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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell sequencing is crucial for understanding cellular heterogeneity.
• Sample multiplexing enhances single-cell experiment capacity, reduces costs, and minimizes batch effects.
• Accurate demultiplexing is essential for reliable analysis of multiplexed single-cell data.

Purpose of the Study:

• To develop a robust computational tool for demultiplexing multiplexed single-cell sequencing data.
• To improve the accuracy of cell-to-sample assignment in pooled single-cell experiments.
• To provide a method that enhances the characterization of cellular heterogeneity.

Main Methods:

• Proposed scDemultiplex, a novel computational approach for demultiplexing.
• Modeled hashtag oligo (HTO) counts using a beta-binomial distribution.
• Employed an iterative refinement strategy for enhanced demultiplexing accuracy.

Main Results:

• scDemultiplex demonstrated superior performance compared to seven existing methods.
• Achieved high accuracy in demultiplexing both high-quality and low-quality datasets.
• Showcased the potential for scDemultiplex to improve existing demultiplexing approaches.

Conclusions:

• scDemultiplex offers a significant advancement in analyzing multiplexed single-cell data.
• The method provides reliable cell assignment, crucial for accurate biological interpretation.
• scDemultiplex can be integrated with other tools to boost overall performance in single-cell studies.