Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data [article]

Yifan Zhao, Huiyu Cai, Zuobai Zhang, Jian Tang, Yue Li
2021 bioRxiv   pre-print
The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, integrative analysis of scRNA-seq data remains a challenge largely due to batch effects. We present single-cell Embedded Topic Model (scETM), an unsupervised deep generative model that recapitulates known cell types by inferring the latent cell topic mixtures via a variational autoencoder. scETM is scalable to over 106 cells and enables effective knowledge transfer across
more » ... s. scETM also offers high interpretability and allows the incorporation of prior pathway knowledge into the gene embeddings. The scETM-inferred topics show enrichment in cell-type-specific and disease-related pathways.
doi:10.1101/2021.01.13.426593 fatcat:r4miiy6kxnfufh2h7mbij3rbwy