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Updated: May 7, 2026
Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
SNACS: a tool for demultiplexing single-cell DNA sequencing data
Vanessa E Kennedy1, Ritu Roy2, Cheryl A C Peretz2,3
1Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford University, Stanford, CA, 94304, United States.
Motivation:
Single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have recently provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.
Results:
Here, we introduce SNACS: single-nucleotide polymorphism 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. Using SNACS, accuracy ranged from 0.948 to 0.991 versus 0.552 to 0.934 using demultiplexing methods from the single-cell literature.
Availability And Implementation:
SNACS is available at https://github.com/olshena/SNACS.
