ngs-bulk-rnaseq-differential-expression Skill
Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks. Published by openai in plugins.
Decision snapshot
Is this a fit?
Data analysis, Design and media, Includes SKILL.md, Reusable instructions
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Copy skill directory
19 days ago
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What is ngs-bulk-rnaseq-differential-expression Skill?
Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks. Published by openai in plugins. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Design and media
- Data analysis use cases
- Design and media use cases
Declared skill metadata
- Source file: plugins/ngs-analysis/skills/ngs-bulk-rnaseq-differential-expression/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use ngs-bulk-rnaseq-differential-expression Skill
- Use it for data analysis.
- Use it for design and media.
Built with
Editorial notes
Source
- Creator: openai
- Repository: openai/plugins
- Skill file: plugins/ngs-analysis/skills/ngs-bulk-rnaseq-differential-expression/SKILL.md
What it does
Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks.
Skill instructions
Bulk RNA-seq Differential Expression Use this skill when the user has raw counts or a count-generation output and wants differential expression, contrasts, QC plots, or ranked gene tables. Essential Inputs Confirm: - raw count matrix path and sample metadata path - gene ID type and annotation mapping requirement - biological conditions, replicates, batch variables, donor pairing, covariates, and exclusions - exact contrasts and baseline levels - preferred statistical framework: DESeq2, edgeR, limma-voom, or existing lab standard - output needs: normalized counts, PCA, sample distance, volcano plots, heatmaps, ranked tables, GSEA-ready lists Preconditions Do not start differential expression until: - raw counts are preserved - each requested contrast has enough biological replication - sample metadata row names match count matrix columns - batch/covariate choices are explicit - exploratory PCA/sample-distance plots do not reveal obvious swaps or failed libraries Route For most count mat
Verified compatibility and discovery
Frequently asked questions
What is ngs-bulk-rnaseq-differential-expression?
ngs-bulk-rnaseq-differential-expression is a open-source AI agent skill with Copy skill directory. Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks.
Who is ngs-bulk-rnaseq-differential-expression best for?
ngs-bulk-rnaseq-differential-expression is best for reusing agent instructions, scripts, and references, data analysis workflows, design and media workflows.
How do I install ngs-bulk-rnaseq-differential-expression?
Install or run ngs-bulk-rnaseq-differential-expression using Copy skill directory. Check ngs-bulk-rnaseq-differential-expression for the latest setup command.
Is ngs-bulk-rnaseq-differential-expression actively maintained?
ngs-bulk-rnaseq-differential-expression may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
