Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Microarray Based Gene Expression Analysis using R Programming teaches the analysis of a foundational expression-profiling technology. You learn the microarray-specific workflow in R and Bioconductor: reading raw intensity data, background correction and normalisation, quality assessment, and differential-expression analysis with the appropriate statistics, plus functional interpretation. The course centres on the methods and pitfalls unique to microarray data. You finish able to run a microarray gene-expression analysis end to end in R. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers microarray-based gene-expression analysis using R — the microarray workflow from raw intensity data through normalisation to differential expression in R.
📋 Course Objectives
1. Read and process raw microarray data.
2. Background-correct and normalise intensities.
3. Assess array quality.
4. Run differential-expression analysis.
5. Interpret results functionally.
👥 Who Should Enroll?
• Molecular biologists and geneticists
• Bioinformatics students and staff
• Expression-profiling researchers
• Anyone analysing microarray data
🚀 Key Learning Outcomes
• The ability to analyse microarray data in R.
• An expression-profiling perspective.
• A reproducible R workflow.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Microarray Based Gene Expression Analysis Using R Programming and Core Biological Principles
Analyze the fundamental principles of microarray technology and its applications in gene expression analysis • Develop a comprehensive understanding of the R programming language and its libraries for bioinformatics analysis • Evaluate the importance of data quality control and preprocessing in microarray-based gene expression analysis
Laboratory Techniques, Protocols, and Data Collection
Design and implement laboratory protocols for microarray-based gene expression analysis, including RNA extraction and hybridization • Configure and operate microarray scanning and imaging equipment to generate high-quality data • Develop a workflow for data collection, storage, and management in compliance with regulatory standards
Bioinformatics Tools and Computational Analysis
Implement bioinformatics tools, such as Bioconductor and limma, to analyze and visualize microarray data • Analyze and interpret gene expression data using statistical methods, including hypothesis testing and differential expression analysis • Develop a pipeline for data integration and analysis using R programming and bioinformatics libraries
Research Methodology and Experimental Design
Design and evaluate experimental designs for microarray-based gene expression analysis, including sample size calculation and power analysis • Develop a comprehensive understanding of research methodology, including hypothesis testing and statistical analysis • Configure and implement quality control measures to ensure data integrity and reliability
Advanced Microarray Based Gene Expression Analysis Using R Programming Applications and Translational Research
Develop and apply advanced bioinformatics techniques, such as machine learning and network analysis, to microarray data • Analyze and interpret gene expression data in the context of translational research, including disease diagnosis and treatment • Evaluate the applications of microarray-based gene expression analysis in personalized medicine and precision health
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate and implement regulatory compliance measures, including HIPAA and IRB guidelines, in microarray-based gene expression analysis • Develop a comprehensive understanding of bioethics principles, including informed consent and data privacy • Configure and implement safety standards, including laboratory safety protocols and emergency procedures
Industry Applications, Career Pathways, and Case Studies
Analyze and evaluate industry applications of microarray-based gene expression analysis, including pharmaceutical and biotechnology research • Develop a comprehensive understanding of career pathways and job opportunities in bioinformatics and genomics • Evaluate and discuss case studies of successful applications of microarray-based gene expression analysis in industry and academia
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | limma |
| Covered Tool / Platform | microarray scanning equipment |
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