Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets.
Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets.
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DOI：
10.1186/s13059-023-03143-0
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发表时间：
2024-01-03
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影响因子：
12.3
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为了分析单细胞技术产生的大量数据并识别特定临床或实验结果的细胞相关性，通常应用差异丰度分析。这些算法识别出丰度因疾病进展或实验扰动而发生显着变化的细胞亚群。尽管差异丰度分析在识别关键细胞状态方面很有效，但目前还没有系统的基准研究来比较它们在单细胞模式实践中的适用性、有用性和准确性。在这里，我们进行了全面的基准测试研究，以客观地评估和比较当前最先进的差异丰度测试方法的优点和潜在缺点。我们使用合成和真实的单细胞数据集，对几种实际任务的六种单细胞测试方法进行了基准测试。评估的任务包括识别真正差异丰度亚群的有效性、充分处理批次效应的准确性、运行时效率以及超参数可用性和鲁棒性。基于各种评估结果，本文针对差异丰度测试方法的实际使用给出了针对数据集的建议。基于我们的基准测试研究，我们为实践中单细胞 DA 测试方法的最佳使用提供了一系列建议，特别是关于技术噪声的存在（例如批次效应）、数据集大小和超参数敏感性等因素。在线版本包含可在 10.1186/s13059-023-03143-0 获取的补充材料。
To analyze the large volume of data generated by single-cell technologies and to identify cellular correlates of particular clinical or experimental outcomes, differential abundance analyses are often applied. These algorithms identify subgroups of cells whose abundances change significantly in response to disease progression, or to an experimental perturbation. Despite the effectiveness of differential abundance analyses in identifying critical cell-states, there is currently no systematic benchmarking study to compare their applicability, usefulness, and accuracy in practice across single-cell modalities. Here, we perform a comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art differential abundance testing methods. We benchmarked six single-cell testing methods on several practical tasks, using both synthetic and real single-cell datasets. The tasks evaluated include effectiveness in identifying true differentially abundant subpopulations, accuracy in the adequate handling of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the practical use of differential abundance testing approaches. Based on our benchmarking study, we provide a set of recommendations for the optimal usage of single-cell DA testing methods in practice, particularly with respect to factors such as the presence of technical noise (for example batch effects), dataset size, and hyperparameter sensitivity. The online version contains supplementary material available at 10.1186/s13059-023-03143-0.