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scRNABatchQC: multi-samples quality control for single cell RNA-seq data
2019
Bioinformatics
Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between
doi:10.1093/bioinformatics/btz601
pmid:31373345
pmcid:PMC6954654
fatcat:dga4ihz4ovd4xge2hokqgwbgne