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CAST is a Python library for physically aligning different spatial transcriptome regardless of technologies, magnification, individual variation, and experimental batch effects.
CAST is composed of three modules: CAST Mark, CAST Stack, and CAST Projection.
Installation Guide
For Windows system, we suggest to use the Anaconda powershell to run following code.
Requirements
[Optional] It is recommended to create a conda environment for CAST.
For example, create a new environment named cast_demo and activate it.
We provide with several demos to demonstrate the functions in CAST package.
Due to file size restrictions at GitHub, we cannot upload sample datasets associated with the demos. Please kindly find the data at our zenodo archive for the paper: https://zenodo.org/doi/10.5281/zenodo.12215314. This Zenodo repo also contains code and data to reproduce results in the paper.
Users can use following code to open the Jupyter notebook (We recommend to use Chrome to open the jupyter notebook).
cd $demo_path
jupyter notebook
#### If remote kernel ####
jupyter notebook --ip=0.0.0.0 --port=8800
#### If dead kernel ####
jupyter notebook --NotebookApp.max_buffer_size=21474836480
Demo 1 CAST_Mark
In this demo, CAST_Mark can captures the common spatial features across multiple samples
Users should first replace $demo_path with the CAST demo Path in the first cell.
Demo 2 CAST_Stack_Align_S4_to_S1
In this demo, Stack_Align can align two samples together.
Users should first replace $demo_path with the CAST demo Path in the first cell.
Demo 3 CAST_project
In this demo, CAST_projectiong will project one sample to another one.
Users should first replace $demo_path with the CAST demo Path in the first cell.