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ASI-FIMSA Spatial Omics Workshop 2026

A two-hour, hands-on introduction to spatial omics for immunologists, run at the ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is nothing to install, and you do not need to be a Python programmer. We look at one tissue, breast cancer, through three different technologies. You will read and plot the data yourself, find the tissue niches that make up a tumour microenvironment, and finish by training a small Vision Transformer that predicts gene expression straight from an H&E image. Four Tutorials, each self-contained, each a single notebook you open with one click.

The Tutorials

Click a badge to open that Tutorial in Google Colab. Start with the setup check before you arrive.

TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min

Code and teaching material are MIT licensed — reuse, adapt and teach from them freely. The data are 10x Genomics datasets under their own terms.

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GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
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ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
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GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
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ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
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2 stars
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var __m = "*";
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GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
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ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
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GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
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ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
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Stars
2 stars
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1 watching
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1 fork
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, 'i');
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var __m = "*";
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GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
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ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
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BranchesTags
Repository files navigation
ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
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1 fork
Report repository
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, 'i');
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})();
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GitHub - GenomicsMachineLearning/ASI_FIMSA_2026 · GitHub
Skip to content
BranchesTags
Repository files navigation
ASI-FIMSA Spatial Omics Workshop 2026
A two-hour, hands-on introduction to spatial omics for immunologists, run at the
ASI-FIMSA meeting. Everything happens in your browser through Google Colab — there is
nothing to install, and you do not need to be a Python programmer. We look at one
tissue, breast cancer, through three different technologies. You will read
and plot the data yourself, find the tissue niches that make up a tumour
microenvironment, and finish by training a small Vision Transformer that predicts gene
expression straight from an H&E image. Four Tutorials, each self-contained, each a
single notebook you open with one click.
The Tutorials
Click a badge to open that Tutorial in Google Colab. Start with the setup check
before you arrive.
TutorialWhat you doRough time
Open In Colab00 · Setup checkConfirms Colab can install the packages and reach the data. (For development and bug fixing, no need to run.)Not running
Open In Colab01 · Read and visualiseLoad and plot data from each of the three platforms (Visium spots, Xenium cells, and the whole-transcriptome Atera) to see what each one can and cannot resolve.~30 min
Open In Colab02 · Niche analysisFind tissue niches in the tumour microenvironment (e.g., immune infiltration, tumour boundary, stroma), based on which cells sitting next to which.~30 min
Open In Colab03 · Cell–cell interactionRun LIANA+'s ligand–receptor test on proximal cells and watch how a gene panel may limit the questions you can ask from the data.~30 min
Open In Colab04 · ViT for gene expressionTrain a small Vision Transformer that predicts gene expression from H&E morphology alone, then look at which genes it gets right and which it does not.~30 min
Code and teaching material are MIT licensed — reuse, adapt and teach from
them freely. The data are 10x Genomics datasets under their own terms.
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