Ensemble learning for classifying single-cell data and projection across reference atlases

Bioinformatics. 2020 Jun 1;36(11):3585-3587. doi: 10.1093/bioinformatics/btaa137.


Summary: Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.

Availability and implementation: https://github.com/diazlab/ELSA.

Contact: aaron.diaz@ucsf.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

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

MeSH terms

  • Gene Expression Profiling*
  • Sequence Analysis, RNA
  • Single-Cell Analysis
  • Software*