scSNPdemux: a sensitive demultiplexing pipeline using single nucleotide polymorphisms for improved pooled single-cell RNA sequencing analysis
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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
BMC bioinformatics - 24(2023), 1 vom: 31. Aug. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wong, John K. L. [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Sample demultiplexing |
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| Anmerkungen: |
© BioMed Central Ltd., part of Springer Nature 2023 |
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| doi: |
10.1186/s12859-023-05440-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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. | ||
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| 700 | 1 | |a Seiffert, Martina |4 aut | |
| 700 | 1 | |a Mallm, Jan-Philipp |4 aut | |
| 700 | 1 | |a Lichter, Peter |4 aut | |
| 700 | 1 | |a Zapatka, Marc |4 aut | |
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