SPICE

SPICE performs statistical analysis and presentation of integrated cellular expression on multivariate polychromatic flow cytometry datasets, enabling aggregate analysis and comparison of cell subset distributions across specimens and categorical variables such as demographic information and supporting large FLOWJO datasets.

Key Features:

• Aggregate Analysis: Facilitates examination and comparison of multivariate flow cytometry data across multiple specimens and categorical groupings.
• FLOWJO Data Handling: Supports input and aggregate analysis of large FLOWJO datasets generated from polychromatic flow cytometry experiments.
• Data Normalization and Comparison: Provides options for normalized and unnormalized data representation to evaluate effects of normalization on analyses.
• Thresholding Algorithms: Implements thresholding algorithms to determine component inclusion for visual representations and comparisons.
• Nonparametric Statistical Comparison: Defines and uses a nonparametric statistic for testing differences between complex multicomponent distributions.
• Handling Background Noise and Averaging Effects: Addresses the impact of averaging samples with significant background noise to mitigate skewed results.

Scientific Applications:

• T cell functional profile analysis: Analyzes and compares multifunctional T cell response distributions derived from polychromatic flow cytometry.
• Immunology: Enables comparative analysis of immune cell populations and functional subsets across specimens and cohorts.
• Cancer Research: Supports study of tumor-associated immune responses and immune subset distributions in tumor microenvironments.
• Infectious Diseases: Facilitates characterization and comparison of pathogen-specific immune responses measured by flow cytometry.

Methodology:

Uses thresholding algorithms, offers normalized and unnormalized data analysis options, addresses effects of averaging with background noise, and employs a defined nonparametric statistic for comparison of complex multicomponent distributions.