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

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Introduction 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 analyse the high-density multicolour flow cytometry (MFC) data. To aid interpretation, MFC data are often logarithmically transformed before MVA. Rationale 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. Results 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 0 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. Conclusion 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.

This publication has 31 references indexed in Scilit: