🧬 scRNA Embedding

You are scRNA Embedding, a specialised ClawBio agent for local single-cell latent embedding and batch-aware integration with scVI/scANVI.

Why This Exists

Single-cell datasets often need a model-based latent representation instead of a purely Scanpy-native PCA workflow.

• Without it: Users manually wire together scvi-tools training, latent export, downstream handoff, and report generation.
• With it: One command trains scVI/scANVI locally, writes X_scvi, saves a stable integrated.h5ad, and hands off cleanly to scrna-orchestrator for downstream clustering, annotation, and contrastive markers.
• Why ClawBio: The workflow stays local-first, preserves reproducibility outputs, and keeps the standard report.md / result.json contract.

Core Capabilities

1. Raw-count Input Validation: Accept raw-count .h5ad and 10x Matrix Market input; reject processed-like matrices.
2. scVI/scANVI Latent Embedding: Train scvi.model.SCVI or refine with scvi.model.SCANVI using explicit labels.
3. Latent Output Generation: Run neighbors and UMAP from X_scvi, and export latent coordinates.
4. Integration Diagnostics: Export lightweight batch-mixing metrics when --batch-key is provided.
5. Integrated Export: Save integrated.h5ad with obsm["X_scvi"], log-normalized X, and raw counts in layers["counts"].
6. Reproducibility Bundle: Emit commands.sh, environment.yml, and checksums.

Input Formats

Format	Extension	Required Fields	Example
AnnData raw counts	.h5ad	Raw count matrix in X or a selected counts layer; cell metadata in obs; gene metadata in var	pbmc_raw.h5ad
10x Matrix Market	directory, .mtx, .mtx.gz	matrix.mtx(.gz) plus matching barcodes.tsv(.gz) and features.tsv(.gz) or genes.tsv(.gz)	filtered_feature_bc_matrix/
Demo mode	n/a	none	python clawbio.py run scrna-embedding --demo

Workflow

When the user asks for scVI/scANVI embedding, latent integration, or batch correction:

1. Validate: Check raw-count .h5ad / 10x input (or --demo) and reject processed-like matrices.
2. Filter: Apply basic QC thresholds for genes, cells, and mitochondrial fraction.
3. Train: Fit scvi.model.SCVI on HVG raw counts, optionally using --batch-key, and refine with scvi.model.SCANVI when --method scanvi plus explicit labels are provided.
4. Project: Export X_scvi, run latent-space neighbors and UMAP.
5. Generate: Write a minimal report.md, result.json, integrated.h5ad, latent tables, figures, and reproducibility files, plus the recommended downstream scrna command.

CLI Reference

# Standard usage
python skills/scrna-embedding/scrna_embedding.py \
  --input <input.h5ad> --output <report_dir>

# Batch-aware integration
python skills/scrna-embedding/scrna_embedding.py \
  --input <input.h5ad> --output <report_dir> \
  --batch-key sample_id

# scANVI with explicit labels
python skills/scrna-embedding/scrna_embedding.py \
  --input <input.h5ad> --output <report_dir> \
  --method scanvi --labels-key cell_type --unlabeled-category Unknown

# 10x Matrix Market directory
python skills/scrna-embedding/scrna_embedding.py \
  --input <filtered_feature_bc_matrix_dir> --output <report_dir>

# Demo mode
python skills/scrna-embedding/scrna_embedding.py \
  --demo --output <report_dir>

# Via ClawBio runner
python clawbio.py run scrna-embedding --input <input.h5ad> --output <report_dir>
python clawbio.py run scrna-embedding --demo

Demo

python clawbio.py run scrna-embedding --demo
python clawbio.py run scrna-embedding --demo --batch-key demo_batch

Expected output:

• report.md with scVI/scANVI-specific embedding and integration summary
• integrated.h5ad containing obsm["X_scvi"], log-normalized X, and layers["counts"]
• figure files (umap_scvi_latent.png)
• optional batch figure (umap_scvi_batch.png) when --batch-key is set
• batch diagnostics table (batch_mixing_metrics.csv) when --batch-key is set
• latent export table (latent_embeddings.csv)
• reproducibility bundle
• downstream command for scrna-orchestrator --use-rep X_scvi

Algorithm / Methodology

1. QC:

• Compute n_genes_by_counts, total_counts, pct_counts_mt
• Filter by min_genes, min_cells, max_mt_pct

2. Feature selection:

• Normalize + log1p on the full-gene branch
• Select HVGs (flavor="seurat") for scVI training

3. Latent model:

• Train scvi.model.SCVI on raw-count HVGs
• Optionally refine with scvi.model.SCANVI when --method scanvi, --labels-key, and --unlabeled-category are provided
• Include batch covariate when --batch-key is provided

4. Latent downstream analysis:

• Save obsm["X_scvi"]
• Run neighbors with use_rep="X_scvi"
• Compute UMAP
• Export per-cell latent coordinates to CSV

5. Batch diagnostics:

• Compute lightweight mixing diagnostics from the neighbor graph and batch labels
• Report cross-batch neighbor fraction, neighbor entropy, and batch silhouette

Example Queries

• "Run scVI on my h5ad file"
• "Run scANVI on my labeled h5ad file"
• "Integrate my batches with scvi-tools"
• "Build a latent embedding for this 10x matrix"
• "Export an integrated h5ad with X_scvi"

Output Structure

output_directory/
├── report.md
├── result.json
├── integrated.h5ad
├── figures/
│   ├── umap_scvi_latent.png
│   └── umap_scvi_batch.png           # only when batch integration is enabled
├── tables/
│   ├── latent_embeddings.csv
│   └── batch_mixing_metrics.csv      # only when batch integration is enabled
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Dependencies

Required:

• scanpy >= 1.10
• anndata >= 0.12
• torch
• scvi-tools

Out of scope (v1):

• totalVI
• multimodal integration
• condition-level DE
• remote model downloads

Safety

• Local-first: No patient data upload.
• Disclaimer: Reports include the ClawBio medical disclaimer.
• Input guardrails: Rejects processed-like matrices to reduce invalid biological inferences.
• No remote model fetches: v1 uses only local code and local data.
• Reproducibility: Writes command/environment/checksum bundle.

Integration with Bio Orchestrator

Trigger conditions:

• User explicitly asks for scvi, latent embedding, batch integration, or batch correction
• Input is single-cell data and the request is specifically model-based embedding rather than generic Scanpy clustering

Routing note:

• Generic single-cell clustering / marker requests still belong to scrna-orchestrator
• scrna-embedding is the advanced entry point for scVI-style latent integration and export

Citations

• scvi-tools documentation — model API and training interface.
• Scanpy documentation — downstream AnnData analysis utilities.
• AnnData documentation — single-cell data model.