Abstract
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.
Abbreviations
- DCA
- deep count autoencoder
- MolSigDB
- the Molecular Signatures Database
- scRNA-seq
- single cell RNA sequencing
- TF
- transcription factor
- TP
- transcriptional program
- tSNE
- t-distributed Stochastic Neighbor Embedding
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Posted December 05, 2019.
DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data
