标杆管理
计算机科学
聚类分析
规范化(社会学)
数据挖掘
人工智能
水准点(测量)
大地测量学
人类学
社会学
业务
营销
地理
作者
Tian Li,Xueyi Dong,Saskia Freytag,Kim‐Anh Lê Cao,Shian Su,Abolfazl JalalAbadi,Daniela Amann‐Zalcenstein,Tom S. Weber,Azadeh Seidi,Jafar S. Jabbari,Shalin H. Naik,Matthew E. Ritchie
出处
期刊:Nature Methods
[Springer Nature]
日期:2019-05-27
卷期号:16 (6): 479-487
被引量:260
标识
DOI:10.1038/s41592-019-0425-8
摘要
Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create ‘pseudo cells’ from up to five distinct cancer cell lines. In total, 14 datasets were generated using both droplet and plate-based scRNA-seq protocols. We compared 3,913 combinations of data analysis methods for tasks ranging from normalization and imputation to clustering, trajectory analysis and data integration. Evaluation revealed pipelines suited to different types of data for different tasks. Our data and analysis provide a comprehensive framework for benchmarking most common scRNA-seq analysis steps. A dataset made up of single cancer cells or their mixtures serves as a benchmark for testing almost 4,000 combinations of scRNA-seq data analysis methods.
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