Huang Li-Ching, Stolze Lindsey K, Chen Hua-Chang, Gelbard Alexander, Shyr Yu, Liu Qi, Sheng Quanhu
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Comput Struct Biotechnol J. 2023 Aug 19;21:4044-4055. doi: 10.1016/j.csbj.2023.08.013. eCollection 2023.
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.
单细胞测序已被广泛用于表征细胞异质性。样本多路复用是将多个样本汇集在一起进行单细胞实验，因其具有增加通量、降低成本和最小化批次效应等优点而备受关注。要分析多路复用数据，第一个关键步骤是解复用，即将细胞分配到各个样本的过程。不准确的解复用会产生错误的细胞类型并导致误导性的表征。我们提出了scDemultiplex，它用β-二项分布对标签寡核苷酸（HTO）计数进行建模，并使用迭代策略进行进一步优化。与现有的七种解复用方法相比，scDemultiplex在高质量和低质量数据中均表现出色。此外，scDemultiplex可以与其他方法结合以提高其性能。
