ADAGE signature analysis: differential expression analysis with data-defined gene sets.

ABSTRACT:

Background

Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.

Results

Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural ne

SUBMITTER: Tan J 

PROVIDER: S-EPMC5700673 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature