SAM) may also not be satisfactory, since its performance is sensitive to the tuning parameter, and its selection is not straightforward. In this work, we propose a robust rerank approach to overcome the above-mentioned diffculties. In particular, we obtain a rank-based statistic for each feature based on the concept of "rank-over-variable." Techniques of "random subset" and "rerank" are then iteratively applied to rank features, and the leading features will be selected for further studies. The proposed re-rank approach is especially applicable for large-p-small-n datasets. Moreover, it is insensitive to the selection of tuning parameters, which is an appealing property for practical implementation. Simulation studies and real data analysis of pooling-based genome wide association (GWA) studies demonstrate the usefulness of our method.
authors: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.
authors: Jia-Rou
authors: JR
authors: Liu
authors: Po-Hsiu
authors: PH
authors: Kuo
authors: Hung
authors: H
authors: Hung
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001714:Bipolar Disorder / Q000235:genetics; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D003865:Major Depressive Disorder / Q000235:genetics; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D005819:Genetic Markers; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D018709:Statistics, Nonparametric
chemical_list: D005819:Genetic Markers
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