Microarray Based Gene Expression Analysis using R Programming

1. Develop R Proficiency: Enable participants to confidently use R for microarray data analysis, including data manipulation, statistical testing, and visualization.
2. Comprehend Microarray Technology: Provide a solid understanding of microarray technology, its applications, and its role in genomics research.
3. Master Data Preprocessing: Teach participants how to preprocess microarray data, covering normalization, quality control, and addressing data outliers.
4. Identify Differentially Expressed Genes: Equip participants with the skills to identify and interpret differentially expressed genes or features in microarray datasets.
5. Apply Statistical Analysis: Familiarize participants with statistical methods for hypothesis testing, p-value adjustments, and controlling for false positives in microarray analysis.
6. Visualize Results Effectively: Instruct participants in creating informative plots and visualizations to communicate microarray data analysis outcomes.
7. Conduct Pathway Analysis: Introduce tools and techniques for functional enrichment analysis and interpreting biological pathways related to gene expression changes.
8. Explore Machine Learning: Cover machine learning approaches for classification, prediction, and feature selection using microarray data.
9. Ensure Reproducibility: Stress the importance of documenting analysis workflows and adopting best practices for reproducible research.
10. Promote Biological Interpretation: Encourage participants to interpret their findings within the context of biological systems, facilitating the extraction of meaningful insights from microarray data.

Day 1

• Introduction to Microarray data analysis workflow
• Obtaining microarray data from GEO and TCGA
• Understanding data formats
• Setting up R for data analysis

Day 2

• Data Normalization
• Differential Gene Expression Analysis
• Annotation of DEGs
• Pathway Analysis of DEGs

Day 3

• Heatmap Generation
• Volcano Plot Generation
• Survival analysis- Kaplan-Meier Plot

Installation Requirements

1. Download the most recent versions of R and RStudio for your laptop:

• R
• RStudio

Graduates, Post Graduates, Research Scholars, Academicians, Industry Professionals of Biology, Genetics, Bioinformatics, Pharmaceutical and Biotech companies.