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
Gene Expression Analysis using R teaches the standard computational workflow behind modern transcriptomics. Starting from expression count data, you learn quality control, normalisation, and the statistics of differential expression using the field’s core Bioconductor packages, DESeq2 and edgeR. From there you move to interpretation — functional enrichment with GO and KEGG — and communication, producing the volcano plots, heatmaps and PCA that make results legible. Every step is hands-on in R with realistic data. You finish able to take an expression dataset from raw counts to a publication-ready analysis. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches gene expression analysis in R — from count data through normalisation and differential expression with DESeq2/edgeR to enrichment and visualisation.
📋 Course Objectives
1. Perform QC and normalisation of expression data.
2. Run differential expression with DESeq2 and edgeR.
3. Interpret results with GO and KEGG enrichment.
4. Produce volcano plots, heatmaps and PCA.
5. Build a reproducible RNA-seq analysis in R.
👥 Who Should Enroll?
• Molecular biologists and geneticists
• Bioinformatics students and researchers
• Core-facility and omics analysts
• Anyone analysing RNA-seq data
🚀 Key Learning Outcomes
• The ability to analyse gene expression data in R.
• A reproducible transcriptomics project.
• Publication-quality analysis skills.
• 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 Gene Expression Analysis Using R Programming and Core Biological Principles
Analyze gene expression data using R programming to identify differentially expressed genes • Develop a comprehensive understanding of core biological principles underlying gene expression analysis • Configure R programming environments to perform gene expression data analysis and visualization
Laboratory Techniques, Protocols, and Data Collection
Design and implement laboratory experiments to collect gene expression data using various techniques such as PCR and microarray analysis • Evaluate the quality and integrity of gene expression data collected from laboratory experiments • Develop a protocol for data collection and management to ensure reproducibility and accuracy
Bioinformatics Tools and Computational Analysis
Implement bioinformatics tools such as BLAST and GenBank to analyze gene expression data • Analyze gene expression data using computational methods such as clustering and dimensionality reduction • Configure bioinformatics pipelines to perform gene expression data analysis and visualization
Research Methodology and Experimental Design
Develop a research hypothesis and design an experiment to test the hypothesis using gene expression analysis • Evaluate the statistical significance of gene expression data using various statistical tests • Configure experimental designs to account for variability and bias in gene expression data analysis
Advanced Gene Expression Analysis Using R Programming Applications and Translational Research
Apply advanced R programming techniques such as machine learning and deep learning to analyze gene expression data • Develop a comprehensive understanding of translational research and its applications in gene expression analysis • Design and implement R programming scripts to perform gene expression data analysis and visualization for translational research
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate the regulatory compliance and bioethics of gene expression analysis research • Develop a protocol for ensuring safety standards in laboratory experiments involving gene expression analysis • Configure laboratory procedures to comply with regulatory requirements and bioethics guidelines
Industry Applications, Career Pathways, and Case Studies
Analyze industry applications of gene expression analysis and their impact on biomedical research • Develop a career pathway in gene expression analysis and bioinformatics • Evaluate case studies of gene expression analysis in various industries such as pharmaceuticals and biotechnology
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | GenBank |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
