多路复用
聚类分析
水准点(测量)
工作流程
联营
计算机科学
规范化(社会学)
样品(材料)
数据挖掘
生物
计算生物学
人工智能
数据库
色谱法
电信
化学
大地测量学
社会学
人类学
地理
作者
Mohammed Sayed,Yue J. Wang,Hee‐Woong Lim
出处
期刊:Briefings in Functional Genomics
[Oxford University Press]
日期:2024-09-25
摘要
Abstract Single-cell technology opened up a new avenue to delineate cellular status at a single-cell resolution and has become an essential tool for studying human diseases. Multiplexing allows cost-effective experiments by combining multiple samples and effectively mitigates batch effects. It starts by giving each sample a unique tag and then pooling them together for library preparation and sequencing. After sequencing, sample demultiplexing is performed based on tag detection, where cells belonging to one sample are expected to have a higher amount of the corresponding tag than cells from other samples. However, in reality, demultiplexing is not straightforward due to the noise and contamination from various sources. Successful demultiplexing depends on the efficient removal of such contamination. Here, we perform a systematic benchmark combining different normalization methods and demultiplexing approaches using real-world data and simulated datasets. We show that accounting for sequencing depth variability increases the separability between tagged and untagged cells, and the clustering-based approach outperforms existing tools. The clustering-based workflow is available as an R package from https://github.com/hwlim/hashDemux.
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