hands-on tutorial, E-Learning
RNA-seq | From Counts 2 Biological Insights
Overview
This hands-on tutorial introduces a reproducible and interpretable RNA‑Seq analysis workflow using R and R Markdown. Starting from a count matrix, participants will learn to assess data quality, perform differential expression analysis, and interpret biological results through visualization and enrichment analysis. The tutorial is designed for learners with basic R experience and provides a step-by-step guide to key concepts in transcriptomics data analysis.
Questions Addressed
Which genes are significantly differentially expressed between experimental groups?
How can we link differentially expressed genes to biological processes and functions?
How to visualize the outcome of the various steps of the transcriptomics data analysis?
Learning Objectives
Use R Markdown notebooks for literate programming, integrating code, results, and documentation in a single, reproducible workflow.
Assess quality at the gene count level, using PCA, sample clustering, and diagnostic plots to identify outliers, batch effects, and validate experimental design.
Perform differential expression analysis with DESeq2, identifying genes with statistically significant changes between conditions.
Visualize and interpret gene expression results, producing heatmaps, volcano plots, and summary statistics to communicate findings.
Conduct functional enrichment analysis, mapping gene-level results to biological processes, molecular functions, and pathways (e.g. Gene Ontology enrichment).
Licence: Creative Commons Attribution 4.0 International
Contact: iduarte.scientist@gmail.com
Keywords: RNA-Seq, bulk, mouse, Quality Control, PCA, Functional enrichment, Visualisation
Target audience: Students, Researchers, Biologists, Bioinformaticians
Resource type: hands-on tutorial, E-Learning
Version: V1
Status: Active
Prerequisites:
Basic R knowledge
Learning objectives:
- Learn to use R markdown notebooks for literate programming.
- Assess quality at the gene count level: Use exploratory analyses (e.g. PCA, clustering) to evaluate sample quality, detect batch effects, and validate the experimental design.
- Conduct differential expression analysis: Identify differentially expressed genes using DESeq2.
- Visualize and interpret results: Create informative plots (e.g. heatmaps, volcano plots) to explore gene expression changes.
- Perform functional enrichment analysis: Link gene expression results to biological functions, pathways, and processes (e.g. GO BP enrichment).
Date created: 2025-06-01
Date modified: 2025-06-01
Date published: 2025-06-01
Contributors: rmagno@pattern.institute
Scientific topics: Molecular genetics, Gene expression, Transcriptomics, RNA-Seq
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