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Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

ABSTRACT: Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

PROVIDER: PRJNA1073979 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR27895255_1.fastq.gz	Fastqsanger.gz
	SRR27895255_2.fastq.gz	Fastqsanger.gz
	SRR27895256_1.fastq.gz	Fastqsanger.gz
	SRR27895256_2.fastq.gz	Fastqsanger.gz
	SRR27895257_1.fastq.gz	Fastqsanger.gz

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Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Project description:Motivation Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is frequently cost and time prohibitive. Multiplexing, in which cells from unique patients and pooled into a single experiment, offers a possible solution. While multiplexing methods are available for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need. Results Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Relative to demultiplexing methods derived from the scRNAseq literature, SNACS offered superior accuracy. Availability Implementation SNACS is available at https://github.com/olshena/SNACS.

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Project description:Recent advances in single cell RNA sequencing allow users to pool multiple samples into one run and demultiplex in downstream analysis, greatly increasing the experimental efficiency and cost-effectiveness. However, the expensive reagents for cell labeling, limited pooling capacity, non-ideal cell recovery rate and calling accuracy remain great challenges for this approach. To date, there are two major demultiplexing methods, antibody-based cell hashing and Single Nucleotide Polymorphism (SNP)-based genomic signature profiling, and each method has advantages and limitations. Here, we propose a hybrid demultiplexing strategy that increases calling accuracy and cell recovery at the same time. We first develop a computational algorithm that significantly increases calling accuracy of cell hashing. Next, we cluster all single cells based on their SNP profiles. Finally, we integrate results from both methods to make corrections and retrieve cells that are only identifiable in one method but not the other. By testing on several real-world datasets, we demonstrate that this hybrid strategy combines advantages of both methods, resulting in increased cell recovery and calling accuracy at lower cost.

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