scDemultiplex: An iterative beta-binomial model-based method for accurate demultiplexing with hashtag oligos

Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.

Automated summary

Single-cell sequencing is widely used for studying cellular heterogeneity, and sample multiplexing is an important technique that allows for increased capacity, decreased costs, and minimized batch effects. The crucial step in analyzing multiplexed data is demultiplexing, which assigns cells to individual samples. Accurate demultiplexing is essential to avoid misleading characterization.