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
Programming in R to Analyze Biological Data equips life scientists to turn biological datasets into insight with code. You learn R from a biology-first angle: handling and tidying biological data, applying the statistics that life-science analysis needs, and producing clear, publication-quality visualisations. Examples are drawn from real biological data rather than generic tutorials, so the skills transfer directly to research. You finish able to analyse and visualise biological data confidently in R. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches R programming to analyse biological data — from data handling and statistics to visualisation of biological datasets, built for life scientists.
📋 Course Objectives
1. Handle and tidy biological datasets in R.
2. Apply life-science statistics.
3. Test hypotheses on biological data.
4. Produce publication-quality visualisations.
5. Build reproducible R analyses.
👥 Who Should Enroll?
• Biologists and life-science researchers
• Wet-lab scientists moving to analysis
• Bioinformatics students
• Anyone analysing biological data
🚀 Key Learning Outcomes
• The ability to analyse biological data in R.
• A reproducible biology-analysis workflow.
• Strong statistical-visualisation 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 Programming In R To Analyze Biological Data and Core Biological Principles
Configure the R programming environment, including RStudio, Bioconductor, and essential packages like tidyverse for biological data manipulation. • Manipulate core R data structures such as vectors, matrices, data frames, and lists to parse high-throughput biological sequencing files. • Implement custom control structures and vectorization techniques in R to automate the parsing of genomic coordinate files.
Laboratory Techniques, Protocols, and Data Collection
Programmatically clean and preprocess raw intensity data from microarray experiments and plate readers using the limma and affy packages. • Map experimental laboratory metadata structures to standardized R tidy data frames to ensure reproducible links to downstream molecular assays. • Develop quality control pipelines using R to identify and filter out technical artifacts, outliers, and batch effects in PCR and sequencing datasets.
Bioinformatics Tools and Computational Analysis
Perform differential gene expression analysis on high-throughput RNA-Seq count matrices using statistical frameworks in DESeq2 and EdgeR. • Build phylogenetic trees and conduct sequence alignment analysis utilizing Biostrings, msa, and ape packages in R. • Execute cluster analysis and principal component analysis (PCA) on high-dimensional genomic datasets to identify molecular subtypes.
Research Methodology and Experimental Design
Design robust statistical power analysis models in R using the pwr package to determine optimal sample sizes for clinical and genomic studies. • Implement randomized block design and multi-factor ANOVA frameworks in R to control for confounding variables in biological experiments. • Formulate statistical hypothesis testing pipelines, applying false discovery rate (FDR) corrections like Benjamini-Hochberg to large-scale biological screens.
Advanced Programming In R To Analyze Biological Data Applications and Translational Research
Develop predictive machine learning models for clinical classification of genomic profiles using the caret and randomForest R libraries. • Construct interactive biological network visualizations and pathway enrichment maps using igraph, RCy3, and clusterProfiler. • Process single-cell RNA-sequencing (scRNA-seq) datasets, executing cell-clustering and marker gene identification via the Seurat framework.
Regulatory Compliance, Bioethics, and Safety Standards
Implement data de-identification and anonymization protocols on clinical datasets in R to comply with HIPAA and GDPR regulations. • Generate automated, reproducible audit trails and compliance reports for computational workflows using R Markdown and knitr. • Program data verification scripts to validate genomic database integrity against international standard reference databases like NCBI and Ensembl.
Industry Applications, Career Pathways, and Case Studies
Analyze real-world pharmaceutical screening datasets to identify lead drug candidates using quantitative structure-activity relationship models in R. • Build scalable pipeline architectures integrating R scripts with command-line bioinformatic tools for industrial pipeline integration. • Create dynamic, production-grade Shiny dashboards to present molecular assay findings to cross-functional R&D and clinical stakeholders.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | DESeq2 |
| Covered Tool / Platform | Seurat |
| Covered Tool / Platform | ggplot2 |
| Covered Tool / Platform | Shiny |
| Covered Tool / Platform | Git |
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