scTree

scTree identifies a minimal set of marker genes from single-cell RNA sequencing (scRNA-seq) data using tree-based analysis to support selection of genes for downstream experiments such as fluorescence-activated cell sorting (FACS).

Key Features:

• Tree-Based Analysis: Employs tree-based methodologies to analyze clusters derived from scRNA-seq data and to discriminate cell populations based on transcriptomic profiles.
• Marker Gene Identification: Narrows lists of differentially expressed genes to a concise, experimentally tractable set of marker genes that distinguish subpopulations.
• Experimental Follow-Up Support: Produces minimal marker sets intended for downstream experimental applications, including FACS-based selection.

Scientific Applications:

• Cell Population Identification: Identifies distinct cell populations within heterogeneous scRNA-seq samples for studies in developmental biology, immunology, and cancer research.
• Marker Gene Validation: Provides candidate marker genes for validation experiments following scRNA-seq differential expression analyses.
• Experimental Design Optimization: Supplies reduced, prioritized gene sets to inform and streamline experimental assays such as FACS.

Methodology:

Uses tree-based analysis on clustered scRNA-seq data, clustering cells by transcriptomic profiles, identifying differentially expressed genes, and selecting a minimal subset of marker genes for downstream experiments.