This tutorial will teach you how to use the vitessce python package to construct Vitessce configurations for local datasets.
The example notebooks in examples/ demonstrate the types of visualizations that are possible to create with Vitessce. These contain minimal examples of each data type, and they point to remote data so that they can be executed in cloud notebook environments such as Google Colab.
The tutorial notebooks in tutorials/ demonstrate required data processing and conversion steps, visualization configuration, and data/configuration exporting.
The template notebooks in templates/ contain fill-in-the-blank comments for adapting them to your own data. They contain fewer explanations and descriptions than the tutorial notebooks, and the three steps of data processing, visualization configuration, and exporting are merged rather than split across different notebooks.
Prerequisites:
- conda installation
- familiarity with Python code and Jupyter notebooks
- familiarity with using the command line (e.g., installing command line tools, downloading files)
Set up the Python environment using conda:
conda env create -f environment.ymlTo convert image data into OME-TIFF format, you will want to install bftools by unzipping it. My installation is located at ~/software/bftools.
Activate the environment:
conda activate vitessce-tutorial-envLaunch JupyterLab in the sub-directory of interest:
jupyter lab --notebook-dir=./tutorials/transcriptomics
# or
jupyter lab --notebook-dir=./tutorials/imaging
# or
jupyter lab --notebook-dir=./tutorials/spatial_single_cell
# or
jupyter lab --notebook-dir=./templatesTo download the raw data for the tutorials, run the following notebooks:
./tutorials/transcriptomics/raw_data/download.ipynb./tutorials/spatial_single_cell/raw_data/download.ipynb
Raw data:
- Transcriptomics data from https://www.covid19cellatlas.org/index.healthy.html#habib17
- Visium data from https://scanpy.readthedocs.io/en/stable/generated/scanpy.datasets.visium_sge.html#scanpy.datasets.visium_sge