Discovering What Dimensionality Reduction Really Tells Us About RNA-Seq Data.

ABSTRACT: Biology is being inundated by noisy, high-dimensional data to an extent never before experienced. Dimensionality reduction techniques such as principal component analysis (PCA) are common approaches for dealing with this onslaught. Though these unsupervised techniques can help uncover interesting structure in high-dimensional data they give little insight into the biological and technical considerations that might explain the uncovered structure. Here we introduce a hybrid approach--component selection using mutual information (CSUMI)--that uses a mutual information--based statistic to reinterpret the results of PCA in a biologically meaningful way. We apply CSUMI to RNA-seq data from GTEx. Our hybrid approach enables us to unveil the previously hidden relationship between principal compon

SUBMITTER: Simmons S 

PROVIDER: S-EPMC4523039 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature