STALocator is a method that spatially localizes scRNA-seq data through integration with ST data. STALocator is a deep learning-based tool consisting of an integration network that integrates scRNA-seq data with ST data and a localization network that predicts spatial location for scRNA-seq data. Among them, the integration network adopted the modified domain translation networks equipped with sliced Wasserstein distance to robustly align scRNA-seq and ST data. The localization network adopted the spatial location-supervised auto-encoder equipped with well-designed loss function to robustly fit low-dimensional representations and spatial locations for ST data. We designed different pipelines for ST data with different resolutions, which can be applied on different biological scenarios.
For low-resolution ST data, such as Spatial Transcriptomics and 10x Visium, we first train an integration network to obtain a low-dimensional representation that removes batch effects, and then train a localization network to predict the spatial location of cells. Single-cell data with spatial location information can be can be considered as a higher-resolution form of spatial transcriptome data compared to raw ST data. For high-resolution ST data, such as Slide-seq and FISH, it already has a resolution close to that of scRNA-seq data, so we only train the integration network, and update the optimal transport (OT) plan during the training process, and finally we can obtain a global OT plan from scRNA-seq data to ST data which can be used to enhance ST data. For Slide-seq data, exhibits more distinct and discernible patterns than raw data. Furthermore, it is noteworthy that FISH data presents a limitation in terms of the number of measurable genes. In light of this constraint, the application of our approach enables the acquisition of genome-wide ST data, addressing this limitation effectively.
It is recommended to use a Python version between 3.7 and 3.9, and a R version above 4.2.
The important Python packages used to run the model are as follows:
scanpy>=1.8.2,<=1.9.6
torch>=1.8.0,<=1.13.0
torchvision>=0.9.0,<=1.14.0
POT==0.9.0
In addition, if you choose to use GPU, the versions of torch and torchvision need to be compatible with the version of CUDA.
The important R packages used to process data and perform enrichment analysis are as follows:
Seurat==4.2
SeuratData==0.2
GSEABase==1.60
DOSE==3.24
fgsea==1.24
clusterProfiler==4.6
org.Mm.eg.db==3.16
AnnotationDbi==1.60
After download STALocator from Github, you can install STALocator via
cd STALocator-main
python setup.py build
python setup.py install
In addtion, if you choose to install STALocator in a virtual environment, you should install the imageio and igraph packages first.
And if you choose to use R in jupyter notebook, you should install the R kernel in jupyter notebook first.
The following are detailed tutorials. Some related additional files can be downloaded here.