School of Bioinformatics · Programming & data
R for Bioinformatics & Biostatistics
R remains the language of choice for statistical analysis of biological data and hosts Bioconductor, the largest collection of genomics packages. This course combines R programming with the statistics needed to analyse experiments correctly.
Course overview
R for Bioinformatics & Biostatistics combines the R language with the statistics needed to analyse experiments correctly. It suits biologists who will analyse their own data, and anyone heading towards RNA-seq, microbiome or other Bioconductor-based work.
Statistics are taught as decisions: which test fits the design, what its assumptions are, and how multiple testing changes what a p-value means. Every analysis is written as a Quarto document, so code, results and interpretation stay together and can be re-run by anyone.
Learning outcomes
By the end of the course you will be able to:
- Write R code with the tidyverse
- Choose, run and check the right statistical test for a design
- Control the false discovery rate across many tests
- Make clear, accessible figures with ggplot2
- Write reproducible reports with Quarto
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- Basic biology and school-level mathematics
- No programming required
Best suited to
- Biology and biotechnology students
- Researchers analysing experimental or omics data
- Anyone heading towards RNA-seq or microbiome analysis
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Vectors, data frames and factors
- Functions and packages
- RStudio projects
Topics
- dplyr for transforming data
- tidyr for reshaping
- Reading and writing files
Topics
- Distributions and hypothesis tests
- Regression and ANOVA
- Multiple testing and the false discovery rate
Topics
- The grammar of graphics
- Faceting and themes
- Figures that survive peer review
Topics
- SummarizedExperiment and GRanges
- Annotation packages
- A first differential analysis
Topics
- Quarto documents
- Session information and package versions
- Publishing the report
Practical sessions and projects
Capstone project
Reproduce a published figure
Download the data behind a published figure, reproduce it in R and write the analysis up as a reproducible Quarto report, noting any point where your result differs.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- R fundamentals
Set up an RStudio project, import a plate-reader export and compute summary statistics per condition.
- The tidyverse
Reshape a wide experimental table to long format, join sample metadata and compute group-wise summaries.
- Statistics for biologists
Choose and run t-tests, Wilcoxon tests, ANOVA and linear models on designed experiments, then apply Benjamini–Hochberg correction.
- Visualisation with ggplot2
Rebuild a multi-panel figure from raw data with consistent scales and colour-blind-safe palettes.
- Bioconductor
Load the airway dataset as a SummarizedExperiment and run a first differential analysis of treated against untreated cells.
- Project: a reproducible analysis report
Reproduce a published figure from its underlying data and publish the analysis as a Quarto report with session information.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- airway: SummarizedExperiment and a first differential analysis.Bioconductor (Himes et al. 2014, GEO GSE52778)
- PlantGrowth and ToothGrowth: ANOVA and linear models on designed experiments.R datasets package
- Supplementary tables from open-access papers: Reproducing a published figure.PubMed Central
Core reading
- Wickham H, Çetinkaya-Rundel M, Grolemund G. R for Data Science, 2nd edition. O’Reilly, 2023.
- Holmes S, Huber W. Modern Statistics for Modern Biology. Cambridge University Press, 2019.
- Huber W et al. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods 12:115–121.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for R for Bioinformatics & Biostatistics, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for R for Bioinformatics & Biostatistics
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
The focus is on choosing the right method and interpreting it correctly. The mathematics is explained where it changes how a method is used, for example why a t-test needs roughly normal residuals or why many tests need FDR control.
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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