Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.

ABSTRACT: Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that asso

SUBMITTER: Ciucci S 

PROVIDER: S-EPMC5347127 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature