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

Abstract

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.