| id | omics_skills_index |
| name | Omics Analysis Skills Index |
| description | Skills for single-cell and spatial omics data analysis.
Best practices, code snippets, and workflows for the scverse ecosystem.
|
Agent Skills for Omics Data Analysis
Best practices and workflows for single-cell and spatial omics analysis.
Load the relevant skill files when performing specific analysis tasks.
Core Single-Cell Skills
High-priority, actionable workflows for the most common single-cell analysis tasks.
Skill index: single_cell/SKILL.md
Skills:
- Quality Control: Filtering, doublet detection, normalization, QC metrics
- Cell Type Annotation: Marker-based and reference-based label assignment
- Trajectory Inference: Pseudotime, lineage tracing, RNA velocity
Gene Panel Selection
End-to-end workflow for designing gene panels in scRNA-seq and spatial
transcriptomics (HVG/DE/RF/scGeneFit/SpaPROS), with sub-panel discovery,
consensus scoring, biological completion, and benchmarking.
Skill folder: gene_panel_selection/
When to use:
- Designing a gene panel for spatial transcriptomics in a static / terminal system
- Benchmarking existing panels (ARI/NMI/Silhouette + UMAP)
- IMPORTANT: When doing gene panel selection, strictly follow this workflow
Developmental Gene Panel Design
Panel design for developing / dynamic systems (embryonic organs, differentiation, regeneration),
where terminal cell types are end-products of earlier lineage programs. Uses TWO references — the
target (usually late) stage for cell-state resolution, and an independent EARLIER-stage reference
mined by trajectory/pseudotime ranking for developmental regulators — under an explicit budget split.
Skill folder: developmental_gene_panel/
When to use (use this INSTEAD of gene_panel_selection):
- The tissue is embryonic / differentiating / regenerating, or the user cares about lineage origin
and regulators, not only terminal cell-type classification
- The assay is at a late stage but the biology is a trajectory
- Why it matters: developmental regulators are frequently NOT recoverable from the target stage —
in a benchmarked case study six unbiased methods (cell-type DE, DPT pseudotime Pearson/Spearman,
cross-stage ANOVA, global marker rank) all ranked a known CHD regulator in the top 0.6–1.8% on an
early reference and failed (top 3–79%) on the target stage; literature lookup also failed
Spatial Omics
Skills for spatial transcriptomics mapping, imputation, and 3D visualization.
Skill index: spatial/SKILL.md
Skills:
- Single-Cell to Spatial Mapping: Map scRNA-seq to spatial data with MOSCOT
for gene imputation and cell type transfer
- 3D Spatial Visualization: Interactive 3D plots and rotating animations
with PyVista
When to use:
- You have paired scRNA-seq and spatial transcriptomics data
- You want to impute genes or transfer cell type labels to spatial coordinates
- Your spatial data has 3D coordinates and you want to visualize them
Single-Cell Foundation Models (SCFM)
Workflow and model reference for embedding/integration with foundation models
(scGPT, Geneformer, UCE, scBERT, etc.).
Skill index: scfm/SKILL.md
When to use:
- You want FM embeddings (e.g.,
obsm["X_uce"], obsm["X_scGPT"])
- You need model selection based on gene ID scheme and species
- You want a validation-first workflow before heavy inference
Database Access
Tools for querying genomic databases, downloading sequencing data, and
accessing large-scale single-cell datasets programmatically.
Skill index: database_access/SKILL.md
Tools covered:
- gget: 23 modules for querying Ensembl, NCBI, UniProt, COSMIC, OpenTargets, etc.
- iSeq: CLI for downloading from GSA, SRA, ENA, DDBJ, GEO
- CZ CELLxGENE Census: API for 217M+ single-cell observations
Upstream Processing
Technology-specific pipelines for processing raw sequencing data into
analysis-ready count matrices.
Skill index: upstream_processing/SKILL.md
Technologies covered:
- nf-core Pipelines: 143+ Nextflow pipelines for scRNA-seq, spatial, bulk,
ATAC-seq, ChIP-seq, variant calling
- OpenST: Open-source spatial transcriptomics processing pipeline
General Data Analysis
Cross-cutting skills for environment setup and computational performance.
Skill index: general_data_analysis/SKILL.md
Skills:
- Environment Management: Conda/Mamba/venv setup for reproducible environments
- Parallel Computing: Multi-core CPU, GPU acceleration, memory optimization
Supplementary Reference: SC Best Practices
Comprehensive guidance derived from the
Single-cell Best Practices book.
Use as supplementary context when the core skills above need deeper background.
Skill index: sc_best_practices/SKILL.md
Topics covered:
- Preprocessing, normalization, dimensionality reduction
- Clustering, annotation, dataset integration
- Trajectory analysis, RNA velocity, lineage tracing
- Differential expression, compositional analysis, pathway analysis
- Gene regulatory networks, cell-cell communication
- Bulk deconvolution, scATAC-seq, spatial omics
- CITE-seq, immune repertoire (TCR/BCR)
- Multimodal integration, reproducibility
Using Skills
- Before analysis: Scan this index for relevant skills
- Load skill file: Read the full skill document for detailed guidance
- Follow best practices: Use the code snippets and workflows provided
- Adapt as needed: Skills are templates; adjust for your specific data
