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The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data.

Original publication

DOI

10.1016/j.crmeth.2021.100008

Type

Journal article

Journal

Cell Rep Methods

Publication Date

24/05/2021

Volume

1

Keywords

ADT, B cell receptor, CITE-seq, T cell receptor, doublets, multi-omics profiling, single-cell transcriptomics