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TRANSCRIPTOMICS 30 days · 6 modules · Online

Spatial Transcriptomics Data Analysis Workshop (30-Day, Online)

Map gene activity across tissue sections: spot QC, spatial clustering, spatially variable genes and cell deconvolution.

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About this workshop

Conventional expression data tells you what a tissue is making but not where. Spatial transcriptomics keeps the location of each measurement, so you can see how gene activity is organised across a tissue section and how regions relate to the underlying structure. This workshop covers the data from the first step: understanding spots, coordinates and tissue images, filtering low-quality or off-tissue spots, and normalising for depth. You then group spots into regions, find genes whose expression follows spatial patterns, and describe the cell mixtures within each spot using single-cell references. The result is a set of maps and gene lists that link molecular signals to tissue architecture. It is useful for anyone working with tissue sections in development, cancer or neuroscience.

Tools and pipelines you will use

This workshop emphasizes standard, open-source bioinformatics tools widely used in peer-reviewed genomics literature:

What you will learn

Module 1 Foundations
Week 1
Spatial technologies Compare slide-based and imaging-based approaches.
Data structure Spots, coordinates, images and count matrices.
Environment setup Run R or Python spatial tools.
Raw data processing Align reads to spots and tissue images.
Module 2 Quality control
Spot filtering Remove low-quality or off-tissue spots.
Gene filtering Keep informative genes.
Tissue image checks Match expression to histology.
QC summaries Report quality per section.
Module 3 Normalization and clustering
Normalization for spatial data Handle depth and tissue differences.
Dimensionality reduction Reduce the data to its main patterns.
Clustering spots Find regions with similar expression.
Mapping clusters to tissue Draw clusters over the image.
Module 4 Spatial patterns
Spatially variable genes Find genes with location-dependent expression.
Region markers Define genes that characterise each area.
Neighbourhood analysis Study which regions sit next to each other.
Pattern visualization Plot expression over tissue.
Module 5 Integration and interpretation
Cell-type deconvolution Estimate cell mixtures within each spot.
Using single-cell references Combine spatial and single-cell data.
Comparing sections or conditions Look for differences between samples.
Biological interpretation Link regions to function.
Module 6 Mini-project and report
Week 4
Project dataset Analyse a public spatial dataset.
Reproducible workflow Record every step and setting.
Figures and write-up Present spatial maps and findings.
Review and next steps Discuss validation and follow-up.

What you receive & prerequisites

What you receive

  • 30 days of structured online self-paced access & mentor guidance
  • Curated real-world NGS benchmark datasets
  • Reusable pipeline scripts and command-line code templates
  • Independent capstone mini-project evaluation
  • Certificate of Completion issued by Xenoi Biotech Pvt. Ltd.

Prerequisites & setup

  • Basic life science background (B.Sc., M.Sc., B.Tech, or research scholar)
  • No prior programming or Linux experience required (covered in Module 1)
  • Laptop or PC with 8 GB RAM and internet access
  • Remote compute setup instructions provided during onboarding

Course curriculum designed by Anjali Chaudhary, Bioinformatics Scientist at Xenoi Biotech.

Last updated: October 2026

Frequently asked questions

Is this Spatial Transcriptomics workshop delivered online?

Yes. All Xenoi Biotech workshops are delivered 100% online, allowing researchers and students across India and internationally to participate with remote compute support.

How long is the workshop and what is the workload?

The workshop runs for 30 days across 6 structured modules. Learners typically dedicate 1 to 2 flexible hours per day to work through pipeline commands, code exercises, and the mini-project.

Who is this workshop designed for?

It is built for B.Sc., M.Sc., and Ph.D. students, postdoctoral researchers, faculty members, and industry professionals in biotechnology, biochemistry, microbiology, and medicine who need to analyze NGS datasets independently.

What will I learn in this workshop?

You will master Space Ranger output interpretation, tissue image alignment, spot-level QC, spatially variable gene identification with Moran's I and SpatialDE, spot clustering, and cellular deconvolution using single-cell reference datasets.

What are the fees, dates, and enrollment details?

The workshop fee is MRP ₹5,000, inclusive of GST. Batch dates are shared on enquiry. Use the Enrol now button to reserve your place or receive the upcoming schedule.

Fees, dates and batches

The workshop fee is MRP ₹5,000 (incl. GST). Batch dates and onboarding materials are shared upon enquiry.

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