scTyper

scTyper performs cell-type annotation of single-cell RNA sequencing (scRNA-seq) data using curated marker sets, multiple marker-expression estimation methods, and DNA copy number inference to distinguish malignant from non-malignant cells.

Key Features:

• scTyper.db: A curated cell marker database containing 213 marker sets covering epithelial cells, immune cells, fibroblasts, malignant cells, cancer-associated fibroblasts, and tumor-infiltrating T cells.
• Nearest Template Prediction (NTP): Template-matching method to predict cell types based on closest marker expression profiles.
• Gene Set Enrichment Analysis (GSEA): Uses GSEA to assess enrichment of predefined marker gene sets across cells or clusters.
• Average Expression Values: Computes average expression of marker genes to infer cell-type-specific signals.
• DNA Copy Number Inference (improved inferCNV): Incorporates an improved version of inferCNV to infer DNA copy number variation for malignant cell typing.
• Integration with preprocessing pipelines: Accepts outputs from Cell Ranger (10X Genomics) and Seurat-based preprocessing workflows.
• Summary reporting: Generates summary reports to document cell-typing results and associated analyses.

Scientific Applications:

• Oncology: Identification and characterization of malignant cells and cancer-associated fibroblasts using marker expression and inferred copy number variation.
• Immunology: Annotation of immune cell subsets, including tumor-infiltrating T cells, based on curated marker sets and enrichment analyses.
• Developmental biology: Cell-type annotation in heterogeneous tissues to resolve developmental cell populations using marker-based methods.

Methodology:

Marker-based cell typing using scTyper.db (213 marker sets), Nearest Template Prediction (NTP), Gene Set Enrichment Analysis (GSEA), average expression scoring, and DNA copy number inference via an improved inferCNV; supports inputs from Cell Ranger and Seurat.