单细胞多组学预测和整合的基准算法。

IF 36.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Nature Methods Pub Date : 2024-09-25 DOI:10.1038/s41592-024-02429-w
Yinlei Hu, Siyuan Wan, Yuanhanyu Luo, Yuanzhe Li, Tong Wu, Wentao Deng, Chen Jiang, Shan Jiang, Yueping Zhang, Nianping Liu, Zongcheng Yang, Falai Chen, Bin Li, Kun Qu
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引用次数: 0

摘要

单细胞多组学技术的发展极大地促进了我们对生物学的理解,与此同时,人们也提出了许多算法来预测单细胞转录组信息中蛋白质的丰度和/或细胞染色质的可及性,以及整合各种类型的单细胞多组学数据。然而,很少有研究系统地比较和评估这些算法的性能。在此,我们利用 47 个单细胞多组学数据集对 14 种蛋白质丰度/染色质可及性预测算法和 18 种单细胞多组学整合算法进行了基准研究。我们的基准研究表明,在预测蛋白质丰度方面,totalVI 和 scArches 的总体表现优于其他算法,而在预测染色质可及性方面,LS_Lab 在大多数情况下是表现最好的算法。Seurat、MOJITOO 和 scAI 成为垂直整合的领先算法,而 totalVI 和 UINMF 则在水平整合和镶嵌整合场景中表现出色。此外,我们还提供了一个管道,帮助研究人员选择最佳的多组学预测和整合算法。
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Benchmarking algorithms for single-cell multi-omics prediction and integration
The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm. This Analysis study compares computational methods for single-cell multi-omics prediction and integration, generating useful insights for method users and developers working with different analysis purposes and biological problems.
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来源期刊
Nature Methods
Nature Methods 生物-生化研究方法
CiteScore
58.70
自引率
1.70%
发文量
326
审稿时长
1 months
期刊介绍: Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
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