| id | single_cell_skills_index |
| name | Single-Cell Analysis Skills Index |
| description | Core skills for single-cell RNA-seq analysis: quality control, cell type
annotation, and trajectory inference. These are high-priority actionable
workflows — load them first for common single-cell tasks.
|
| tags | ["single-cell","qc","annotation","trajectory","scanpy"] |
Core Single-Cell Analysis Skills
High-priority, actionable workflows for the most common single-cell analysis tasks.
For deeper background and alternative methods, see the supplementary
SC Best Practices reference.
Available Skills
Quality Control
Standard QC workflow: filtering low-quality cells, doublet detection,
normalization, and QC metric visualization.
Skill file: quality_control.md
When to use:
- Starting analysis of a new single-cell dataset
- Need to filter low-quality cells
- Assessing data quality metrics
Cell Type Annotation
Marker-based and reference-based approaches for assigning cell type labels.
Skill file: cell_type_annotation.md
When to use:
- After clustering, need to assign cell type labels
- Using marker genes for annotation
- Using reference-based methods (CellTypist, scArches)
Trajectory Inference
Pseudotime analysis and trajectory inference for cell differentiation,
lineage tracing, and RNA velocity.
Skill file: trajectory_inference.md
When to use:
- Studying cell differentiation paths
- Neurogenesis or developmental trajectory analysis
- RNA velocity for directional dynamics
