scrna-seq-qc - Quality Control and Annotation for scRNA-seq Datasets
Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species.
Tags
Updated: 2026-09-19Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Filter cells using custom QC thresholds
- Detect doublets using scDblFinder
- Filter ambient RNA metrics
- Build latent space using scVI
- Annotate cells via reference mapping
- Annotate cells via marker genes
- Generate global UMAP plots
- Generate per-group UMAP plots
Inputs
- AnnData objects with raw counts
- Per-sample and per-batch metadata
- Reference atlases and marker panels
- 10x-style matrix bundle directories
Outputs
- Filtered h5ad dataset files
- QC summary tables
- Threshold justification plots
- Parameter manifest files
- UMAP visualization plots and coordinates
- HTML review reports
- Analysis status JSON files
Requirements
- Python and R execution environments
- scDblFinder package
- Scanpy and scVI packages
- MapMyCells or cell_type_mapper tools