omics-analysis-skills-index - SKILL.md Agent Skill

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

Here are best practices and workflows for analyzing single-cell and spatial omics data. When performing specific analysis tasks, load the relevant skill files to guide your approach.

Available Skills

Single-Cell to Spatial Mapping

If you have both single-cell and spatial data for the same/similar sample, you can map single-cell data to spatial data to impute unobserved genes and enhance spatial resolution.

Skill file: single_cell_spatial_mapping.md

When to use:

You have paired scRNA-seq and spatial transcriptomics data
You want to impute genes not measured in the spatial modality
You want to transfer cell type annotations to spatial coordinates

3D Spatial Data Visualization

For visualizing 3D spatial transcriptomics data with interactive plots and animations.

Skill file: visualize_3d_spatial.md

When to use:

Your spatial data has 3D coordinates
You want to visualize gene expression or cell types in 3D
You want to create rotating GIF animations

Quality Control Workflow

Standard quality control workflow for single-cell data.

Skill file: quality_control.md

When to use:

Starting analysis of new single-cell dataset
Need to filter low-quality cells
Assessing data quality metrics

Cell Type Annotation

Approaches for annotating cell types in single-cell data.

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

Single-Cell Foundation Models (SCFM)

Workflow and model reference for embedding/integration with foundation models (scGPT, Geneformer, UCE).

Skill files:

When to use:

You want FM embeddings (e.g., obsm["X_uce"], obsm["X_scGPT"], obsm["X_geneformer"])
You need model selection based on gene ID scheme and species
You want a validation-first workflow before heavy inference

Trajectory Inference

Pseudotime analysis and trajectory inference for cell differentiation, neurogenesis, and lineage tracing studies.

Skill file: trajectory_inference.md

When to use:

Studying cell differentiation paths (e.g., stem cell → mature cell)
Neurogenesis analysis (neural progenitors → neurons)
Comparing developmental trajectories between conditions
RNA velocity analysis for directional dynamics

Parallel Computing & Performance

Strategies for accelerating single-cell analysis using multi-core CPU, GPU acceleration, and memory optimization.

Skill file: parallel_computing.md

When to use:

Analysis is running slowly on single core
Dataset has >50k cells and operations are timing out
GPU is available and you want 10-100x speedup
Need to parallelize custom analysis loops

Upstream Processing

Technology-specific pipelines for processing raw sequencing data into analysis-ready count matrices with spatial coordinates. These cover the steps that precede standard single-cell analysis (QC, normalization, clustering, etc.).

Skill index: upstream_processing/SKILL.md

Technologies covered:

nf-core Pipelines: 143+ curated Nextflow pipelines for scRNA-seq, spatial transcriptomics, bulk RNA-seq, ATAC-seq, ChIP-seq, CUT&Run, methylation, and variant calling (WGS/WES)
OpenST: Open-source spatial transcriptomics at sub-cellular resolution — flow cell barcode preprocessing, spacemake alignment, image registration, Cellpose segmentation, 3D reconstruction, and downstream analysis

When to use:

Processing raw BCL/FASTQ files with nf-core community pipelines
Running technology-specific alignment and preprocessing pipelines
Spatial coordinate registration and cell segmentation
Variant calling from WGS/WES/targeted sequencing
3D reconstruction from serial tissue sections

Database Access

Tools for querying genomic databases, downloading sequencing data from public repositories, and accessing large-scale single-cell datasets programmatically.

Skill index: database_access/SKILL.md

Tools covered:

gget: Python package with 23 modules for querying Ensembl, NCBI, UniProt, ARCHS4, Enrichr, COSMIC, OpenTargets, CellxGene, cBioPortal, PDB, and Bgee
iSeq: Bash CLI for downloading sequencing data from GSA, SRA, ENA, DDBJ, and GEO databases with parallel downloads and Aspera support
CZ CELLxGENE Census: Cloud-based Python API for accessing 217M+ single-cell RNA-seq observations with flexible metadata queries and pre-computed embeddings

When to use:

Querying gene/protein information from public databases
Downloading raw sequencing data (FASTQ/SRA) from public repositories
Accessing curated single-cell RNA-seq datasets by tissue, cell type, disease
Performing enrichment analysis or cancer mutation queries
Fetching reference genomes, annotations, and sequences

Supplementary Reference: SC Best Practices

For more comprehensive guidance on single-cell and spatial omics analysis, refer to the SC Best Practices skill collection, derived from the authoritative Single-cell Best Practices book. It covers the full analysis pipeline from preprocessing to reproducibility, including detailed workflows, method comparisons, and code examples for the scverse ecosystem.

Skill index: sc_best_practices/SKILL.md

Topics covered:

Introduction & raw data processing frameworks
Preprocessing (QC, normalization, HVG, dimensionality reduction)
Clustering, annotation & dataset integration
Trajectory analysis (pseudotime, RNA velocity, lineage tracing)
Differential expression & condition analysis
Gene regulatory networks & cell-cell communication
Bulk deconvolution, chromatin accessibility (scATAC-seq)
Spatial omics (neighborhood analysis, deconvolution, imputation)
Surface protein (CITE-seq), immune repertoire (TCR/BCR)
Multimodal integration & reproducibility

When the skills above provide task-specific workflows, these supplementary references offer broader context, alternative methods, and detailed best practices to complement your analysis.

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