nest-st 0.1.0

NEST: Native Expression reconstruction for Spatial Transcriptomics

NEST

NEST, Native Expression reconstruction for Spatial Transcriptomics, reconstructs biologically faithful spatial transcriptomes under native spatial constraints.

It supports two tasks with one parameter:

• mode="missing_gene": imputes unmeasured genes in imaging-based spatial transcriptomics using a matched scRNA-seq reference.
• mode="dropout": recovers dropout-corrupted false zeros in sequencing-based spatial transcriptomics using ST data only.

Installation for development

git clone https://github.com/yourname/nest-st.git
cd nest-st
pip install -e .

For CUDA environments, install PyTorch and PyTorch Geometric using the wheel index recommended for your CUDA version before installing the package.

Python usage

Missing-gene imputation

import scanpy as sc
from nest_st import NEST, NESTConfig

adata_st = sc.read_h5ad("data/st.h5ad")
adata_sc = sc.read_h5ad("data/sc.h5ad")

sc.pp.log1p(adata_st)
sc.pp.log1p(adata_sc)

config = NESTConfig(mode="missing_gene", ae_epochs=1000, gae_epochs=2000, device="cuda")
adata_imputed = NEST(config).fit_transform(adata_st=adata_st, adata_sc=adata_sc)
adata_imputed.write_h5ad("nest_missing_gene_result.h5ad")

Dropout recovery

import scanpy as sc
from nest_st import NEST, NESTConfig

adata_st = sc.read_h5ad("data/st.h5ad")
sc.pp.log1p(adata_st)

config = NESTConfig(mode="dropout", fill_zeros_only=True, ae_epochs=1000, gae_epochs=2000, device="cuda")
adata_recovered = NEST(config).fit_transform(adata_st=adata_st)
adata_recovered.write_h5ad("nest_dropout_result.h5ad")

Command-line usage

python main.py --mode missing_gene --st data/st.h5ad --sc data/sc.h5ad --output result_gene.h5ad --log1p
python main.py --mode dropout --st data/st.h5ad --output result_dropout.h5ad --log1p --fill-zeros-only

Input requirements

• Input files should be AnnData .h5ad files.
• adata_st.obsm["spatial"] must contain spatial coordinates.
• For missing_gene mode, adata_sc must contain genes to be transferred into the ST domain.
• For dropout mode, no scRNA-seq reference is required.

Upload to PyPI

python -m pip install --upgrade build twine
python -m build
python -m twine upload dist/*

For TestPyPI:

python -m twine upload --repository testpypi dist/*

Update the package name, author information and GitHub URLs in pyproject.toml before publishing.