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CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets
View ORCID ProfileStephen J. Fleming, View ORCID ProfileJohn C. Marioni, Mehrtash Babadi
doi: https://doi.org/10.1101/791699
Stephen J. Fleming
1Data Sciences Platform (DSP), The Broad Institute, 415 Main St, Cambridge, MA 02142
2Precision Cardiology Laboratory (PCL), The Broad Institute, Cambridge, MA, USA 02142
John C. Marioni
3Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK
4European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
Mehrtash Babadi
1Data Sciences Platform (DSP), The Broad Institute, 415 Main St, Cambridge, MA 02142
2Precision Cardiology Laboratory (PCL), The Broad Institute, Cambridge, MA, USA 02142
Posted October 03, 2019.
CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets
Stephen J. Fleming, John C. Marioni, Mehrtash Babadi
bioRxiv 791699; doi: https://doi.org/10.1101/791699
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