This repository banchmarks methods for 3D reconstruction of spatial transcriptomics data on the small MERFISH (Moffit et al. 2018 - "Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region") and STARmap datasets (Wang et al. 2018 - "Three-dimensional intact-tissue sequencing of single-cell transcriptional states").
It focuses on reconstructing continuous spatial structures from aligned slices and comparing different computational frameworks for this task (SpatialZ, UOT-based, SpatialZ + PCA/scVI, etc.).
data/– input datasetsexploration/– exploratory notebooks and experimentsinference/– inference outputs and figuresoutput/– saved results and reconstructionsuot/– unbalanced optimal transport (UOT) implementationutils/– helper functions
SpatialZ.py– main SpatialZ method
my_method_pca.py– reconstruction using SpatialZ (for cell locations + cell type) and PCA (for gene expression)my_method_scvi.py– reconstruction using SpatialZ (for cell locations + cell type) and scVI (for gene expression)my_method_pretrained_scvi.py– reconstruction using SpatialZ (for cell locations + cell type) and pre-trained scVI (for gene expression)
compare_merfish_small.py– comparison on MERFISH datasetcompare_starmap.py– comparison on STARmap datasetcompare_location_models.ipynb– notebook comparing other spatial models (KDE vs. OT)
evaluation_merfish_small.py– evaluation on MERFISHevaluation_starmap.py– evaluation on STARmap
cut_dataset.ipynb– dataset preprocessing and slice splittingsplit_train_test*.ipynb– train/test splittingoptimization_vs_kde.ipynb– experiments comparing other spatial models (KDE vs. OT)
requirements_compare.txt– dependencies for comparisonsrequirements_scvi.txt– dependencies for scVI modelsrequirements_spatialz.txt– dependencies for SpatialZ