DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps.

Findings

Here, we present DrivAER, a machine learning approach for the identification of driving transcriptional programs using autoencoder-based relevance scores. DrivAER scores annotated gene sets on the basis of their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to

SUBMITTER: Simon LM 

PROVIDER: S-EPMC7727875 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature