GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

ABSTRACT:

Method

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a r

SUBMITTER: Wagner F 

PROVIDER: S-EPMC4648502 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature