Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Spatial Omics Summer School

This repository contains course material for the Spatial Omics Summer School. The notebooks guide participants through working with SpatialData objects, targeted transcriptomics data and spatial proteomics data.

The tutorials use Harpy for spatial omics analysis workflows and napari-harpy for interactive inspection and visualization in napari.

Installation

This project uses uv to manage the Python environment.

Dependencies are defined in pyproject.toml and locked in uv.lock. Use the lockfile for the course environment so everyone gets the same package versions.

1. Get the repository

Clone this repository and move into the project directory:

git clone https://github.com/vibspatial/targeted_transcriptomics_training.git
cd targeted_transcriptomics_training

If you already cloned the repository, update it from inside the project directory:

git pull

2. Install uv

Make sure uv is installed and available on your PATH.

Check with:

uv --version

3. Create or sync the environment

From the repository root, create or sync the Python 3.12 course environment by running:

uv sync --python 3.12 --locked

This creates or updates the project environment at:

.venv

This means:

.venv is created if it does not exist
packages are installed from uv.lock
uv.lock is not modified
the command fails if pyproject.toml and uv.lock are out of sync

4. Activate the environment

On macOS, Linux, or WSL:

source .venv/bin/activate

On Windows PowerShell:

Set-ExecutionPolicy-Scope Process-ExecutionPolicy Bypass
. .\.venv\Scripts\Activate.ps1

On Windows Command Prompt:

.venv\Scripts\activate.bat

On Windows Git Bash:

source .venv/Scripts/activate

5. Download the course data

After activating the environment, download the datasets and model weights used in the notebooks.

On macOS, Linux, or WSL, run:

python download_data.py

On Windows, always set the cache directory explicitly to C:/hp_cache:

python download_data.py --cache-dir-path C:/hp_cache

Use a short cache path on Windows to avoid path-length errors when downloading and unpacking nested dataset files. Some Windows setups still enforce the traditional 260-character path limit unless long-path support is enabled.

This downloads the course data into the selected cache directory. It also downloads the InstanSeg model and the Cellpose cyto3 and nuclei models.

6. Use the environment in VS Code

Open this repository folder in VS Code.

Open the Command Palette and run Python: Select Interpreter. Select the interpreter from .venv.

On macOS, Linux, or WSL, choose:

.venv/bin/python

On Windows, choose:

.venv\Scripts\python.exe

When opening a notebook, click the kernel selector in the top-right corner and choose the same .venv environment. It will be named targeted-transcriptomics-training.

Vitessce notebooks

The Vitessce hands-on notebooks use a separate environment because current harpy-vitessce releases require a newer SpatialData stack than the main course notebooks.

See vitessce/README.md for the Vitessce-specific setup.

Updating dependencies

For normal course use, do not edit uv.lock.

If dependencies in pyproject.toml are changed intentionally, update the lockfile with:

uv lock

Then recreate or sync the environment with:

uv sync --python 3.12 --locked

Name		Name	Last commit message	Last commit date
.claude		.claude
anndata_to_r		anndata_to_r
annotations		annotations
globus		globus
ilastik		ilastik
img		img
presentations		presentations
vitessce		vitessce
.gitignore		.gitignore
1.Xenium_Introduction.ipynb		1.Xenium_Introduction.ipynb
2.Xenium_registration_annotation.ipynb		2.Xenium_registration_annotation.ipynb
3.Xenium_initial_analysis.ipynb		3.Xenium_initial_analysis.ipynb
4.Xenium_downstream_analysis.ipynb		4.Xenium_downstream_analysis.ipynb
5.proteomics_macsima.ipynb		5.proteomics_macsima.ipynb
6.proteomics_macsima_object_classification.ipynb		6.proteomics_macsima_object_classification.ipynb
7.proteomics_macsima_segmentation_free.ipynb		7.proteomics_macsima_segmentation_free.ipynb
8.proteomics_segmentation.ipynb		8.proteomics_segmentation.ipynb
CLAUDE.md		CLAUDE.md
README.md		README.md
Screenshot.png		Screenshot.png
XE_annotations.geojson		XE_annotations.geojson
agents.md		agents.md
create_env.ps1		create_env.ps1
create_env.sh		create_env.sh
create_env_vitessce.sh		create_env_vitessce.sh
download_data.py		download_data.py
extra_repair_broken_spatialdata.ipynb		extra_repair_broken_spatialdata.ipynb
ilastik.ipynb		ilastik.ipynb
pyproject.toml		pyproject.toml
read_multichannel_qupath.groovy		read_multichannel_qupath.groovy
spatial_multiome_integration.ipynb		spatial_multiome_integration.ipynb
summer_school_datasets.py		summer_school_datasets.py
uv.lock		uv.lock
vsc-connection.md		vsc-connection.md

Name		Name	Last commit message	Last commit date
.claude		.claude
anndata_to_r		anndata_to_r
annotations		annotations
globus		globus
ilastik		ilastik
img		img
presentations		presentations
vitessce		vitessce
.gitignore		.gitignore
1.Xenium_Introduction.ipynb		1.Xenium_Introduction.ipynb
2.Xenium_registration_annotation.ipynb		2.Xenium_registration_annotation.ipynb
3.Xenium_initial_analysis.ipynb		3.Xenium_initial_analysis.ipynb
4.Xenium_downstream_analysis.ipynb		4.Xenium_downstream_analysis.ipynb
5.proteomics_macsima.ipynb		5.proteomics_macsima.ipynb
6.proteomics_macsima_object_classification.ipynb		6.proteomics_macsima_object_classification.ipynb
7.proteomics_macsima_segmentation_free.ipynb		7.proteomics_macsima_segmentation_free.ipynb
8.proteomics_segmentation.ipynb		8.proteomics_segmentation.ipynb
CLAUDE.md		CLAUDE.md
README.md		README.md
Screenshot.png		Screenshot.png
XE_annotations.geojson		XE_annotations.geojson
agents.md		agents.md
create_env.ps1		create_env.ps1
create_env.sh		create_env.sh
create_env_vitessce.sh		create_env_vitessce.sh
download_data.py		download_data.py
extra_repair_broken_spatialdata.ipynb		extra_repair_broken_spatialdata.ipynb
ilastik.ipynb		ilastik.ipynb
pyproject.toml		pyproject.toml
read_multichannel_qupath.groovy		read_multichannel_qupath.groovy
spatial_multiome_integration.ipynb		spatial_multiome_integration.ipynb
summer_school_datasets.py		summer_school_datasets.py
uv.lock		uv.lock
vsc-connection.md		vsc-connection.md

Name		Name	Last commit message	Last commit date
.claude		.claude
anndata_to_r		anndata_to_r
annotations		annotations
globus		globus
ilastik		ilastik
img		img
presentations		presentations
vitessce		vitessce
.gitignore		.gitignore
1.Xenium_Introduction.ipynb		1.Xenium_Introduction.ipynb
2.Xenium_registration_annotation.ipynb		2.Xenium_registration_annotation.ipynb
3.Xenium_initial_analysis.ipynb		3.Xenium_initial_analysis.ipynb
4.Xenium_downstream_analysis.ipynb		4.Xenium_downstream_analysis.ipynb
5.proteomics_macsima.ipynb		5.proteomics_macsima.ipynb
6.proteomics_macsima_object_classification.ipynb		6.proteomics_macsima_object_classification.ipynb
7.proteomics_macsima_segmentation_free.ipynb		7.proteomics_macsima_segmentation_free.ipynb
8.proteomics_segmentation.ipynb		8.proteomics_segmentation.ipynb
CLAUDE.md		CLAUDE.md
README.md		README.md
Screenshot.png		Screenshot.png
XE_annotations.geojson		XE_annotations.geojson
agents.md		agents.md
create_env.ps1		create_env.ps1
create_env.sh		create_env.sh
create_env_vitessce.sh		create_env_vitessce.sh
download_data.py		download_data.py
extra_repair_broken_spatialdata.ipynb		extra_repair_broken_spatialdata.ipynb
ilastik.ipynb		ilastik.ipynb
pyproject.toml		pyproject.toml
read_multichannel_qupath.groovy		read_multichannel_qupath.groovy
spatial_multiome_integration.ipynb		spatial_multiome_integration.ipynb
summer_school_datasets.py		summer_school_datasets.py
uv.lock		uv.lock
vsc-connection.md		vsc-connection.md

