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Zepeng-Mu
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user-test

Validate ezGenomeTracks R package plotting components with external genomic datasets.

1|Updated Sep 1, 2025
Search Tags:#data validation#visualization#genomics#r#plotting#package testing
One-click install
npx skills add https://github.com/Zepeng-Mu/ezGenomeTracks --skill user-test-zepeng-mu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: user-test
Source: https://github.com/Zepeng-Mu/ezGenomeTracks/tree/main/.github/skills/user-test
Command: npx skills add https://github.com/Zepeng-Mu/ezGenomeTracks --skill user-test-zepeng-mu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures the ezGenomeTracks R package functions correctly with real-world genomic data, validating its plotting and integration capabilities beyond simple unit tests.

Core Features & Use Cases

  • Data-driven Validation: Tests core track types (gene, feature, coverage, Hi-C, loop) using data from plotgardenerData.
  • Vignette Integrity: Verifies that all vignettes within the package compile without errors and produce correct visualizations.
  • Use Case: Before releasing a new version of ezGenomeTracks, run this Skill to confirm that all plotting functions work as expected with sample genomic datasets and that the documentation examples are accurate.

Quick Start

Run the user-test skill to validate the ezGenomeTracks package using example data and vignettes.

Frequently Asked Questions about user-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate an R package for genomic visualization with real datasets?▼

Validate a genomic visualization R package by testing core plotting components against external datasets and verifying vignette integrity to ensure functions compile without errors and produce correct visual outputs.

What is vignette integrity testing for R packages?▼

Vignette integrity testing for R packages verifies that all documentation vignettes compile without errors, ensuring code examples run correctly and produce accurate visualizations for users.

Do I need Bioconductor packages to test genomic plotting functions in R?▼

Yes, comprehensive testing of genomic plotting functions in R requires Bioconductor packages like GenomicRanges and rtracklayer, alongside R and devtools, to handle external datasets properly.

How do I test core track types like gene, feature, and Hi-C using sample data?▼

Test core track types like gene, feature, and Hi-C by applying data-driven validation techniques that use external datasets from plotgardenerData to execute plotting functions and confirm expected outputs.

When should I run data-driven validation for R package releases?▼

Run data-driven validation before releasing a new version of an R package to confirm that all plotting functions work as expected with sample datasets and that documentation examples remain accurate.

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