diffcyt

diffcyt performs differential discovery analyses on high-dimensional cytometry data to identify differential protein marker expression and detect rare cell populations across experimental conditions.

Key Features:

• Data support: Supports flow cytometry, mass cytometry (CyTOF), and oligonucleotide-tagged cytometry datasets.
• Single-cell protein marker analysis: Analyzes expression of targeted protein markers measured at single-cell resolution, including panels with 40+ markers.
• High-resolution clustering: Performs high-resolution clustering to group cells by marker expression profiles for precise cell-type and state delineation.
• Empirical Bayes moderated tests: Applies empirical Bayes moderated differential tests adapted from transcriptomics to identify differential expression between conditions.
• Detection of rare populations: Enhances statistical power to detect differential signals in rare cell populations by combining clustering and moderated testing.
• Flexible experimental designs: Accommodates flexible experimental designs in differential analyses.
• Scalability and runtime: Handles large, high-dimensional cytometry datasets with fast runtimes.

Scientific Applications:

• Immunology: Characterizes immune cell types, states, and differential marker expression in immunological studies.
• Oncology: Identifies tumor-associated cellular phenotypes and differential protein expression in cancer research.
• Developmental biology: Investigates cellular heterogeneity and state transitions during development.
• Cellular heterogeneity and dynamics: Enables study of cellular heterogeneity and dynamic changes across experimental conditions or timepoints.

Methodology:

Performs high-resolution clustering of single-cell marker expression and applies empirical Bayes moderated differential tests adapted from transcriptomics, borrowing strength across comparisons.