CytoGLMM

CytoGLMM implements two multiple regression strategies—a bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM)—to find differential proteins in flow and mass cytometry data for a single cell type while reducing biases from marker correlations and safeguarding against false discoveries induced by patient heterogeneity.

Key Features:

• Bootstrapped GLM: Provides a bootstrapped generalized linear model for conditional differential analysis of cytometry data.
• GLMM: Provides a generalized linear mixed model that accounts for patient heterogeneity in flow and mass cytometry experiments.
• Single cell type focus: Focuses on one cell type to define a more specific statistical model with easier to control statistical guarantees.
• Bias reduction: Reduces biases arising from marker correlations in cytometry data.
• False discovery control: Safeguards against false discoveries induced by patient heterogeneity.

Scientific Applications:

• Flow cytometry differential protein analysis: Identifies differentially expressed proteins in flow cytometry data from a single cell type.
• Mass cytometry differential protein analysis: Identifies differentially expressed proteins in mass cytometry data from a single cell type.

Methodology:

CytoGLMM implements a bootstrapped generalized linear model and a generalized linear mixed model for conditional differential analysis of flow and mass cytometry experiments focused on a single cell type.