scQUEST

scQUEST analyzes mass and flow cytometry single-cell data to identify and quantify tumor ecosystem heterogeneity across patient cohorts.

Key Features:

• Cell Type Identification: Precise identification of diverse cell types within tumor samples using single-cell cytometry profiles.
• Quantification of Heterogeneity: Measurement of heterogeneity across patient cohorts to capture variability and complexity of tumor ecosystems at the single-cell level.
• High-dimensional Single-cell Analysis: Processing of millions of single-cell profiles, each characterized by numerous parameters from mass or flow cytometry.
• Population Proportion Quantification: Identification of distinct cell populations and quantification of their proportions across different samples.
• Applicability to Cancer Atlases: Applied to a human breast cancer single-cell atlas generated via mass cytometry and adaptable to other single-cell cytometry datasets.

Scientific Applications:

• Tumor Ecosystem Characterization: Dissection of cellular composition and diversity within tumor microenvironments using single-cell cytometry data.
• Cohort-level Comparative Analysis: Comparative quantification of cell population variability across patient cohorts to study inter-patient heterogeneity.
• Breast Cancer Atlas Analysis: Analysis of human breast cancer single-cell mass cytometry atlases to map tumor cellular ecosystems.
• Therapeutic Research Support: Characterization of cellular interactions and diversity within tumors to inform therapeutic strategy development.

Methodology:

Processes large-scale cytometry datasets (mass or flow cytometry) by analyzing high-dimensional single-cell profiles to identify distinct cell populations and quantify their proportions across samples.