Feature selection in omics prediction problems using cat scores and false nondiscovery rate control

Details Feature selection in omics prediction problems using cat scores and false nondiscovery rate control Feature selection in omics prediction problems using cat scores and false nondiscovery rate control

2009-03-11

arxiv.org/abs/0903.2003v4

Annals of Applied Statistics 2010, Vol. 4, No. 1, 503-519

Statistics

Applications

Published in at http://dx.doi.org/10.1214/09-AOAS277 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS277

We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted $t$-scores (cat scores). Second, for feature selection we propose thresholding cat scores by controlling false nondiscovery rates (FNDR). Third, training of the classifier is based on James--Stein shrinkage estimates of correlations and variances, where regularization parameters are chosen analytically without resampling. Overall, this results in an effective and computationally inexpensive framework for high-dimensional prediction with natural feature selection. The proposed shrinkage discriminant procedures are implemented in the R package ``sda'' available from the R repository CRAN.

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