FunPart

FunPart partitions heterogeneous cell populations and simultaneously identifies functionally relevant gene modules to resolve functional cellular states from single-cell data, particularly for studying immune responses.

Key Features:

• Partitioning Heterogeneous Cell Populations: Dissects complex cell populations into distinct functional states.
• Identification of Functionally Relevant Gene Modules: Identifies gene modules that characterize each subpopulation.
• Single-Cell-Based Computational Methodology: Employs a single-cell-based computational approach to enable high-resolution analysis of cellular heterogeneity.

Scientific Applications:

• Mapping immune cell states (Catalogus Immune Muris): Used to generate the Catalogus Immune Muris by analyzing 114 datasets from six immune cell types across 12 pathogen challenges.
• Identification of common and pathogen-specific states: Identifies both common and pathogen-specific functional states across infection contexts.
• Discovery of immunomodulatory candidates: Supports discovery of immunomodulatory candidates by characterizing previously unknown cellular states.
• Characterization of macrophage response: Enabled experimental characterization of an unknown macrophage state that modulates the response to Salmonella Typhimurium infection.

Methodology:

Compiles extensive single-cell data, partitions cells into functionally distinct states, and identifies gene modules associated with each state to create maps of cellular states across stimuli such as different pathogens.