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.

Content

• 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

• API

Latest updates

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.

Version 1.1.5 2023-07-26

Fixed Bugs

• Fixed a bug in the similarity loss of Splane, where it minimized the cosine similarity of the latent vectors of spots with their neighbors.

Features

• Optimized the time and memory consumption of the Splane training process for large datasets.

Version 1.1.2 2023-07-12

Fixed Bugs

• Removed rpy2 from the pypi dependency of SPACEL. It now needs to be pre-installed when creating the environment through conda.

• Fixed a bug in Scube where the best_model_state was not referenced before being used.

Features

• Added function documentations for Scube related to the GPR model.