EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data
By Aaron T. L. Lun, Samantha J. Riesenfeld, Tallulah S. Andrews, T. Dao, T. Gomes, John C. Marioni. Genome Biology, 2019
871 citations recorded by the source corpus.
Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that EmptyDrops has greater power than existing approaches while controlling the false discovery rate among detected cells. Our method also retains distinct cell types that would have been discarded by existing methods in several real data sets.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2ee8877e26055f2561e9dcee2f2c388a89d59d2c
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Citations in the corpus
- A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors (2019, methodology): “We conducted most data analysis using scCloud, a Cloud based single-cell analysis pipeline (Li et al., 2019) (BL, JG, YR, ORR and AR, Methods, Fig.”
- Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, neutral): “For each scRNA-Seq sample, cell calling was performed using ‘emptyDrops’ (Lun et al., 2019) function from DropletUtils (version 1.”
- Modular, efficient and constant-memory single-cell RNA-seq preprocessing (2021, methodology): “The DropletUtils package 33,34 was used to remove empty droplets from the kallisto bustools gene count matrix.”
- Single-cell chromatin state analysis with Signac (2021, methodology): “…of sequencing depth and assay sensitivity on the performance of different scATAC-seq dimension reduction methods, we downsampled the total number of counts per cell from 100% (full dataset) down to 80%, 60%, 40% and 20% using the downsampleMatrix function from the DropletUtils R package 67,68 .”
- Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender (2019, methodology): “In addition, CellBender detects more than 50% extra cells compared to CellRanger v3, and more than five times as many cells as EmptyDrops.”