Microarray Based Gene Expression Analysis using R Programming
Microarray, Data Analysis, R, Genomics, Differential Expression, Pathway Analysis, Clustering, Machine Learning, Data Preprocessing, Reproducibility.
The Microarray Data Analysis using R workshop offers a comprehensive exploration of microarray data analysis techniques within the context of genomics, with a strong focus on leveraging the R programming language. Participants will gain a deep understanding of fundamental concepts such as data preprocessing, differential expression analysis, clustering, pathway analysis, and machine learning. Through engaging presentations, real-world case studies, and expert insights, attendees will acquire the knowledge and skills needed to effectively analyze microarray data, making it a valuable learning opportunity for researchers, data analysts, and bioinformaticians in the genomics field.
Aim: The aim of the “Microarray Data Analysis using R” workshop is to equip participants with the practical skills and knowledge required to proficiently analyze microarray data using the R programming language, enabling them to uncover meaningful insights, identify differentially expressed genes, and effectively contribute to genomics research and data-driven decision-making in both academic and professional settings.
- Develop R Proficiency: Enable participants to confidently use R for microarray data analysis, including data manipulation, statistical testing, and visualization.
- Comprehend Microarray Technology: Provide a solid understanding of microarray technology, its applications, and its role in genomics research.
- Master Data Preprocessing: Teach participants how to preprocess microarray data, covering normalization, quality control, and addressing data outliers.
- Identify Differentially Expressed Genes: Equip participants with the skills to identify and interpret differentially expressed genes or features in microarray datasets.
- Apply Statistical Analysis: Familiarize participants with statistical methods for hypothesis testing, p-value adjustments, and controlling for false positives in microarray analysis.
- Visualize Results Effectively: Instruct participants in creating informative plots and visualizations to communicate microarray data analysis outcomes.
- Conduct Pathway Analysis: Introduce tools and techniques for functional enrichment analysis and interpreting biological pathways related to gene expression changes.
- Explore Machine Learning: Cover machine learning approaches for classification, prediction, and feature selection using microarray data.
- Ensure Reproducibility: Stress the importance of documenting analysis workflows and adopting best practices for reproducible research.
- Promote Biological Interpretation: Encourage participants to interpret their findings within the context of biological systems, facilitating the extraction of meaningful insights from microarray data.
What you will learn?
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
- Download the most recent versions of R and RStudio for your laptop:
Intended For :
Graduates, Post Graduates, Research Scholars, Academicians, Industry Professionals of Biology, Genetics, Bioinformatics, Pharmaceutical and Biotech companies.
Career Supporting Skills