scSNPdemux: a sensitive demultiplexing pipeline using single nucleotide polymorphisms for improved pooled single-cell RNA sequencing analysis

BMC Bioinformatics

Software

Christel Herold-Mende¹  Peter Lichter²  John K. L. Wong²  Marc Zapatka²  Martina Seiffert²  Lena Jassowicz³  Jan-Philipp Mallm⁴ 

[1] Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany;Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany;Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany;Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany;Single-Cell Open Lab, German Cancer Research Center, Heidelberg, Germany;

关键词: Single-cell;    Sample pooling;    Sample demultiplexing;    Single nucleotide polymorphisms;   

DOI  :  10.1186/s12859-023-05440-8

 received in 2023-01-11, accepted in 2023-08-08,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundHere we present scSNPdemux, a sample demultiplexing pipeline for single-cell RNA sequencing data using natural genetic variations in humans. The pipeline requires alignment files from Cell Ranger (10× Genomics), a population SNP database and genotyped single nucleotide polymorphisms (SNPs) per sample. The tool works on sparse genotyping data in VCF format for sample identification.ResultsThe pipeline was tested on both single-cell and single-nuclei based RNA sequencing datasets and showed superior demultiplexing performance over the lipid-based CellPlex and Multi-seq sample multiplexing technique which incurs additional single cell library preparation steps. Specifically, our pipeline demonstrated superior sensitivity and specificity in cell-identity assignment over CellPlex, especially on immune cell types with low RNA content.ConclusionsWe designed a streamlined pipeline for single-cell sample demultiplexing, aiming to overcome common problems in multiplexing samples using single cell libraries which might affect data quality and can be costly.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

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