Omics Analysis Skills Index

Research

Skills for single-cell and spatial omics data analysis. Best practices, code snippets, and workflows for the scverse ecosystem.

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How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/aristoteleo/PantheonOS/blob/HEAD/pantheon/factory/templates/skills/omics/SKILL.md

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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
Benchmarking existing panels (ARI/NMI/Silhouette + UMAP)
IMPORTANT: When doing gene panel selection, strictly follow this workflow

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