PT  - JOURNAL ARTICLE
AU  - Lukas M. Simon
AU  - Fangfang Yan
AU  - Zhongming Zhao
TI  - DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data
AID  - 10.1101/864165
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 864165
4099  - http://biorxiv.org/content/early/2019/12/05/864165.short
4100  - http://biorxiv.org/content/early/2019/12/05/864165.full
AB  - 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.DCAdeep count autoencoderMolSigDBthe Molecular Signatures DatabasescRNA-seqsingle cell RNA sequencingTFtranscription factorTPtranscriptional programtSNEt-distributed Stochastic Neighbor Embedding