Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data - Yifan Zhao (Nature Portfolio, 2021)
Computational single-cell RNA-seq analyses often face challenges in scalability, model interpretability, and confounders. Here, we show a new model to address these challenges by learning meaningful embeddings from the data that simultaneously refine gene signatures and cell functions in diverse conditions.