“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.