Description
RNA-Seq Practicals Bundle
Four hands-on practicals for moving from RNA-Seq count data to statistical results, biological interpretation, and reproducible reporting
RNA-Seq analysis involves more than running one statistical test. A reliable workflow requires careful data inspection, differential expression modelling, biological interpretation, experimental-design awareness, and clear reporting.
This bundle brings those skills together through four structured CDI Practicals that can be completed individually or followed as a connected RNA-Seq learning pathway.
You will begin with count matrices, sample metadata, and completed analysis outputs rather than raw FASTQ files. This keeps the practicals focused on statistical analysis, interpretation, visualization, experimental design, and reproducible communication.
The bundle is designed for learners, researchers, students, laboratory scientists, and early-career analysts who want practical experience with RNA-Seq analysis using R and widely used bioinformatics tools.
What is included
1. RNA-Seq Differential Expression Practical
Build a complete differential expression workflow from an RNA-Seq count matrix and sample metadata.
- inspect count data and metadata
- check sample consistency and library sizes
- filter low-count genes
- normalize counts with DESeq2
- explore samples using PCA and correlation analysis
- fit a differential expression model
- interpret log2 fold changes, p-values, and adjusted p-values
- create MA plots, volcano plots, heatmaps, and result tables
2. RNA-Seq Functional Enrichment Practical
Turn differential expression results into pathways, biological processes, and structured interpretation.
- prepare significant gene lists
- map gene identifiers
- define an appropriate background gene universe
- run Gene Ontology enrichment analysis
- examine pathway-level findings
- interpret gene ratios and adjusted p-values
- create enrichment dot plots and gene-concept networks
- write evidence-based biological interpretations
3. RNA-Seq Statistical Analysis Practical
Understand how batch effects, sample allocation, and experimental design influence RNA-Seq conclusions.
- compare balanced, imbalanced, and confounded designs
- inspect condition and batch assignments
- create sample-allocation and contingency tables
- visualize sample structure using PCA and distance heatmaps
- build and compare DESeq2 design formulas
- evaluate models with and without batch adjustment
- recognize confounding and model-identifiability problems
- interpret effect sizes, uncertainty, and model reliability
4. RNA-Seq Reproducible Reporting Practical
Bring analysis code, figures, tables, methods, interpretation, and reproducibility details into one clear report.
- organize RNA-Seq project files and outputs
- document the research question and analysis workflow
- present quality-control findings
- summarize differential expression results
- embed publication-ready figures and formatted tables
- write statistical and biological interpretations
- document limitations, package versions, and session details
- produce a browser-ready Quarto report
Skills covered across the bundle
- RNA-Seq count-matrix analysis
- sample metadata inspection
- normalization and exploratory analysis
- differential expression modelling
- effect-size and adjusted p-value interpretation
- Gene Ontology and pathway enrichment
- gene identifier mapping
- batch-effect recognition
- experimental-design evaluation
- confounding and model comparison
- publication-ready visualization
- reproducible reporting with Quarto
- biological interpretation and limitations
Figures and outputs
Across the four practicals, you will create and work with outputs such as:
- library-size plots
- sample-correlation heatmaps
- PCA plots
- sample-distance heatmaps
- MA plots
- volcano plots
- normalized count tables
- differential expression result tables
- Gene Ontology enrichment tables
- pathway enrichment results
- enrichment dot plots
- gene-concept network plots
- model-comparison figures
- experimental-design summaries
- a reproducible browser-ready RNA-Seq report
These outputs can support research documentation, teaching, manuscript preparation, supervisor discussions, portfolio development, and further bioinformatics learning.
Tools used
The bundle uses R, Quarto, and widely used packages such as:
- DESeq2
- clusterProfiler
- enrichplot
- org.Hs.eg.db
- AnnotationDbi
- DOSE
- ggplot2
- dplyr
- readr
- pheatmap
- tibble
- knitr
- Quarto
The practicals begin with RNA-Seq count matrices, sample metadata, differential expression results, or completed analysis outputs. Raw FASTQ processing, alignment, and read-level quality control are intentionally excluded.
Who this bundle is for
- undergraduate and postgraduate students
- researchers beginning RNA-Seq analysis
- laboratory scientists working with sequencing results
- bioinformatics learners
- data analysts entering transcriptomics and omics research
- research teams strengthening analysis and reporting practices
- mentors and instructors teaching RNA-Seq workflows
Basic familiarity with R is helpful, but each practical is written step by step and includes reusable code, realistic examples, statistical explanations, interpretation guidance, and organized outputs.
What you will complete
By completing the bundle, you will have:
- a complete RNA-Seq differential expression workflow
- a reproducible functional enrichment workflow
- experience evaluating batch effects and experimental designs
- experience comparing RNA-Seq statistical models
- publication-ready figures and organized result tables
- structured statistical and biological interpretations
- a browser-ready reproducible RNA-Seq report
- portfolio-ready evidence across four connected bioinformatics practicals
Each practical is browser-first, self-paced, and available through a separate access link so that you can complete the bundle in the order that best fits your learning or research needs.
