scientific-single-cell-genomics

What problem does it solve?

Provides a standardized, reproducible workflow for scRNA-seq analysis, covering quality control, normalization, dimensionality reduction, clustering, differential expression, and cell-type annotation, all built on the Scanpy/AnnData ecosystem.

Core Features & Use Cases

• QC and preprocessing for scRNA-seq data, ensuring high-quality cells and reliable gene metrics.
• Normalization, highly variable gene selection, PCA/UMAP, and Leiden clustering to reveal cellular heterogeneity.
• Differential expression analysis and cell-type annotation to interpret clusters and cell states.
• RNA velocity integration for lineage trajectory inference and dynamic cellular states.
• Intercellular communication estimation with CellChat/CellPhoneDB to study cellular crosstalk.
• Compatible with Scanpy/AnnData workflows for scalable, reproducible analyses.

Quick Start

Apply the standard scRNA-seq workflow to an AnnData object to generate QC metrics, normalization and HVG selection, PCA/UMAP, Leiden clustering, DEG identification, and cell-type annotation.