Transformation of multicolour flow cytometry data with OTflow prevents misleading multivariate analysis results and incorrect immunological conclusions,Cytometry Part A

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Transformation of multicolour flow cytometry data with OTflow prevents misleading multivariate analysis results and incorrect immunological conclusions
Cytometry Part A ( IF 2.5 ) Pub Date : 2021-07-29 , DOI: 10.1002/cyto.a.24491
Rita Folcarelli ₁ , Selma van Staveren _{2,

3} , Gerjen Tinnevelt _{1,

3} , Emily Cadot ₁ , Nienke Vrisekoop ₂ , Lutgarde Buydens ₁ , Leo Koenderman ₂ , Jeroen Jansen ₁ , Oscar F van den Brink ₃

Affiliation

The rapid evolution of the flow cytometry field, currently allowing the measurement of 30–50 parameters per cell, has led to a marked increase in deep multivariate information. Manual gating is insufficient to extract all this information. Therefore, multivariate analysis (MVA) methods have been developed to extract information and efficiently analyze the high-density multicolour flow cytometry (MFC) data. To aid interpretation, MFC data are often logarithmically transformed before MVA. We studied the consequences of different transformations of flow cytometry data in datasets containing negative intensities caused by background subtractions and spreading error, as logarithmic transformation of negative data is impossible. Transformations such as logicle or hyperbolic arcsine transformations allow linearity around zero, whereas higher (positive and negative) intensities are logarithmically transformed. To define the linear range, a parameter (or cofactor) must be chosen. We show how the chosen transformation parameter has great impact on the MVA results. In some cases, peak splitting is observed, producing two distributions around zero in an actual homogeneous population. This may be misinterpreted as the presence of multiple cell populations. Moreover, when performing arbitrary transformation before MVA analysis, biologically relevant and statistically significant information might be missed. We present a new algorithm, Optimal Transformation for flow cytometry data (OTflow), which uses various statistical methods to optimally choose the parameter of the transformation and prevent artifacts such as peak splitting. Arbitrary or unconsidered transformation can lead to wrong conclusions for the MVA cluster methods, dimensionality reduction methods, and classification methods. We recommend transformation of flow cytometry data by using OTflow-defined parameters estimated per channel, in order to prevent peak splitting and other artifacts in the data.

中文翻译：

使用 OTflow 转换多色流式细胞仪数据可防止误导性多变量分析结果和不正确的免疫学结论

流式细胞术领域的快速发展，目前允许每个细胞测量 30-50 个参数，导致深度多元信息显着增加。手动门控不足以提取所有这些信息。因此，已经开发了多变量分析（MVA）方法来提取信息并有效地分析高密度多色流式细胞术（MFC）数据。为了帮助解释，MFC 数据通常在 MVA 之前进行对数转换。我们研究了流式细胞仪数据在包含由背景减法和传播误差引起的负强度的数据集中的不同转换的后果，因为负数据的对数转换是不可能的。诸如逻辑或双曲反正弦变换之类的变换允许零附近的线性，而更高的（正和负）强度是对数转换的。要定义线性范围，必须选择一个参数（或辅因子）。我们展示了选择的转换参数如何对 MVA 结果产生重大影响。在某些情况下，观察到峰分裂，在实际同质种群中产生两个大约为零的分布。这可能被误解为存在多个细胞群。此外，在 MVA 分析之前执行任意转换时，可能会遗漏生物学相关和统计学上显着的信息。我们提出了一种新算法，流式细胞仪数据 (OTflow) 的优化转换，它使用各种统计方法来优化转换参数并防止峰值分裂等伪影。任意或未考虑的变换可能导致对 MVA 聚类方法、降维方法和分类方法的错误结论。我们建议使用每个通道估计的 OTflow 定义的参数来转换流式细胞仪数据，以防止数据中出现峰分裂和其他伪影。

更新日期：2021-07-29

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