We are analyzing https://link.springer.com/article/10.1186/s13059-019-1662-y.

Title:
EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data | Genome Biology
Description:
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.
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Keywords {🔍}

cells, barcodes, droplets, cell, data, emptydrops, ambient, total, rna, counts, empty, detected, point, article, method, count, methods, set, additional, dataset, singlecell, number, threshold, file, google, scholar, expression, knee, profile, small, real, barcode, pool, datasets, based, content, analysis, dropletbased, low, sequencing, large, fdr, cellranger, umi, fig, approach, droplet, cas, lun, defined,

Topics {✒️}

high-throughput single-cell transcriptomics quantitative single-cell rna-seq single-cell rna-seq data droplet-based scrna-seq data single-cell transcriptomics studies single-cell transcriptomics applied /single-cell-gene-expression/datasets single-cell transcriptomics droplet-based protocols droplet-based technologies poses innovation programme “enlight-ten” simulated droplet-based data samantha riesenfeld early maternal-fetal interface uk/publications/emptydrops2017-datafiles tallulah andrews good-turing frequency estimation human cell atlas droplet-based data identity-link factor model single-cell reconstruction single-cell transcriptomes related subjects normalized log-expression values low-level data processing cancer research uk null dirichlet-multinomial model sloan kettering institute original author article download pdf total count-based methods wellcome genome campus calculating exact p-values protocols based droplet-based datasets privacy choices/manage cookies high rna content creative commons license removed low-quality cells cell detection methods potentially represent cell ribosomal protein mrna low-level analysis low rna content quantile-based approach [3] detecting outliers based distinct cell types differential expression analysis single cell improving detection power

Questions {❓}

Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola?

Schema {🗺️}

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         description: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.
         datePublished:2019-03-22T00:00:00Z
         dateModified:2019-03-22T00:00:00Z
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            Droplet-based protocols
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            Cell detection
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            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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      headline:EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data
      description: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.
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         Single-cell transcriptomics
         Droplet-based protocols
         Empty droplets
         Cell detection
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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      name:Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
      name:Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
      name:Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
      name:EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

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