Computational Biology and Bioinformatics, de duve Institute, UCLouvain, Brussels, Belgium.
GSK, Rixensart, Belgium.
BMC Bioinformatics. 2024 Feb 20;25(1):80. doi: 10.1186/s12859-024-05691-z.
With the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses. This part can in turn be split into a number of elementary steps: signal compensation or unmixing, scale transformation, debris, doublets and dead cells removal, batch effect correction, etc. However, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, non standardized diagnostic and visualizations, making objective comparison difficult for the end user.
Here, we present CytoPipeline and CytoPipelineGUI, two R packages to build, compare and assess pre-processing pipelines for flow cytometry data. To exemplify these new tools, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate different visual assessment use cases. We also set up a benchmarking comparing two pre-processing pipelines differing by their quality control methods, and show how the package visualization utilities can provide crucial user insight into the obtained benchmark metrics.
CytoPipeline and CytoPipelineGUI are two Bioconductor R packages that help building, visualizing and assessing pre-processing pipelines for flow cytometry data. They increase productivity during pipeline development and testing, and complement benchmarking tools, by providing user intuitive insight into benchmarking results.
近年来，流式细胞术数据的维度不断增加，因此越来越需要用自动化数据分析管道来替代或补充传统的手动分析（即迭代二维门控）。这些管道的一个关键部分包括对原始数据进行预处理和应用质量控制过滤，以便在下游分析中使用高质量的事件。这部分可以进一步细分为许多基本步骤：信号补偿或解混，比例变换，碎片，双细胞和死细胞去除，批次效应校正等。然而，由于以下原因，组装和评估预处理部分可能具有挑战性。首先，所涉及的基本步骤中的每一个都可以使用各种方法和 R 包来实现。其次，步骤的顺序会对下游分析结果产生影响。最后，每种方法通常都有其特定的、非标准化的诊断和可视化，使得最终用户难以进行客观比较。
在这里，我们介绍了 CytoPipeline 和 CytoPipelineGUI，这两个 R 包可用于构建、比较和评估流式细胞术数据的预处理管道。为了举例说明这些新工具，我们展示了在真实数据集上设计预处理管道所涉及的步骤，并演示了不同的可视化评估用例。我们还设置了一个基准测试，比较了两种预处理管道，它们的质量控制方法不同，并展示了包可视化实用程序如何为获得的基准指标提供关键的用户洞察。
CytoPipeline 和 CytoPipelineGUI 是两个 Bioconductor R 包，可帮助构建、可视化和评估流式细胞术数据的预处理管道。它们通过为基准测试结果提供用户直观的洞察，提高了管道开发和测试过程中的生产力，并补充了基准测试工具。
