Arevalo John, Su Ellen, van Dijk Robert, Carpenter Anne E, Singh Shantanu
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
bioRxiv. 2024 Feb 28:2023.09.15.558001. doi: 10.1101/2023.09.15.558001.
High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.
基于图像的高通量分析平台是强大的技术，能够以经济高效的方式从数十亿个受到数千种干扰的细胞中收集数据。因此，基于图像的分析数据越来越多地用于各种生物学应用，例如预测药物作用机制或基因功能。然而，批次效应严重限制了社区范围内整合和解释跨不同实验室和设备收集的基于图像的分析数据的努力。为了解决这个问题，我们使用新发布的细胞绘画数据集（最大的可公开获取的基于图像的数据集）对代表不同方法的七种高性能单细胞RNA测序批次校正技术进行了基准测试。我们专注于五个不同复杂程度的场景，发现基于混合模型的方法Harmony始终优于其他测试方法。我们提出的框架、基准和指标还可用于未来评估新的批次校正方法。总体而言，这项工作为改进铺平了道路，使社区能够充分利用公共细胞绘画数据进行科学发现。
