School of Bioinformatics · Systems & multi-omics
Multi-Omics Data Integration
Most modern studies measure more than one molecular layer. This course teaches how to combine them properly: when integration helps, which method suits which design, and how to validate what an integrated model claims.
Course overview
Multi-Omics Data Integration starts with study design and data preparation, the steps that decide whether integration is possible at all: matched samples, batch structure, normalisation per layer and missing data.
You then apply the main families of methods (factor models such as MOFA+, supervised integration with mixOmics DIABLO, and network-based approaches) to public cohorts, interpret the factors and signatures they produce, and test them on held-out data.
Learning outcomes
By the end of the course you will be able to:
- Decide when and how multi-omics integration is appropriate
- Prepare, normalise and harmonise several omics layers
- Run unsupervised factor analysis with MOFA+ and interpret the factors
- Build supervised multi-omics signatures with DIABLO
- Validate integrated results on held-out data and report them clearly
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
- R for Bioinformatics or equivalent
- RNA-seq Data Analysis or similar experience
Best suited to
- Researchers with two or more omics layers on the same samples
- Bioinformaticians supporting translational cohorts
- PhD students in systems biology
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Matched versus unmatched designs
- Sample size and batch structure
- Questions integration can answer
Topics
- Per-layer normalisation and filtering
- Missing values
- Feature selection
Topics
- Factor models and MOFA+
- Variance explained per layer
- Annotating factors
Topics
- sPLS-DA and DIABLO
- Tuning and cross-validation
- Signature stability
Topics
- Correlation and knowledge-based networks
- Joint pathway analysis
- Visualising integrated results
Topics
- Analysis plan
- Integration
- Validation
Practical sessions and projects
Capstone project
Integrate a cohort
Combine at least two omics layers from a public cohort, validate the integrated signature on held-out samples and explain what integration revealed.
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.
- Designing for integration
Review public multi-omics cohorts and decide which questions each design can support.
- Preparing the layers
Prepare transcript, protein and methylation layers from a public cancer cohort for integration.
- Unsupervised integration
Fit a MOFA+ model and link its factors to clinical variables and pathways.
- Supervised integration
Build a multi-omics classifier of tumour subtype and measure its performance honestly.
- Networks and pathways
Map the integrated signature onto pathways and build a summary figure.
- Project: an integrated study
Integrate a public multi-omics dataset end to end and report what the combined analysis adds beyond each layer alone.
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
- TCGA multi-omics cohorts: Integration practicals.GDC Data Portal (open-access tier)
- Public CLL multi-omics data: Factor analysis.MOFA tutorial data (Bioconductor)
Core reading
- Argelaguet R et al. (2020). MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biology 21:111.
- Rohart F, Gautier B, Singh A, Lê Cao KA (2017). mixOmics: an R package for ’omics feature selection and multiple data integration. PLOS Computational Biology 13:e1005752.
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 Multi-Omics Data Integration, 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 Multi-Omics Data Integration
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
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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