Abstract
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. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. 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-subject 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 CytoGLMMR package and workflow for both strategies on a pregnancy dataset.ConclusionOur approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.
Highlights
Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells
Besides comparisons on paired samples, where samples are available for the same subject under different experimental conditions, our CytoGLMM is applicable to unpaired samples, where samples are collected on two separate groups of individuals
Our findings suggest that CytoGLMM will not detect any differential expression for rare cell types with around 100 cells per sample
Summary
Flow 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. Flow [1] and mass cytometry [2] allow researchers to simultaneously assess expression patterns of a large number of proteins on individual cells, allowing deep interrogation of cellular responses. The goal of such studies is to improve our understanding of the response mechanisms on a single cell basis by defining protein expression patterns that are associated with a particular stimulus or experimental condition. The most popular differential analysis tools are: Citrus [9], the Bioconductor workflow by [10], cydar [11], CellCnn [12], and diffcyt [13]
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