Super-sparse principal component analyses for high-throughput genomic data

  title={Super-sparse principal component analyses for high-throughput genomic data},
  author={Donghwan Lee and Woojoo Lee and Youngjo Lee and Yudi Pawitan},
  booktitle={BMC Bioinformatics},
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… CONTINUE READING
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