Aim
Microarray Based Gene Expression Analysis using R Programming teaches end-to-end analysis of microarray data using R. Learn experimental design concepts, data preprocessing, normalization, differential expression analysis, visualization, and biological interpretation.
Program Objectives
- Microarray Basics: probe design, platforms, and expression measures.
- R for Genomics: data handling and Bioconductor workflows.
- Preprocessing: background correction and normalization.
- Differential Expression: statistical testing and result interpretation.
- Visualization: plots for QC and biological insight.
- Annotation: gene mapping and functional context.
- Capstone: complete microarray analysis report.
Program Structure
Module 1: Microarray Technology Overview
- Types of microarrays and platforms.
- Experimental design and replicates.
- Common sources of bias and noise.
Module 2: R and Bioconductor Setup
- R environment and package setup.
- Key Bioconductor packages (overview).
- Importing raw microarray data.
Module 3: Data Preprocessing and QC
- Background correction and filtering.
- Normalization methods (RMA, quantile).
- Quality control plots and diagnostics.
Module 4: Differential Gene Expression Analysis
- Design matrices and contrasts.
- Linear models and statistical testing.
- Multiple testing correction (FDR).
Module 5: Visualization of Results
- Boxplots, MA plots, volcano plots.
- Heatmaps and clustering concepts.
- Interpreting expression patterns.
Module 6: Annotation and Functional Analysis
- Gene annotation and ID mapping.
- Pathway and GO enrichment (intro).
- Biological interpretation of results.
Module 7: Reporting and Best Practices
- Reproducible analysis and documentation.
- Common pitfalls in microarray studies.
- Preparing publication-ready outputs.
Final Project
- Analyze a microarray dataset using R.
- Deliverables: QC plots, DEG list, visualizations, short report.
- Submit: complete analysis report.
Participant Eligibility
- Students and researchers in Biotechnology, Genetics, Bioinformatics
- Basic knowledge of molecular biology
- Introductory R knowledge helpful
Program Outcomes
- Perform microarray data analysis using R.
- Identify and interpret differentially expressed genes.
- Create clear visualizations and reports.
- Build a portfolio-ready gene expression project.
Program Deliverables
- e-LMS Access: lessons, datasets, scripts.
- Toolkit: R scripts, QC checklist, report template.
- Assessment: certification after project submission.
- e-Certification and e-Marksheet: digital credentials.
Future Career Prospects
- Bioinformatics Analyst (Expression Data)
- Genomics Research Assistant
- Computational Biology Trainee
- Academic Research Associate
Job Opportunities
- Research Labs: gene expression and functional genomics.
- CROs: transcriptomics data analysis support.
- Biotech: biomarker discovery teams.
- Universities: genomics and systems biology labs.
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