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

Published Web Location

https://doi.org/10.1093/bioinformatics/btaa137
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.

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