DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data

DePasquale, Erica A. K.; Schnell, Daniel; Camp, Pieter-Jan Van; Valiente-Alandí, Íñigo; Blaxall, Burns C.; Grimes, H. Leighton; Singh, Harinder; Salomonis, Nathan

doi:10.1016/j.celrep.2019.09.082

Cited by 158 publications

(144 citation statements)

References 31 publications

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“…Examine the clustering merging heatmap that was automatically generated in conjunction with previously identified cluster labels to assess the appropriateness of merging. The example below in Figure 1 shows the result of various ρ' values in the mouse hematopoietic progenitor dataset from Figure 3 of the original DoubletDecon paper (DePasquale et al, 2019). Figure 1A shows no cluster merging, which may cause a loss of sensitivity in DoubletDecon due to the biological similarity of the HSCP-1 and HSCP-2 clusters as well as the similarity of the monocyte-dendritic cell precursor (MDP) and monocyte progenitor (Mono) clusters.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

“…min_uniqthis parameter, set to 4 by default, indicates the minimum number of genes that must be uniquely expressed per potential doublet cluster to be 'rescued' during the final step of DoubletDecon. When using the full gene list (when 'useFull' is TRUE), the 'min_uniq' value should be set to 30, which was guided by the verified mouse non-doublet evaluation dataset in the original DoubletDecon publication (see STAR Methods for more details) (DePasquale et al, 2019). These values were chosen to correct for multiple tests and reduce the risk of false positive genes labeled as uniquely expressed.…”

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confidence: 99%

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Identification and Removal of Doublets with DoubletDecon

DePasquale

Schnell

Chetal

et al. 2020

Preprint

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Retention of multiplet captures in single-cell RNA-sequencing (scRNA-seq) data can hinder identification of discrete or transitional cell populations and associated marker genes. To overcome this challenge, we created DoubletDecon to identify and remove doublets, multiplets of two cells, by using a combination of deconvolution to identify putative doublets and analyses of unique gene expression.Here we provide the protocol for running DoubletDecon on scRNA-seq data.

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Section: Step-by-step Methods Detailsmentioning

confidence: 99%

mentioning

confidence: 99%

Identification and Removal of Doublets with DoubletDecon

DePasquale

Schnell

Chetal

et al. 2020

Preprint

Self Cite

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“…Ambient RNA, from disrupted ("broken") cells may be a major source of contamination, as recently described 34 . If true, this would occur before the library preparation and sequencing steps, would cause only a modest expression of the gene, and would not be detectable by current doublet correcting mechanisms 12,35 . This method of contamination might have implications for Human Cell Atlas studies, as we demonstrated it clearly affects cross-tissue comparisons of common cell types in Tabula Muris 31,36 .…”

Section: Articlementioning

confidence: 99%

Consistent RNA sequencing contamination in GTEx and other data sets

et al. 2020

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A challenge of next generation sequencing is read contamination. We use Genotype-Tissue Expression (GTEx) datasets and technical metadata along with RNA-seq datasets from other studies to understand factors that contribute to contamination. Here we report, of 48 analyzed tissues in GTEx, 26 have variant co-expression clusters of four highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicate contamination. Sample contamination is strongly associated with a sample being sequenced on the same day as a tissue that natively expresses those genes. Discrepant SNPs across four contaminating genes validate the contamination. Low-level contamination affects ~40% of samples and leads to numerous eQTL assignments in inappropriate tissues among these 18 genes. This type of contamination occurs widely, impacting bulk and single cell (scRNA-seq) data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses.

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“…High data quality in single-cell analyses depends on the ability to discern which droplets contain single cells [31][32][33] . Previously, it has been difficult to attain predictable single-cell loading in DEs due to droplet polydispersity 39,47 .…”

Section: Device Design and Characterizationmentioning

confidence: 99%

“…This capability would enable new opportunities for single-cell analysis. First, isolating and sequencing only those droplets containing cells would dramatically lower assay costs 15 while increasing sequencing accuracy and depth [31][32][33] . Second, encapsulated cells could be isolated based on phenotypes not currently measurable with standard fluorescence-activated cell sorting 34,35 (FACS), such as enzymatic turnover, presence of secreted molecules, or quantification of proteins lacking cell surface markers 17 .…”

Section: Introductionmentioning

confidence: 99%

Double Emulsion Picoreactors for High-Throughput Single-Cell Encapsulation and Phenotyping via FACS

Brower

Khariton

Suzuki

et al. 2020

Preprint

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In the past five years, droplet microfluidic techniques have unlocked new opportunities for the high-throughput genome-wide analysis of single cells, transforming our understanding of cellular diversity and function. However, the field lacks an accessible method to screen and sort droplets based on cellular phenotype upstream of genetic analysis, particularly for large and complex cells. To meet this need, we developed Dropception, a robust, easy-to-use workflow for precise single-cell encapsulation into picoliter-scale double emulsion droplets compatible with high-throughput phenotyping via fluorescence-activated cell sorting (FACS). We demonstrate the capabilities of this method by encapsulating five standardized mammalian cell lines of varying size and morphology as well as a heterogeneous cell mixture of a whole dissociated flatworm (5 -25 µm in diameter) within highly monodisperse double emulsions (35 µm in diameter). We optimize for preferential encapsulation of single cells with extremely low multiplecell loading events (<2% of cell-containing droplets), thereby allowing direct linkage of cellular phenotype to genotype. Across all cell lines, cell loading efficiency approaches the theoretical limit with no observable bias by cell size. FACS measurements reveal the ability to discriminate empty droplets from those containing cells with good agreement to single-cell occupancies quantified via microscopy, establishing robust droplet screening at single-cell resolution. High-throughput FACS phenotyping of cellular picoreactors has the potential to shift the landscape of single-cell droplet microfluidics by expanding the repertoire of current nucleic acid droplet assays to include functional screening. Supporting InformationSupplemental Information including extended methods, a full workflow protocol, and referenced supplemental figures (SI, PDF).

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DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data

Cited by 158 publications

References 31 publications

Identification and Removal of Doublets with DoubletDecon

Identification and Removal of Doublets with DoubletDecon

Consistent RNA sequencing contamination in GTEx and other data sets

Double Emulsion Picoreactors for High-Throughput Single-Cell Encapsulation and Phenotyping via FACS

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