# Scrna Preprocessing Clustering

> Standard scRNA-seq preprocessing and clustering with Scanpy. Use for QC, normalization, HVG selection, PCA, neighbor graph construction, UMAP, Leiden clustering, and export of an analysis-ready AnnData object.

- **Type:** Skill
- **Install:** `agentstack add skill-cheatthegod-biohermes-scrna-preprocessing-clustering`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [cheatthegod](https://agentstack.voostack.com/s/cheatthegod)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [cheatthegod](https://github.com/cheatthegod)
- **Source:** https://github.com/cheatthegod/BioHermes/tree/main/optional-skills/bioinformatics/scrna-preprocessing-clustering

## Install

```sh
agentstack add skill-cheatthegod-biohermes-scrna-preprocessing-clustering
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# scRNA Preprocessing And Clustering

## Version Compatibility

Reference examples assume:

- `scanpy` 1.10+
- `anndata` 0.10+
- `pandas` 2.2+
- `matplotlib` 3.8+

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

- Python: `python -c "import scanpy, anndata; print(scanpy.__version__, anndata.__version__)"`
- If signatures differ, inspect the installed API and adapt the pattern instead of retrying unchanged.

## Overview

Use this skill to turn raw or minimally processed scRNA-seq data into an analysis-ready object with:

- QC-filtered cells and genes
- normalized expression values
- highly variable genes
- PCA and UMAP embeddings
- Leiden clusters
- saved `h5ad` artifact for annotation, DE, integration, or trajectory analysis

## When To Use This Skill

- raw 10x matrices, filtered count matrices, or `h5ad` inputs need standard preprocessing
- the user wants UMAP, clustering, or marker discovery
- downstream tasks depend on a stable single-cell object rather than ad hoc plots

## Quick Route

- If the input is already a processed `h5ad`, inspect `adata.raw`, embeddings, cluster columns, and QC columns before rerunning preprocessing.
- If the input is raw counts, do QC first and only normalize after filtering obvious low-quality cells.
- If multiple batches are present, preprocess cleanly first, then consider integration instead of hiding batch effects with aggressive filtering.

## Progressive Disclosure

- Read [technical_reference.md](technical_reference.md) for QC decision rules, assay caveats, and integration branching.
- Read [commands_and_thresholds.md](commands_and_thresholds.md) for concrete Scanpy code, default thresholds, and output conventions.

## Default Rules

- Keep raw counts recoverable. Prefer `adata.raw = adata.copy()` before regression or scaling.
- Report thresholds explicitly. Do not silently drop cells or genes.
- Show QC distributions before applying hard filters.
- Use vector outputs such as `.pdf` or `.svg` for final figures when possible.

## Expected Inputs

- 10x directory, `.h5`, `.h5ad`, or count matrix
- cell metadata if available
- species context for mitochondrial or ribosomal gene detection

## Expected Outputs

- `results/processed.h5ad`
- `qc/cell_qc_metrics.tsv`
- `qc/gene_qc_metrics.tsv`
- `figures/qc_violin.pdf`
- `figures/pca_variance_ratio.pdf`
- `figures/umap_leiden.pdf`

## Preferred Tools

- `scanpy`
- `anndata`
- `pandas`
- `matplotlib`
- `seaborn`

## Starter Pattern

```python
import scanpy as sc

adata = sc.read_10x_mtx("counts/")
adata.var_names_make_unique()
adata.var["mt"] = adata.var_names.str.upper().str.startswith("MT-")
sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], inplace=True)

adata = adata[
    (adata.obs["n_genes_by_counts"] >= 200)
    & (adata.obs["n_genes_by_counts"] = 200`
- `max_genes = 3` for genes

### 4. Normalize, log-transform, and select HVGs

- normalize with `target_sum=1e4`
- `log1p`
- select `2000-4000` HVGs
- save raw counts before heavy transformations

### 5. Reduce dimensions and cluster

- PCA on HVGs
- neighbor graph using `10-30` PCs and `10-30` neighbors as a starting range
- UMAP for visualization
- Leiden across a small resolution grid such as `0.2`, `0.5`, `0.8`, `1.0`

### 6. Export analysis-ready artifacts

Always save:

- processed `h5ad`
- QC tables
- cluster assignments
- publication-ready QC and UMAP figures

## Output Artifacts

- `results/processed.h5ad`: main reusable AnnData object
- `results/cluster_assignments.tsv`: barcode plus cluster labels
- `qc/filter_summary.tsv`: counts before and after filtering
- `figures/umap_leiden.pdf`: main embedding figure

## Quality Review

- Median genes per cell should be plausible for the chemistry and tissue.
- Mitochondrial fraction should not dominate retained cells.
- PCA variance should decay smoothly rather than showing obvious technical axes only.
- UMAP should be reviewed together with QC metrics and batch labels, not alone.
- Cluster labels should not be finalized before marker inspection.

## Anti-Patterns

- reprocessing an already integrated object as if it were raw counts
- using a single universal mitochondrial threshold for every tissue
- interpreting UMAP separation as biology before checking batch and QC covariates
- discarding raw counts needed later for DE or pseudobulk

## Related Skills

- Cell Annotation
- Cell Communication
- Trajectory And Lineage
- Multiome And scATAC

## Optional Supplements

- `anndata`
- `scanpy`

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [cheatthegod](https://github.com/cheatthegod)
- **Source:** [cheatthegod/BioHermes](https://github.com/cheatthegod/BioHermes)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-cheatthegod-biohermes-scrna-preprocessing-clustering
- Seller: https://agentstack.voostack.com/s/cheatthegod
- Browse the marketplace: https://agentstack.voostack.com/browse

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