VirtualCytometry

VirtualCytometry analyzes immune cell differentiation using single-cell RNA sequencing (scRNA-seq) data to identify cellular states and marker-driven subsets.

Key Features:

• Exploitation of cell-to-cell variation: Utilizes inherent variability in gene expression across individual cells to detect distinct cellular states within heterogeneous populations.
• Marker-gene based subset identification: Identifies cellular subsets and functional states by analyzing expression patterns of marker genes.
• Genome-wide quantification: Leverages scRNA-seq's unbiased, genome-wide measurement of gene expression at the single-cell level.
• Comprehensive dataset repository: Includes over 226 precompiled scRNA-seq datasets from public repositories covering mouse and human immune cell types in normal and disease contexts.
• Case-study applications: Has been applied to uncover signaling molecules and transcription factors involved in T-cell exhaustion in tumor environments and in pathogen-activated dendritic cells in mice.
• Continuous-state analysis: Supports analysis of continuous spectra of cellular states rather than enforcing discrete population assignments.

Scientific Applications:

• Molecular dissection of immune differentiation: Enables identification of genes and transcriptional programs underlying transitions between immune cell functional states.
• Identification of regulatory molecules: Facilitates discovery of signaling molecules and transcription factors that govern differentiation and functional changes in immune cells.
• Analysis of continuous cellular states: Supports study of phenomena characterized by gradual state changes, such as T-cell exhaustion in tumor microenvironments.

Methodology:

Processes scRNA-seq data to evaluate gene expression variation across individual cells and identifies distinct cellular subsets and functional states based on marker-gene expression and genome-wide single-cell quantification.