CytoGLMM

CytoGLMM performs conditional differential analysis of protein marker expression in flow and mass cytometry data in R, focusing on fixed cell types while accounting for marker correlations and inter-individual heterogeneity.

Key Features:

• Cell-specific differential analysis: Performs differential testing within a fixed cell type rather than jointly learning cell types and differences.
• Statistical strategies: Implements two multiple-regression strategies—a bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM)—to address marker correlations and inter-individual heterogeneity.
• Robustness and power: Comparative analyses on simulated datasets indicate both strategies maintain the target false discovery rate under medium marker correlation, with the GLMM showing increased statistical power when the model is correctly specified.
• Paired and unpaired experiment support: Demonstrates robustness to marker correlations in paired designs and highlights the need for larger patient sample sizes in unpaired designs to detect significant differences.
• Workflow illustration: Includes a reproducible workflow demonstrating implementation of both statistical strategies, exemplified by an analysis of a pregnancy dataset.

Scientific Applications:

• Human immunology: Detects cell-type-specific differential protein expression in flow and mass cytometry studies of immune responses while mitigating biases from marker correlations and patient heterogeneity.
• Single-cell protein expression analysis: Supports identification of nuanced changes in protein markers at the single-cell level within fixed cell populations.

Methodology:

Implements bootstrapped GLM and GLMM multiple-regression strategies; evaluates performance using simulated datasets; provides a workflow applied to a pregnancy cytometry dataset.