# Pydeseq2 Bulk Rna

> Automated negative binomial differential gene expression analysis, log2 fold-change calculation, p-value adjustment (FDR), and Volcano plot generation.

- **Type:** Skill
- **Install:** `agentstack add skill-yulianuzhnenko-bioinformatics-agent-skills-pydeseq2-bulk-rna`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [YuliaNuzhnenko](https://agentstack.voostack.com/s/yulianuzhnenko)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [YuliaNuzhnenko](https://github.com/YuliaNuzhnenko)
- **Source:** https://github.com/YuliaNuzhnenko/bioinformatics-agent-skills/tree/main/skills/pydeseq2-bulk-rna

## Install

```sh
agentstack add skill-yulianuzhnenko-bioinformatics-agent-skills-pydeseq2-bulk-rna
```

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

## About

# Agent Skill: PyDESeq2 Bulk RNA-Seq Differential Expression Skill

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## 📌 Description
Automated negative binomial differential gene expression analysis, log2 fold-change calculation, p-value adjustment (FDR), and Volcano plot generation.

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## 🤖 Agent Execution Protocol

When an AI Agent is tasked with `pydeseq2-bulk-rna`:
1. **Input Validation**: Verify that the required input files or coordinates are supplied.
2. **Environment Check**: Ensure dependencies (`PyDESeq2, DESeq2, Pandas, Plotly`) are installed.
3. **Execution**: Run the protocol pipeline snippet below.
4. **Output Generation**: Produce actionable Markdown/JSON summaries with publication figures.

---

## 💻 Protocol Code Snippet

```python
import pandas as pd
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

def run_dge(counts_df, metadata_df, design_factors="condition"):
    # Real PyDESeq2 Differential Expression Pipeline
    dds = DeseqDataSet(
        counts=counts_df,
        metadata=metadata_df,
        design_factors=design_factors
    )
    dds.deseq2()
    
    stat_res = DeseqStats(dds, contrast=["condition", "treated", "control"])
    stat_res.summary()
    return stat_res.results_df
```

---

## 📥 Input & Output Specifications

### Input Contract
* **Target Files**: Valid input data matching domain formats.
* **Parameters**: Quality thresholds and cutoffs.

### Output Contract
* **Results Table**: Structured summary dataframe or matrix.
* **Visualization**: Rendered SVG/PNG figures.

---

## 📄 License
Distributed under the MIT License. See `LICENSE` for details.

## Source & license

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

- **Author:** [YuliaNuzhnenko](https://github.com/YuliaNuzhnenko)
- **Source:** [YuliaNuzhnenko/bioinformatics-agent-skills](https://github.com/YuliaNuzhnenko/bioinformatics-agent-skills)
- **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-yulianuzhnenko-bioinformatics-agent-skills-pydeseq2-bulk-rna
- Seller: https://agentstack.voostack.com/s/yulianuzhnenko
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.