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scSNPdemux:一种使用单核苷酸多态性提高pooled single-cell RNA sequencing 分析灵敏度的解复用方法。

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

机构信息

Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.

Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.

出版信息

BMC Bioinformatics. 2023 Aug 31;24(1):326. doi: 10.1186/s12859-023-05440-8.

Abstract

BACKGROUND

Here 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.

RESULTS

The 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.

CONCLUSIONS

We 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.

摘要

背景

本文介绍了 scSNPdemux,这是一种用于单细胞 RNA 测序数据的样本解复用管道,利用了人类自然遗传变异。该管道需要来自 Cell Ranger(10× Genomics)的对齐文件、人群 SNP 数据库和每个样本的基因分型单核苷酸多态性(SNP)。该工具使用稀疏的 VCF 格式的基因分型数据进行样本识别。

结果

该管道在单细胞和基于单个核的 RNA 测序数据集上进行了测试,在样本多路复用技术上表现出优于基于脂质的 CellPlex 和 Multi-seq 的优越的解复用性能,后者需要额外的单细胞文库制备步骤。具体来说,我们的管道在细胞身份分配方面表现出优于 CellPlex 的更高的灵敏度和特异性,特别是在 RNA 含量低的免疫细胞类型上。

结论

我们设计了一种用于单细胞样本解复用的简化管道,旨在克服使用单细胞文库多路复用样本的常见问题,这些问题可能会影响数据质量并且成本高昂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccf1/10469441/9eea323dea69/12859_2023_5440_Fig1_HTML.jpg

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