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

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 gi

SUBMITTER: Lee D 

PROVIDER: S-EPMC2902448 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature