EmptyDrops: Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data Journal Article
| Authors: | Lun, A. T. L.; Riesenfeld, S.; Andrews, T.; Dao, T. P.; Gomes, T.; Marioni, J. C.; participants in the 1st Human Cell Atlas Jamboree; Marioni, J. C. |
| Article Title: | EmptyDrops: Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data |
| Abstract: | 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. © 2019 The Author(s). |
| Keywords: | gene expression profiling; transcriptomics; simulation; statistical analysis; rna sequence; article; single-cell transcriptomics; cell detection; droplet-based protocols; empty droplets |
| Journal Title: | Genome Biology |
| Volume: | 20 |
| ISSN: | 1465-6906 |
| Publisher: | Biomed Central Ltd |
| Date Published: | 2019-03-22 |
| Start Page: | 63 |
| Language: | English |
| DOI: | 10.1186/s13059-019-1662-y |
| PUBMED: | 30902100 |
| PROVIDER: | scopus |
| PMCID: | PMC6431044 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 May 2019 -- Source: Scopus |
