CytoGLMM: Conditional Differential Analysis for Flow and Mass Cytometry Experiments
BackgroundFlow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. We focus on cell-specific differential analysis and one fixed cell type. In contrast, most current methods learn cell types and perform differential analysis jointly. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. ResultsDifferential analysis of marker expressions can be difficult due to marker correlations and inter-individual heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: A bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the CytoGLMM R package and workflow for both strategies on a pregnancy dataset. ConclusionsOur approach to find differential proteins in flow and mass cytometry data reduces biases arising from maker correlations and safeguards against false discoveries induced by patient heterogeneity.