Microarray Based Gene Expression Analysis using R Programming Course

About

The Microarray Data Analysis using the 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.

Program Structure

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:
• R
• RStudio

Participant’s Eligibility

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

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.

Program Deliverables

• Access to e-LMS
• Real-Time Project for Dissertation
• Project Guidance
• Paper Publication Opportunity
• Self Assessment
• Final Examination
• e-Certification
• e-Marksheet