scGate

scGate performs hierarchical marker-based purification of specific cell populations from heterogeneous single-cell RNA-seq, ATAC-seq, and CITE-seq datasets without requiring training data or reference gene expression profiles.

Key Features:

• Hierarchical marker gating: Uses a hierarchical structure of markers that mirrors flow cytometry gating strategies to sequentially identify and isolate cell populations.
• Multi-modality support: Operates on single-cell RNA-seq, ATAC-seq, and CITE-seq modalities for marker-based purification across data types.
• Seurat integration: Implemented as an R package that integrates with the Seurat framework for use within Seurat workflows.
• No training data required: Performs classification and purification without the need for training datasets or reference gene expression profiles.
• Performance assessment: Reported to outperform state-of-the-art single-cell classifiers in accurately purifying target cell populations.

Scientific Applications:

• Immunology: Isolating specific immune cell subsets for downstream functional and transcriptomic analyses.
• Developmental biology: Extracting progenitor and differentiated cell populations to study differentiation pathways.
• Cancer research: Identifying tumor-infiltrating lymphocytes and cancer stem cell populations within heterogeneous tumor microenvironments.

Methodology:

The algorithm organizes markers into a hierarchical structure that mimics flow cytometry gating strategies to systematically identify and isolate specific cell types; the method does not require training data or reference gene expression profiles.