Abstract
To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for cell lineation and identifying bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch-Effect ReMoval Using Deep Autoencoders) — a novel transfer-learning-based method for batch-effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.
List of Abbreviations
- AUROC
- area under the receiver operator characteristic curve
- CCA
- canonical correlation analysis
- MMD
- maximum mean discrepancy
- MNN
- mutual nearest neighbor
- PBMC
- peripheral blood mononuclear cell
- RKHS
- reproducing kernel Hilbert space
- scRNA-seq
- single-cell RNA sequencing
- TMP
- transcript-per-million
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.