RT Journal Article
SR Electronic
T1 DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 864165
DO 10.1101/864165
A1 Simon, Lukas M.
A1 Yan, Fangfang
A1 Zhao, Zhongming
YR 2019
UL http://biorxiv.org/content/early/2019/12/05/864165.abstract
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