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Testing significance of features by lassoed principal components. (English) Zbl 1149.62092

Summary: We consider the problem of testing the significance of features in high-dimensional settings. In particular, we test for differentially-expressed genes in a microarray experiment. We wish to identify genes that are associated with some type of outcome, such as survival time or cancer type. We propose a new procedure, called Lassoed Principal Components (LPC), that builds upon existing methods and can provide a sizable improvement. For instance, in the case of two-class data, a standard (albeit simple) approach might be to compute a two-sample \(t\)-statistic for each gene. The LPC method involves projecting these conventional gene scores onto the eigenvectors of the gene expression data covariance matrix and then applying an \(L_{1}\) penalty in order to de-noise the resulting projections.
We present a theoretical framework under which LPC is the logical choice for identifying significant genes, and we show that LPC can provide a marked reduction in false discovery rates over the conventional methods on both real and simulated data. Moreover, this flexible procedure can be applied to a variety of types of data and can be used to improve many existing methods for the identification of significant features.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62H25 Factor analysis and principal components; correspondence analysis
65C60 Computational problems in statistics (MSC2010)

Software:

lpc; Eigenstrat
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References:

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