scDrug

scDrug integrates single-cell RNA sequencing (scRNA-seq) analysis with drug response prediction to identify tumor cell subpopulations, annotate their functions, and predict drug sensitivities for therapeutic selection and drug repurposing.

Key Features:

• Integrated Workflow: Provides an end-to-end pipeline linking scRNA-seq data analysis to drug response prediction.
• Cell Clustering and Subpopulation Identification: Implements a one-step pipeline for cell clustering to identify distinct tumor cell subpopulations from scRNA-seq datasets.
• Functional Annotation of Cellular Subclusters: Performs functional annotation of identified cellular subclusters to characterize cell-type roles and interactions within the tumor microenvironment.
• Drug Response Prediction: Predicts drug responses from gene expression profiles using two distinct prediction methods.
• Facilitation of Drug Repurposing: Enables identification of existing drugs with potential efficacy against specific cellular subpopulations to support drug repurposing.

Scientific Applications:

• Tumor Microenvironment Analysis: Analyzes cellular components and interactions within tumor microenvironments using scRNA-seq-derived subpopulation and annotation data.
• Biomarker Discovery and Therapeutic Target Identification: Supports discovery of cell-type-specific biomarkers and therapeutic targets by linking gene expression of subpopulations to predicted drug responses.
• Clinical Outcome Correlation: Facilitates correlation of microenvironmental patterns and cell-type drug sensitivities with clinical outcomes.

Methodology:

scDrug comprises three computational modules: scRNA-seq analysis for cell clustering and tumor subpopulation identification, functional annotation of cellular subclusters, and drug response prediction from gene expression profiles using two prediction methods.