pydeseq2
Analyze RNA-Seq Expression with PyDESeq2
Bulk RNA-seq comparisons require careful data alignment, model design, and multiple-testing correction. This skill guides PyDESeq2 workflows from count preparation through differential expression results and plots.
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Install with my Agent
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Review the Skillstore skill "pydeseq2" from https://skillstore.io/skills/davila7-pydeseq2.md and its manifest at https://skillstore.io/api/skills/davila7-pydeseq2/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
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Test it
Using "pydeseq2". Compare treated and control samples while adjusting for sequencing batch.
Expected outcome:
- Recommended design: batch followed by condition.
- Primary contrast: treated versus control within the condition factor.
- Report adjusted p-values, effect sizes, Cook distance handling, and sample alignment checks.
Using "pydeseq2". Summarize significant genes from a completed PyDESeq2 result table.
Expected outcome:
The summary reports tested genes, adjusted-significance counts, upregulated and downregulated totals, and top genes ranked by adjusted p-value.
Using "pydeseq2". Create visualization guidance for differential expression results.
Expected outcome:
Use shrunk fold changes for volcano and MA plots, label adjusted significance clearly, and retain unshrunken test statistics for inference.
Security Audit
High RiskAll 74 static findings are false positives involving Markdown, expected output writes, documentation links, or sample-filtering guidance. However, the workflow recommends pickle.load without a trust warning, which can execute malicious payloads. No prompt injection, network requests, credential access, or covert command execution was found.
Confirmed security concerns (1)
Risk Factors
📁 Filesystem access (4)
⚙️ External commands (50)
🌐 Network access (2)
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APA citation
davila7. (2026). pydeseq2 security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pydeseq2/audits/9BibTeX citation
@techreport{davila7-davila7-pydeseq2-2026,
author = {davila7},
title = {pydeseq2 security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-pydeseq2/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pydeseq2 security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pydeseq2/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-pydeseq2:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
k-dense-ai-pydeseq2
2026-08-21
davila7-pydeseq2
2026-08-21
Skillstore Score
Why this score Evidence Confidence: MediumWhat You Can Build
Compare treatment and control samples
Prepare a two-group analysis with filtered counts, an explicit contrast, adjusted p-values, and ranked genes.
Control for batch effects
Construct a multi-factor design that estimates condition effects while accounting for batch or continuous covariates.
Automate repeatable RNA-seq analyses
Use the command-line script to process datasets and export tables, fitted objects, volcano plots, and MA plots.
Try These Prompts
Analyze my counts file at [counts path] and metadata at [metadata path]. Compare [treated] against [control] using the [condition] column.
Inspect the count matrix and metadata at [paths]. Verify orientation, sample alignment, non-negative integer counts, missing values, and low-count filtering.
Create a PyDESeq2 workflow for design [~batch + condition] and contrast [condition, treated, control]. Explain assumptions, diagnostics, and adjusted result interpretation.
Design an advanced PyDESeq2 analysis for [factors and levels]. Include interactions, multiple contrasts, shrinkage strategy, quality checks, exports, and reproducibility notes.
Best Practices
- Confirm that count columns and metadata rows identify the same samples before fitting.
- Use raw non-negative integer counts, filter weak genes, and document every filtering threshold.
- Specify contrasts explicitly and interpret adjusted p-values alongside effect sizes and diagnostics.
Avoid
- Do not use normalized, transformed, or fractional expression values as DESeq2 count input.
- Do not remove confounded variables without explaining how the design and biological question change.
- Do not load pickle files from untrusted or unknown sources.
Frequently Asked Questions
What inputs does the skill expect?
Can the workflow account for batch effects?
Does this skill support single-cell RNA-seq?
Why might the count matrix need transposition?
How should significance be interpreted?
Is it safe to load saved pickle files?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
5 downloads · 200 views
File structure