scrna-meta-analysis

What problem does it solve?

This Skill facilitates the integration and quality assessment of multiple single-cell RNA-seq datasets to identify reproducible cell populations and gene markers across studies.

Core Features & Use Cases

• Dataset Discovery & Download: Finds relevant ENCODE or public scRNA-seq experiments for specific tissues and retrieves gene quantification files.
• Quality Control & Filtering: Assesses experiment QC metrics like gene detection, mitochondrial content, and removes low-quality datasets.
• Data Integration: Combines datasets using methods like Harmony, scVI, or Seurat, accommodating platform differences and batch effects.
• Annotation & Harmonization: Supports manual, automated, or reference-based cell type annotations, resolving label discrepancies with CellHint.
• Reproducibility Analysis: Compares markers across datasets, examines detection limits with TIN scores, and evaluates contamination levels to identify robust findings.
• Downstream Processing: Performs differential expression analysis (pseudobulk), cell proportion comparison, and trajectory inference on integrated data.

Quick Start

Search for scRNA-seq experiments on pancreas tissue, select high-quality datasets, and perform an integrated analysis to discover conserved cell types and reliable marker genes.