Predicting whole-transcriptome expression of spatial transcriptomics data through integration with scRNA-seq data
Python implementation can be found in the 'SpaGE' folder. The SpaGE function takes as input i) two single cell datasets, spatial transcriptomics and scRNA-seq, ii) the number of principal vectors (PVs), and iii) the set of unmeasured genes in the spatial data for which predictions are obtained from the scRNA-seq (optional). The function returns back the predicted expression for these unmeasured genes across all spatial cells.
For full description, please check the SpaGE function description in main.py.
The SpaGE_Tutorial notebook is a step-by-step example showing how to validate SpaGE on the spatially measured genes, and how to use SpaGE to predict new spatial gene patterns.
The 'benchmark' folder contains the scripts to reproduce the results shown in the pre-print. The bencmark folder has five subfolders, each corresponds to one dataset-pair and contains the scripts to run SpaGE, Seurat-v3, Liger and gimVI. Additionally, we provide evaluation scripts to calculate and compare the performance of all four methods, and to reproduce all the figures in the paper.
All datasets used are publicly available data, for convenience datasets can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.3967291)
For citation and further information please refer to: "SpaGE: Spatial Gene Enhancement using scRNA-seq", NAR