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

Yifan Zhao, Huiyu Cai, Zuobai Zhang, Jian Tang, Yue Li
2021 unpublished
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 10^6 cells and enables effective knowledge transfer across
more » ... ansfer across datasets. 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:10.21203/rs.3.rs-151085/v1 fatcat:hh7oedpkqnfczkth5q3vkuif7i