Practical RNA-seq analysis

Overview

Programme overview

Participants move from an RNA-seq count matrix and sample metadata to a quality-assessed differential-expression analysis with an auditable R project and interpretable report.

Audience

Who should attend

Molecular bioscientists, postgraduate researchers and laboratory teams with count matrices or sequencing projects who need to understand a defensible differential-expression workflow.

Capability gained

Learning outcomes

• Organise counts and metadata into a reproducible project
• Identify design and metadata problems before modelling
• Perform exploratory quality assessment and interpret sample-level patterns
• Specify and run a count-based differential-expression analysis
• Examine effect sizes, uncertainty, multiple testing and diagnostic plots
• Produce a concise methods and results record that another analyst can rerun

Structure

Curriculum

1. 01What a count matrix represents: inputs, assumptions and provenance
2. 02Project structure, metadata validation and analysis questions
3. 03Normalisation, transformations and exploratory sample assessment
4. 04Experimental design, contrasts and common confounding failures
5. 05Differential expression with an established Bioconductor workflow
6. 06Effect size, adjusted significance, visualisation and result triage
7. 07Reproducible reports, session information and hand-off

Preparation

Prerequisites

Basic R data-frame and plotting skills; a laptop capable of running the approved environment. Exercises use an ASR-supplied, non-sensitive teaching dataset.