MARS: discovering novel cell types across heterogeneous single-cell experiments

Nat Methods. 2020 Dec;17(12):1200-1206. doi: 10.1038/s41592-020-00979-3. Epub 2020 Oct 19.


Although tremendous effort has been put into cell-type annotation, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as new cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method has a unique ability to discover cell types that have never been seen before and annotate experiments that are as yet unannotated. We apply MARS to a large mouse cell atlas and show its ability to accurately identify cell types, even when it has never seen them before. Further, MARS automatically generates interpretable names for new cell types by probabilistically defining a cell type in the embedding space.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Animals
  • Cells / classification*
  • Databases, Factual
  • Gene Expression Profiling
  • Mice
  • RNA / genetics
  • Sequence Analysis, RNA
  • Single-Cell Analysis / methods*
  • Software
  • Transcriptome / genetics*


  • RNA

Associated data

  • figshare/10.6084/m9.figshare.5829687.v8