School of Bioinformatics · Genomics & transcriptomics
Spatial Transcriptomics Analysis
Spatial transcriptomics measures gene expression while keeping track of where in a tissue it occurs. This course covers the analysis of spot-based and imaging-based platforms and how to combine them with single-cell references.
Course overview
Spatial Transcriptomics Analysis builds on single-cell methods and adds what is new: tissue images, spatial neighbourhoods, statistics for spatial patterns and the problem of spots that contain several cells.
You analyse public spot-based and imaging-based datasets, identify tissue domains and spatially variable genes, estimate cell-type composition by deconvolution against a single-cell reference, and explore ligand–receptor communication between neighbouring cells.
Learning outcomes
By the end of the course you will be able to:
- Explain spot-based and imaging-based spatial technologies and their resolution
- Perform QC, normalisation and spatially aware clustering
- Detect spatially variable genes and tissue domains
- Deconvolve spots with a single-cell reference
- Analyse neighbourhoods and cell–cell communication
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
- Single-Cell RNA-seq Analysis or equivalent
- Python or R
Best suited to
- Researchers with Visium, Xenium, MERFISH or similar data
- Single-cell analysts moving into spatial omics
- Pathologists and tissue biologists working with omics teams
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Spot-based capture versus in-situ imaging
- Resolution, sensitivity and panel design
- Data structures
Topics
- Spot and cell QC
- Normalisation
- Spatially aware clustering
Topics
- Moran’s I and spatial autocorrelation
- Spatially variable genes
- Domain markers
Topics
- Mixed spots
- Reference-based methods (cell2location, RCTD)
- Validating estimates
Topics
- Neighbourhood enrichment
- Ligand–receptor inference
- Caveats of communication tools
Topics
- Analysis
- Integration
- Figure design
Practical sessions and projects
Capstone project
Map a tissue
Analyse a public spatial dataset to define tissue domains and cell-type composition and present an atlas-style figure with a clear account of the method’s limits.
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.
- Spatial technologies
Load spot-based and imaging-based datasets and compare what each measures in the same tissue type.
- QC and clustering
Cluster a mouse brain section and compare expression-only with spatially aware domains.
- Spatial patterns
Rank spatially variable genes and relate them to anatomical structures.
- Deconvolution
Estimate cell-type proportions per spot from a single-cell reference and check them against known anatomy.
- Neighbourhoods and communication
Find which cell types co-localise and which signalling pairs are enriched at their interfaces.
- Project: a spatial atlas figure
Produce an annotated spatial map of a public tissue with domains, cell types and a communication analysis.
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
- 10x Genomics public Visium datasets: Spot-based analysis.10x Genomics datasets page
- Public imaging-based spatial datasets: Single-cell-resolution analysis.Vizgen / 10x public releases
Core reading
- Ståhl PL et al. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82.
- Palla G et al. (2022). Squidpy: a scalable framework for spatial omics analysis. Nature Methods 19:171–178.
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 Spatial Transcriptomics Analysis, 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 Spatial Transcriptomics Analysis
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. At least 16 GB of RAM is recommended; heavy labs run on the course server. 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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