SeuratExtend

SeuratExtend streamlines single-cell RNA sequencing (scRNA-seq) data analysis by integrating essential analytical tools and databases into a unified R framework built upon Seurat.

Key Features:

• Functional Enrichment Analysis: Performs functional enrichment using integrated databases including Gene Ontology and Reactome.
• Trajectory Inference: Incorporates Python tools scVelo and Palantir for trajectory inference through a unified R interface.
• Gene Regulatory Network Reconstruction: Integrates SCENIC for gene regulatory network reconstruction via the R interface.
• Denoising: Provides denoising capabilities for scRNA-seq data.
• Pathway-Level Analysis: Enables novel applications in pathway-level analysis and cluster annotation.
• Data Visualization: Enhances visualization with optimized plotting functions and curated color schemes.

Scientific Applications:

• Tumor-Associated High-Endothelial Venules: Investigates tumor-associated high-endothelial venules through case studies.
• Autoinflammatory Diseases: Applies the framework to study autoinflammatory diseases in case studies.
• Pathway-Level Analysis: Supports novel pathway-level analysis applications for biological interpretation.
• Cluster Annotation: Facilitates cluster annotation for cell type identification and characterization.

Methodology:

SeuratExtend is built as an R package upon the Seurat framework and integrates Python tools including scVelo, Palantir, and SCENIC through a unified R interface.