Name		Name	Last commit message	Last commit date
.claude		.claude
anndata_to_r		anndata_to_r
annotations		annotations
globus		globus
ilastik		ilastik
img		img
presentations		presentations
vitessce		vitessce
.gitignore		.gitignore
1.Xenium_Introduction.ipynb		1.Xenium_Introduction.ipynb
2.Xenium_registration_annotation.ipynb		2.Xenium_registration_annotation.ipynb
3.Xenium_initial_analysis.ipynb		3.Xenium_initial_analysis.ipynb
4.Xenium_downstream_analysis.ipynb		4.Xenium_downstream_analysis.ipynb
5.proteomics_macsima.ipynb		5.proteomics_macsima.ipynb
6.proteomics_macsima_object_classification.ipynb		6.proteomics_macsima_object_classification.ipynb
7.proteomics_macsima_segmentation_free.ipynb		7.proteomics_macsima_segmentation_free.ipynb
8.proteomics_segmentation.ipynb		8.proteomics_segmentation.ipynb
CLAUDE.md		CLAUDE.md
README.md		README.md
Screenshot.png		Screenshot.png
XE_annotations.geojson		XE_annotations.geojson
agents.md		agents.md
create_env.ps1		create_env.ps1
create_env.sh		create_env.sh
create_env_vitessce.sh		create_env_vitessce.sh
download_data.py		download_data.py
extra_repair_broken_spatialdata.ipynb		extra_repair_broken_spatialdata.ipynb
ilastik.ipynb		ilastik.ipynb
pyproject.toml		pyproject.toml
read_multichannel_qupath.groovy		read_multichannel_qupath.groovy
spatial_multiome_integration.ipynb		spatial_multiome_integration.ipynb
summer_school_datasets.py		summer_school_datasets.py
uv.lock		uv.lock
vsc-connection.md		vsc-connection.md

Name		Name	Last commit message	Last commit date
.claude		.claude
anndata_to_r		anndata_to_r
annotations		annotations
globus		globus
ilastik		ilastik
img		img
presentations		presentations
vitessce		vitessce
.gitignore		.gitignore
1.Xenium_Introduction.ipynb		1.Xenium_Introduction.ipynb
2.Xenium_registration_annotation.ipynb		2.Xenium_registration_annotation.ipynb
3.Xenium_initial_analysis.ipynb		3.Xenium_initial_analysis.ipynb
4.Xenium_downstream_analysis.ipynb		4.Xenium_downstream_analysis.ipynb
5.proteomics_macsima.ipynb		5.proteomics_macsima.ipynb
6.proteomics_macsima_object_classification.ipynb		6.proteomics_macsima_object_classification.ipynb
7.proteomics_macsima_segmentation_free.ipynb		7.proteomics_macsima_segmentation_free.ipynb
8.proteomics_segmentation.ipynb		8.proteomics_segmentation.ipynb
CLAUDE.md		CLAUDE.md
README.md		README.md
Screenshot.png		Screenshot.png
XE_annotations.geojson		XE_annotations.geojson
agents.md		agents.md
create_env.ps1		create_env.ps1
create_env.sh		create_env.sh
create_env_vitessce.sh		create_env_vitessce.sh
download_data.py		download_data.py
extra_repair_broken_spatialdata.ipynb		extra_repair_broken_spatialdata.ipynb
ilastik.ipynb		ilastik.ipynb
pyproject.toml		pyproject.toml
read_multichannel_qupath.groovy		read_multichannel_qupath.groovy
spatial_multiome_integration.ipynb		spatial_multiome_integration.ipynb
summer_school_datasets.py		summer_school_datasets.py
uv.lock		uv.lock
vsc-connection.md		vsc-connection.md

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Spatial Omics Summer School

Installation

1. Get the repository

2. Install uv

3. Create or sync the environment

4. Activate the environment

5. Download the course data

6. Use the environment in VS Code

Vitessce notebooks

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Spatial Omics Summer School

Installation

1. Get the repository

2. Install uv

3. Create or sync the environment

4. Activate the environment

5. Download the course data

6. Use the environment in VS Code

Vitessce notebooks

Updating dependencies

About

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Contributors

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Latest commit

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Spatial Omics Summer School

Installation

1. Get the repository

2. Install uv

3. Create or sync the environment

4. Activate the environment

5. Download the course data

6. Use the environment in VS Code

Vitessce notebooks

Updating dependencies

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Uh oh!

Latest commit

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Spatial Omics Summer School

Installation

1. Get the repository