MicroArray Analysis

“Microarray Analysis and Differential Expression with R” is a comprehensive course that teaches participants to analyze microarray data and perform differential expression analysis using R. The course covers the entire workflow, including data retrieval, preprocessing, quality control, normalization, and differential expression analysis.

It starts with an introduction to microarray technology, its principles, and applications. Participants learn the basics of R programming and its significance in microarray analysis. They retrieve raw data from microarray databases for analysis, whether related to cancer or other diseases.

Quality control ensures data reliability and accuracy. Participants learn quality control measures, assess data quality, and explore normalization techniques to enhance comparability.

Differential expression analysis identifies genes differentially expressed between experimental conditions. Statistical methods are taught, along with hands-on experience in implementing them using R. Participants interpret and visualize results to gain biological insights.

The course includes practical examples, exercises, and case studies. By the end, participants confidently analyze microarray data, perform differential expression analysis, and extract insights from their own datasets.

Whether students, researchers, or professionals in genomics or other biological disciplines, this course empowers them to leverage microarray technology and R programming for robust gene expression analysis. Join us and unlock new possibilities in research and disease understanding.

Learning Objectives:

1. Introduction to Microarray Analysis & R:

• Understand microarray principles and applications
• Learn R basics for microarray analysis
• Retrieve and preprocess microarray data in R

2. Quality Control & Normalization:

• Learn quality control measures and their significance
• Assess microarray data quality and identify variations
• Explore normalization methods using R

3. Differential Expression Analysis:

• Understand differential gene expression and its importance
• Learn statistical methods for identifying differentially expressed genes
• Perform differential expression analysis using R
• Interpret and visualize results for biological insights.

Microarray analysis and differential expression in R

Microarrays predate RNA-Seq but remain widely used, and — more importantly — the public archives hold decades of microarray data. GEO and ArrayExpress contain tens of thousands of experiments that have never been reanalysed. This focused course, about 1 hour 20 minutes across three sections, teaches you to work with that data in R.

What you work through

• Microarray analysis and R — how hybridisation-based measurement differs from sequencing, and what that means for the statistics.
• Quality control and normalisation — the step that matters most. Array data carries technical variation that will dominate your results if left uncorrected, and the normalisation choice changes which genes appear significant.
• Differential expression analysis — identifying genes that genuinely differ between conditions, and interpreting the result.

Why microarrays still matter

Two practical reasons. First, the archives: if your research question has been studied before, there is a good chance the data already exists and is free. Reanalysing public microarray data is one of the cheapest ways to generate a real result or a pilot for a grant. Second, cost — for targeted questions on large sample numbers, arrays remain economical.

The statistical reasoning also transfers directly. Normalisation, multiple testing correction and experimental design work the same way in RNA-Seq; learning them on the simpler platform makes the sequencing version easier.

What you can do afterwards

Download a microarray dataset from GEO, assess its quality, normalise it appropriately, run differential expression in R, and interpret the output — including knowing when the data is not good enough to use.

Who it suits

Researchers with existing array data, anyone wanting to mine public expression archives, and students who want the statistics of expression analysis on a platform with fewer moving parts than RNA-Seq.