Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing

Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample o...

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Vydáno v:	Nature communications Ročník 11; číslo 1; s. 2704 - 8
Hlavní autoři:	Farouni, Rick, Djambazian, Haig, Ferri, Lorenzo E., Ragoussis, Jiannis, Najafabadi, Hamed S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 01.06.2020 Nature Publishing Group Nature Portfolio
Témata:	38/91 631/114/2415 631/208/514/1949 Datasets Droplets Gene expression Gene sequencing Humanities and Social Sciences Mathematical models multidisciplinary Multiplexing Probabilistic inference Purging Ribonucleic acid RNA Samples Science Science (multidisciplinary) Statistical analysis Statistical methods Statistical models
ISSN:	2041-1723, 2041-1723
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Abstract	Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003–0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules — the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data. Sample index hopping results in various artefacts in multiplexed scRNA-seq experiments. Here, the authors introduce a statistical model to estimate sample index hopping rate in droplet-based scRNA-seq data and show that artifacts can be corrected by purging phantom molecules from the data.
AbstractList	Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003–0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules — the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data. Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003–0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules — the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data.Sample index hopping results in various artefacts in multiplexed scRNA-seq experiments. Here, the authors introduce a statistical model to estimate sample index hopping rate in droplet-based scRNA-seq data and show that artifacts can be corrected by purging phantom molecules from the data. Sample index hopping results in various artefacts in multiplexed scRNA-seq experiments. Here, the authors introduce a statistical model to estimate sample index hopping rate in droplet-based scRNA-seq data and show that artifacts can be corrected by purging phantom molecules from the data. Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003–0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules — the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data. Sample index hopping results in various artefacts in multiplexed scRNA-seq experiments. Here, the authors introduce a statistical model to estimate sample index hopping rate in droplet-based scRNA-seq data and show that artifacts can be corrected by purging phantom molecules from the data. Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003-0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules - the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data.Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003-0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules - the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data.
ArticleNumber	2704
Author	Djambazian, Haig Ragoussis, Jiannis Najafabadi, Hamed S. Farouni, Rick Ferri, Lorenzo E.
Author_xml	– sequence: 1 givenname: Rick orcidid: 0000-0002-3454-2946 surname: Farouni fullname: Farouni, Rick email: tarek.farouni@mcgill.ca organization: Department of Human Genetics, McGill University, McGill University Genome Centre – sequence: 2 givenname: Haig orcidid: 0000-0003-0222-4182 surname: Djambazian fullname: Djambazian, Haig organization: Department of Human Genetics, McGill University, McGill University Genome Centre – sequence: 3 givenname: Lorenzo E. surname: Ferri fullname: Ferri, Lorenzo E. organization: Department of Surgery, McGill University – sequence: 4 givenname: Jiannis orcidid: 0000-0002-8515-0934 surname: Ragoussis fullname: Ragoussis, Jiannis organization: Department of Human Genetics, McGill University, McGill University Genome Centre – sequence: 5 givenname: Hamed S. orcidid: 0000-0003-2735-4231 surname: Najafabadi fullname: Najafabadi, Hamed S. email: hamed.najafabadi@mcgill.ca organization: Department of Human Genetics, McGill University, McGill University Genome Centre
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Cites_doi	10.1101/125724 10.1186/s12864-018-4703-0 10.1038/s41586-018-0590-4 10.1038/s41467-017-02001-5 10.1038/s41467-018-05083-x 10.1186/s12864-017-4428-5 10.1038/nmeth.4666 10.1186/s13059-019-1662-y
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References	MacConaillLEUnique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencingBMC Genomics20181910.1186/s12864-017-4428-5 Illumina. Effects of index misassignment on multiplexing and downstream analysis. https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf (2017), accessed 16 Apr. 2020. LunATLEmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing dataGenome Biol.20192010.1186/s13059-019-1662-y BachKDifferentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencingNat. Commun.201782017NatCo...8.2128B10.1038/s41467-017-02001-5 The Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature562, 367–372 (2018). GriffithsJARichardACBachKLunATMarioniJCDetection and removal of barcode swapping in single-cell RNA-seq dataNat. Commun.201892018NatCo...9.2667G10.1038/s41467-018-05083-x Sinha, R. et al. Index switching causes ‘spreading-of-signal’ among multiplexed samples in Illumina HiSeq 4000 DNA sequencing. https://www.biorxiv.org/content/10.1101/125724v1 (2017). LarssonAJStanleyGSinhaRWeissmanILSandbergRComputational correction of index switching in multiplexed sequencing librariesNat. Methods2018153053071:CAS:528:DC%2BC1cXosVClsLg%3D10.1038/nmeth.4666 CostelloMCharacterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platformsBMC Genomics20181910.1186/s12864-018-4703-0 16522_CR1 16522_CR4 16522_CR8 AJ Larsson (16522_CR5) 2018; 15 K Bach (16522_CR7) 2017; 8 ATL Lun (16522_CR9) 2019; 20 LE MacConaill (16522_CR3) 2018; 19 M Costello (16522_CR2) 2018; 19 JA Griffiths (16522_CR6) 2018; 9
References_xml	– reference: Sinha, R. et al. Index switching causes ‘spreading-of-signal’ among multiplexed samples in Illumina HiSeq 4000 DNA sequencing. https://www.biorxiv.org/content/10.1101/125724v1 (2017). – reference: CostelloMCharacterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platformsBMC Genomics20181910.1186/s12864-018-4703-0 – reference: Illumina. Effects of index misassignment on multiplexing and downstream analysis. https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf (2017), accessed 16 Apr. 2020. – reference: MacConaillLEUnique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencingBMC Genomics20181910.1186/s12864-017-4428-5 – reference: BachKDifferentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencingNat. Commun.201782017NatCo...8.2128B10.1038/s41467-017-02001-5 – reference: LarssonAJStanleyGSinhaRWeissmanILSandbergRComputational correction of index switching in multiplexed sequencing librariesNat. Methods2018153053071:CAS:528:DC%2BC1cXosVClsLg%3D10.1038/nmeth.4666 – reference: The Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature562, 367–372 (2018). – reference: GriffithsJARichardACBachKLunATMarioniJCDetection and removal of barcode swapping in single-cell RNA-seq dataNat. Commun.201892018NatCo...9.2667G10.1038/s41467-018-05083-x – reference: LunATLEmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing dataGenome Biol.20192010.1186/s13059-019-1662-y – ident: 16522_CR1 doi: 10.1101/125724 – volume: 19 year: 2018 ident: 16522_CR2 publication-title: BMC Genomics doi: 10.1186/s12864-018-4703-0 – ident: 16522_CR8 doi: 10.1038/s41586-018-0590-4 – volume: 8 year: 2017 ident: 16522_CR7 publication-title: Nat. Commun. doi: 10.1038/s41467-017-02001-5 – volume: 9 year: 2018 ident: 16522_CR6 publication-title: Nat. Commun. doi: 10.1038/s41467-018-05083-x – volume: 19 year: 2018 ident: 16522_CR3 publication-title: BMC Genomics doi: 10.1186/s12864-017-4428-5 – volume: 15 start-page: 305 year: 2018 ident: 16522_CR5 publication-title: Nat. Methods doi: 10.1038/nmeth.4666 – volume: 20 year: 2019 ident: 16522_CR9 publication-title: Genome Biol. doi: 10.1186/s13059-019-1662-y – ident: 16522_CR4
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Title	Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
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