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
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