Super-sparse principal component analyses for high-throughput genomic data |
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Authors: | Donghwan Lee Woojoo Lee Youngjo Lee Yudi Pawitan |
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Institution: | (1) Department of Statistics, Seoul National University, Seoul, South Korea;(2) Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden |
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Abstract: | Background Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However,
it is often difficult to interpret the results because the principal components are linear combinations of all variables,
and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector
loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any
tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced
for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data
applications because they still give too many nonzero coefficients. |
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Keywords: | |
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