cytofkit

cytofkit analyzes mass cytometry (CyTOF) single-cell data to perform dimensionality reduction, automated clustering, subset discovery, and regression-based progression analysis for exploring cellular heterogeneity and dynamics.

Key Features:

• Data Preprocessing: Normalization and transformation of raw mass cytometry data to ensure consistency across samples.
• Dimensionality Reduction: Application of methods such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to project high-dimensional marker data into lower-dimensional space.
• Automated Clustering: Identification of distinct cell populations using automated clustering approaches, possibly employing algorithms like k-means or hierarchical clustering.
• Subset Discovery: Discovery and characterization of novel cellular subsets based on marker expression, including subsets defined by interferon-γ (IFN-γ), interleukin-17 (IL-17), or Foxp3 expression.
• Cellular Progression Analysis: Use of regression analysis to model changes in cell populations over time or across biological conditions.

Scientific Applications:

• T helper (T(H)) and follicular helper T (T(FH)) cell analysis: Analysis of peripheral blood and tonsil samples to reveal a continuum of T(FH) subtypes expressing interferon-γ (IFN-γ), interleukin-17 (IL-17), or Foxp3, and to identify CXCR5(low)PD-1(low/−)CCR7(low) T(FH) cells consistent with memory T(FH) accumulation during childhood.

Methodology:

Data preprocessing (normalization and transformation), dimensionality reduction (PCA, t-SNE), automated clustering (possibly k-means or hierarchical clustering), and regression analysis.