SPACEL: characterizing spatial transcriptome architectures by deep-learning
Project description
SPACEL: characterizing spatial transcriptome architectures by deep-learning
SPACEL (SPatial Architecture Characterization by dEep Learning) is a Python package of deep-learning-based methods for ST data analysis. SPACEL consists of three modules:
- Spoint embedded a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot on single ST slice.
- Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify uniform spatial domains that are transcriptomically and spatially coherent across multiple ST slices.
- Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a three-dimensional (3D) alignment of the tissue.
Getting started
- Requirements
- Installation
- Tutorials
- Spoint tutorial: Deconvolution of cell types compostion on human brain Visium dataset
- Splane tutorial: Identify uniform spatial domain on human breast cancer Visium dataset
- Splane&Scube tutorial (1/2): Identify uniform spatial domain on human brain MERFISH dataset
- Splane&Scube tutorial (1/2): Alignment of consecutive ST slices on human brain MERFISH dataset
- Scube tutorial: Alignment of consecutive ST slices on mouse embryo Stereo-seq dataset
- Scube tutorial: 3D expression modeling with gaussian process regression
- SPACEL workflow (1/3): Deconvolution by Spoint on mouse brain ST dataset
- SPACEL workflow (2/3): Identification of spatial domain by Splane on mouse brain ST dataset
- SPACEL workflow (3/3): Alignment 3D tissue by Scube on mouse brain ST dataset
Read the documentation for more information.
Latest updates
Version 1.1.8 2024-07-23
Fixed Bugs
- Fixed the conflict between optax version and phthon 3.8.
Version 1.1.7 2024-01-16
Fixed Bugs
- Fixed a variable reference error in function
identify_spatial_domain
. Thanks to @tobias-zehnde for the contribution.
Version 1.1.6 2023-07-27
Fixed Bugs
- Fixed a bug regarding the similarity loss weight hyperparameter
simi_l
, which in the previous version did not affect the loss value.
Requirements
Note: The current version of SPACEL only supports Linux and MacOS, not Windows platform.
To install SPACEL
, you need to install PyTorch with GPU support first. If you don't need GPU acceleration, you can just skip the installation for cudnn
and cudatoolkit
.
- Create conda environment for
SPACEL
:
conda env create -f environment.yml
or
conda create -n SPACEL -c conda-forge -c default cudatoolkit=10.2 python=3.8 rpy2 r-base r-fitdistrplus
You must choose correct PyTorch
, cudnn
and cudatoolkit
version dependent on your graphic driver version.
Note: If you want to run 3D expression GPR model in Scube, you need to install the Open3D python library first.
Installation
- Install
SPACEL
:
pip install SPACEL
- Test if PyTorch for GPU available:
python
>>> import torch
>>> torch.cuda.is_available()
If these command line have not return True
, please check your gpu driver version and cudatoolkit
version. For more detail, look at CUDA Toolkit Major Component Versions.
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