Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance

• Published in: Nature communications Vol. 10; no. 1; pp. 3908 - 14
• Main Authors: Luquette, Lovelace J., Bohrson, Craig L., Sherman, Max A., Park, Peter J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.08.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 45/23; 631/114/2415; 631/61/212; Algorithms; Alleles; Allelic Imbalance; Amplification; Base Sequence; Chromosomes, Human, X; Deoxyribonucleic acid; DNA; DNA sequencing; Gene Frequency; Genes, Neoplasm; Genomes; Genotype; Heterogeneity; Humanities and Social Sciences; Humans; Male; Models, Genetic; multidisciplinary; Mutation; Neoplasms - genetics; Nucleotides; Polymorphism, Single Nucleotide; Science; Science (multidisciplinary); Single-Cell Analysis - methods; Single-nucleotide polymorphism; Whole Genome Sequencing
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-11857-8

Recent advances in single cell technology have enabled dissection of cellular heterogeneity in great detail. However, analysis of single cell DNA sequencing data remains challenging due to bias and artifacts that arise during DNA extraction and whole-genome amplification, including allelic imbalance and dropout. Here, we present a framework for statistical estimation of allele-specific amplification imbalance at any given position in single cell whole-genome sequencing data by utilizing the allele frequencies of heterozygous single nucleotide polymorphisms in the neighborhood. The resulting allelic imbalance profile is critical for determining whether the variant allele fraction of an observed mutation is consistent with the expected fraction for a true variant. This method, implemented in SCAN-SNV (Single Cell ANalysis of SNVs), substantially improves the identification of somatic variants in single cells. Our allele balance framework is broadly applicable to genotype analysis of any variant type in any data that might exhibit allelic imbalance. Single cell whole-genome sequencing data harbors information about somatic genetic variation but is challenging to analyze. Here, the authors develop a spatial model to correct for allelic amplification imbalance and a somatic SNV genotyper SCAN-SNV for analyzing single cell DNA sequencing data.