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

Single cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic data sets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. Here, we present DrivAER, a machine learning approach that scores annotated gene sets based on their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. We demonstrate that DrivAER extracts the key driving pathways and transcription factors that regulate complex biological processes from scRNA-seq data.

• Publication year

  2019

• Venue

  bioRxiv

• Publication date

  2019-12-05

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1101/864165PMID 33301553PMCID 7727875
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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