Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

Citations Over Time

Abstract

ABSTRACT Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot : a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

Related Papers

• → Genetic Variants of Human Parvovirus B19 in South Africa: Cocirculation of Three Genotypes and Identification of a Novel Subtype of Genotype 1(2009)50 cited
• The Relationship between CYP1A1 Genetic Polymorphism and Susceptibility to Lung Cancer(2002)
• Polymorphism analysis of CYP1A1 MspI in ovarian cancer(2008)
• ANALYSIS OF GENOTYPES AND SUBTYPES OF HEPATITIS B VIRUS IN TWO COUNTIES OF SICHUAN(2009)
• → Hepatitis C virus genotype distribution in Southern China during 2015-2016(2017)