Pydeseq2 Bulk Rna

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.

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💻 Protocol Code Snippet

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

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📥 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.

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📄 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
• Source: YuliaNuzhnenko/bioinformatics-agent-skills
• License: MIT

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