single-cell-rna-qclisted

# Single-Cell RNA-seq Quality Control Automated QC workflow for single-cell RNA-seq data following scverse best practices. ## When to Use This Skill Use when users: - Request quality control or QC on single-cell RNA-seq data - Want to filter low-quality cells or assess data quality - Need QC visualizations or metrics - Ask to follow scverse/scanpy best practices - Request MAD-based filtering or outlier detection **Supported input formats:** - `.h5ad` files (AnnData format from scanpy/Python workflows) - `.h5` files (10X Genomics Cell Ranger output) **Default recommendation**: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic. ## Approach 1: Complete QC Pipeline (Recommended for Standard Workflows) For standard QC following scverse best practices, use the convenience script `scripts/qc_analysis.py`: ```bash python3 scripts/qc_analysis.py input.h5ad # or for 10X Genomics .h5 files: python3 scripts/qc_analysis.py raw_feature_bc_matrix.h5 ``` The script automatically detects the file format and loads it appropriately. **When to use this approach:** - Standard QC workflow with adjustable thresholds (all cells filtered the same way) - Batch processing multiple datasets - Quick exploratory analysis - User wants the "just works" solution **Requirements:** anndata, scanpy, scipy, matplotlib, seaborn, numpy **Parameters:** Customize filtering thresholds and gene patterns using command-lin