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

Li Ching Huang, Lindsey K. Stolze, Hua Chang Chen, Alexander Gelbard, Yu Shyr, Qi Liu, Quanhu Sheng

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)4044-4055
Number of pages12
JournalComputational and Structural Biotechnology Journal
Volume21
DOIs
Publication statusPublished - 2023 Jan

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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