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bio-rna-quantification-count-matrix-qc

Perform quality control on RNA-seq count matrices with PCA and heatmaps.

Updated Feb 3, 2026
Search Tags:#quality control#pca#rna-seq#deseq2#qc#outlier detection#count matrix
One-click install
npx skills add https://github.com/Revelo-RNASeq/2025_RNASeq --skill bio-rna-quantification-count-matrix-qc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-rna-quantification-count-matrix-qc
Source: https://github.com/Revelo-RNASeq/2025_RNASeq/tree/main/.agents/skills/bio-rna-quantification-count-matrix-qc
Command: npx skills add https://github.com/Revelo-RNASeq/2025_RNASeq --skill bio-rna-quantification-count-matrix-qc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need for quality control of RNA-seq count matrices before downstream differential expression analysis, helping to identify and mitigate potential issues that could skew results.

Core Features & Use Cases

  • Data Inspection: Load and inspect raw count data and sample metadata.
  • Quality Metrics: Calculate basic statistics, filter low-count genes, and normalize data for visualization.
  • Visualization: Generate heatmaps for sample correlation and PCA plots to assess sample relationships and batch effects.
  • Outlier Detection: Identify potential outlier samples based on QC metrics.
  • Use Case: Before running DESeq2 for differential expression, use this Skill to confirm that biological replicates cluster together, that there are no obvious batch effects, and that no samples are extreme outliers.

Quick Start

Run quality control on my RNA-seq count matrix to check for outliers and batch effects.

Frequently Asked Questions about bio-rna-quantification-count-matrix-qc

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

FAQPage Schema
How do I perform quality control on an RNA-seq count matrix before differential expression analysis?▼

To perform RNA-seq count matrix quality control, load raw counts and sample metadata to calculate basic statistics, filter low-count genes, and normalize data. This assesses data integrity before downstream analysis.

What is the best way to detect outliers and batch effects in RNA-seq data?▼

Detecting outliers and batch effects in RNA-seq data is best achieved by generating PCA plots and sample correlation heatmaps. These visualizations reveal sample relationships and highlight extreme deviations from biological replicates.

Can I use DESeq2 for RNA-seq count matrix quality control and exploratory analysis?▼

Yes, you can use DESeq2 for RNA-seq count matrix quality control and exploratory analysis. It provides R-based normalization, statistical summaries, and visualization techniques to assess sample relationships and data integrity.

Does RNA-seq quality control work with Python and scikit-learn?▼

RNA-seq quality control works with Python and scikit-learn for robust data inspection and outlier detection. Pandas and scikit-learn provide alternative Python-based analysis alongside DESeq2 R-based workflows.

Why do my biological replicates fail to cluster together in PCA plots during RNA-seq QC?▼

Biological replicates failing to cluster together in PCA plots during RNA-seq QC indicates severe batch effects or extreme outlier samples. This exploratory analysis reveals potential data integrity issues requiring mitigation.

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