High-dimensional cytometry is a powerful technique for deciphering the immunopathological factors common to multiple individuals. However, rational comparisons of multiple batches of experiments performed on different occasions or at different sites are challenging because of batch effects. In this study, we describe the integration of multibatch cytometry datasets (iMUBAC), a flexible, scalable, and robust computational framework for unsupervised cell-type identification across multiple batches of high-dimensional cytometry datasets, even without technical replicates. After overlaying cells from multiple healthy controls across batches, iMUBAC learns batch-specific cell-type classification boundaries and identifies aberrant immunophenotypes in patient samples from multiple batches in a unified manner. We illustrate unbiased and streamlined immunophenotyping using both public and in-house mass cytometry and spectral flow cytometry datasets. The method is available as the R package iMUBAC (https://github.com/casanova-lab/iMUBAC).

中文翻译：

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用于优化免疫表型的多批次流式细胞术数据集成

高维细胞术是一种强大的技术，可以破译多个个体共有的免疫病理因素。然而，由于批次效应，在不同场合或不同地点进行的多批次实验的合理比较具有挑战性。在这项研究中，我们描述了多批次流式细胞术数据集 (iMUBAC) 的集成，这是一种灵活、可扩展且稳健的计算框架，用于跨多批高维细胞术数据集进行无监督细胞类型识别，即使没有技术复制。在跨批次覆盖多个健康对照的细胞后，iMUBAC 学习特定批次的细胞类型分类边界，并以统一的方式识别来自多个批次的患者样本中的异常免疫表型。我们使用公共和内部质谱流式细胞术和光谱流式细胞术数据集来说明无偏见和简化的免疫表型分析。该方法可用作 R 包 iMUBAC (https://github.com/casanova-lab/iMUBAC)。