Multiome Scatac

Multiome And scATAC

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially scanpy and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for paired or integrated single-cell RNA and ATAC analysis with multimodal latent spaces and regulatory interpretation.

When To Use This Skill

• use when the task is scATAC, multiome RNA-ATAC, or multimodal single-cell integration
• use when gene activity, motif activity, or regulatory linkage is required
• use when the user needs a joint view across modalities rather than separate analyses

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• multiome object or paired modalities
• ATAC fragments or peak matrix
• cell metadata

Expected Outputs

• integrated embeddings
• modality-aware clusters
• motif or regulatory summaries

Preferred Tools

• scanpy
• anndata
• scvi-tools where appropriate
• motif-analysis utilities

Starter Pattern

Preferred starting point: scanpy
Inputs: multiome object or paired modalities, ATAC fragments or peak matrix, cell metadata
Outputs: integrated embeddings, modality-aware clusters, motif or regulatory summaries

Workflow

1. QC both modalities

Evaluate RNA and ATAC quality independently before joint integration.

2. Create harmonized features

Build peak, gene, or gene activity representations consistent across cells.

3. Integrate modalities

Use an approach suited to paired or unpaired multimodal data.

4. Interpret regulatory signals

Relate motif accessibility, gene activity, and expression patterns cautiously.

5. Export multimodal state summaries

Save joint embeddings, modality-specific QC, and regulatory annotations.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• integrated embeddings
• modality-aware clusters
• motif or regulatory summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Review embeddings together with QC metrics and batch structure before labeling biology.
• Preserve the processed object with metadata and embeddings for downstream reuse.

Anti-Patterns

• treating weak gene activity estimates as direct expression measurements
• integrating low-quality modalities without modality-specific QC
• reporting regulatory links without stating the evidence type

Related Skills

• scRNA Preprocessing And Clustering
• Cell Annotation
• Cell Communication
• Trajectory And Lineage

Optional Supplements

• scvi-tools
• anndata