Microarray Based Gene Expression Analysis using R Programming
Microarray, Data Analysis, R, Genomics, Differential Expression, Pathway Analysis, Clustering, Machine Learning, Data Preprocessing, Reproducibility.
About Program:
The Microarray Data Analysis using R program 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” program 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.
Program Objectives:
- 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:
Fee Plan
Intended For :
Graduates, Post Graduates, Research Scholars, Academicians, Industry Professionals of Biology, Genetics, Bioinformatics, Pharmaceutical and Biotech companies.
Career Supporting Skills
Program Outcomes
- Proficiency in R: Participants gain a strong foundation in R programming, allowing them to manipulate, visualize, and analyze microarray data efficiently.
- Understanding of Microarray Technology: Attendees develop a comprehensive understanding of microarray technology, including its principles, applications, and data generation processes.
- Data Preprocessing Expertise: Participants learn to clean and preprocess microarray data effectively, addressing issues such as normalization, quality control, and missing data.
- Differential Expression Analysis Skills: Participants can identify and interpret differentially expressed genes or features in microarray datasets, a critical skill in genomics research.
- Statistical Analysis Competence: Attendees become proficient in statistical methods for hypothesis testing, p-value corrections, and multiple testing adjustments related to microarray data.
- Data Visualization Capability: Participants acquire the ability to create informative plots and visualizations to communicate their microarray data analysis findings effectively.
- Pathway Analysis Proficiency: Attendees gain skills in functional enrichment analysis and the interpretation of biological pathways associated with gene expression changes.
- Machine Learning Application: Participants learn how to apply machine learning techniques for classification, prediction, and feature selection using microarray data.
- Reproducibility Emphasis: Attendees understand the importance of reproducibility and adopt best practices for documenting and sharing their analysis workflows.
- Biological Interpretation Skills: Participants can interpret analysis results within the context of biological systems, extracting meaningful insights from microarray data.
