Comprehensive multivariate evaluation of the effects on cell phenotypes in multicolor flow cytometry data using ANOVA simultaneous component analysis

Abstract

This work proposes an approach to assess the effects observed in multicolor flow cytometry (MFC) experiments, for all markers and experimental factors simultaneously. It achieves this end by extending ANOVA simultaneous component analysis (ASCA), a multivariate version of ANOVA, to flow cytometry data. It is based on an initial multiset PCA model to describe the main variation patterns of cell marker expression, followed by an ASCA model on the histograms built from these PCA scores. This approach allows for determining the variations in cell phenotype distribution that are related to the experimental design. On a data set from a study of the immune response to prolonged physical exercise, the proposed method computed the effect size and statistical significance of all the experimental factors and their interactions. Most notably, it provided easily interpretable submodels for the overall effect of the walking exercise and for the interaction between exercise and the responsiveness to a bacterial stimulus. The application of a time-guided sequential clustering algorithm to the ASCA scores revealed a stratification of the studied individuals based on their neutrophil activation dynamics. These effects were not clearly detectable using PCA alone. In comparison with pairwise classification models by DAMACY (a discriminant analysis method for MFC data), ASCA results were less detailed in describing differences between specific samples, but had the advantage of modeling several factors and levels simultaneously. Such characteristics make the proposed implementation of ASCA an effective and complementary addition to the chemometric methodologies for the analysis of MFC data.