single-cell-analysis-skills-index

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