demuxSNP: supervised demultiplexing scRNAseq using cell hashing and SNPs

Abstract

AbstractBackgroundMultiplexing single-cell RNA sequencing experiments reduces sequencing cost and facilitates larger scale studies. However, factors such as cell hashing quality and class size imbalance impact demultiplexing algorithm performance, reducing cost effectivenessFindingsWe propose a supervised algorithm, demuxSNP, leveraging both cell hashing and genetic variation between individuals (SNPs). The supervised algorithm addresses fundamental limitations in demultiplexing with only one data modality. The genetic variants (SNPs) of the subset of cells assigned with high confidence using a probabilistic hashing algorithm are used to train a KNN classifier that predicts the demultiplexing classes of unassigned or uncertain cells. We benchmark demuxSNP against hashing (HTODemux, cellhashR, GMM-demux, demuxmix) and genotype-free SNP (souporcell) methods on simulated and real data from renal cell cancer. Our results demonstrate that demuxSNP outperformed standalone hashing methods on low quality hashing data, improving overall classification accuracy and allowing more high RNA quality cells to be recovered. Through varying simulated doublet rates, we show genotype-free SNP methods are unable to identify biological samples with low cell counts at high doublet rates. When compared to unsupervised SNP demultiplexing methods, demuxSNP’s supervised approach was more robust to doublet rate in experiments with class size imbalance.ConclusionsdemuxSNP is a performant demultiplexing approach that uses hashing and SNP data to demultiplex datasets with low hashing quality where biological samples are genetically distinct. Unassigned cells (negatives) with high RNA quality can be recovered, making more cells available for analysis, especially when applied to data with low hashing quality or suspected misassigned cells. Pipelines for simulated data and processed benchmarking data for 5-50% doublets are publicly available. demuxSNP is available as an R/Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.demuxSNP).