{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Heaton H"],"funding":["British Heart Foundation","Medical Research Council","National Institute for Health Research (NIHR)","Wellcome Trust"],"pagination":["615-620"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7617080"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(6)"],"pubmed_abstract":["Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios."],"journal":["Nature methods"],"pubmed_title":["Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes."],"pmcid":["PMC7617080"],"funding_grant_id":["206194","207492","RG/18/13/33946","HDR-9004","WT098051","G1100339","MR/L003120/1","WT098503","RG/13/13/30194","206194/Z/17/Z","WT207492","RG/13/13/30194; RG/18/13/33946","098051"],"pubmed_authors":["Talman AM","Heaton H","Imaz M","Gaffney DJ","Lawniczak MKN","Knights A","Hemberg M","Durbin R"],"additional_accession":[]},"is_claimable":false,"name":"Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes.","description":"Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jun","modification":"2026-06-02T22:02:36.449Z","creation":"2025-04-06T22:40:48.731Z"},"accession":"S-EPMC7617080","cross_references":{"pubmed":["32366989"],"doi":["10.1038/s41592-020-0820-1"]}}