Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
Leiden Computational Biology Center, Leiden University Medical Center, ZC Leiden, The Netherlands.
Bioinformatics. 2019 Oct 15;35(20):4063-4071. doi: 10.1093/bioinformatics/btz180.
High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel.
To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.
Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge).
Supplementary data are available at Bioinformatics online.
高维液质流式细胞术（CyTOF）允许在单细胞水平上同时测量多个细胞标记物，从而全面了解细胞成分。然而，CyTOF 要在单细胞水平上探索生物样本的全部异质性，目前受到单个面板上同时测量的标记物数量的限制。
为了增加每个细胞的标记物数量，我们提出了一种在计算机上的方法，用于整合使用多个面板测量的 CyTOF 数据集，这些面板共享一组标记物。此外，我们提出了一种从现有的 CyTOF 数据集中选择最具信息量的标记物作为面板之间共享标记物集的方法。我们通过评估两个公共 CyTOF 数据集上集成数据集的聚类质量和邻域保留，证明了我们方法的可行性。我们表明，通过计算扩展标记物的数量，我们可以进一步梳理液质流式细胞术数据的异质性，包括稀有细胞群体的检测。
该方法的实现可在 GitHub（https://github.com/tabdelaal/CyTOFmerge）上获得。
补充数据可在《Bioinformatics》在线获取。
