Smoother: A Unified and Modular Framework to Incorporate Structural Dependency in Spatial Omics Data

Check the notes and documentations for method details.

Installation

Smoother can be directly installed using pip

pip install git+https://github.com/JiayuSuPKU/Smoother.git#egg=smoother

However, we recommend clone the repository and use conda to manage dependencies, especially if you want to use the simulation scripts.

# download the repo from github
git clone git@github.com:JiayuSuPKU/Smoother.git

# cd into the repo and create a new conda environment called 'smoother'
conda env create --file environment.yml
conda activate smoother

# add the new conda enviroment to Jupyter
python -m ipykernel install --user --name=smoother

# install the package
pip install -e .

(Optional) To solve the deconvolution problem via convex optimization, you need to also install the 'cvxpy' package.

conda install -c conda-forge cvxpy

(Optional) To use the topological loss, you need to also install the 'TopologyLayer' package.

pip install git+https://github.com/bruel-gabrielsson/TopologyLayer.git

Smoother tutorials:

1. Smoother-guided data imputation in the DLPFC dataset
2. Smoother-guided cell-type deconvolution in the DLPFC dataset
3. Smoother-guided dimension reduction in the DLPFC dataset
4. Spatial transcriptomics data simulation

Sample usage:

# import spatial losses and models
import torch
from smoother import SpatialWeightMatrix, SpatialLoss, ContrastiveSpatialLoss
from smoother.models.deconv import NNLS
from smoother.models.reduction import PCA

# load data
x = torch.tensor(...) # n_gene x n_celltype, the reference signature matrix
y = torch.tensor(...) # n_gene x n_spot, the spatial count matrix
coords = pd.read_csv(...) # n_spot x 2, tspatial coordinates

# build spatial weight matrix
weights = SpatialWeightMatrix()
weights.calc_weights_knn(coords)

# scale weights by transcriptomics similarity
weights.scale_by_expr(y)

# transform it into spatial loss
spatial_loss = SpatialLoss('icar', weights, scale_weights=0.99)
# or contrastive loss
spatial_loss = ContrastiveSpatialLoss(
    spatial_weights=weights, num_perm=20, neg2pos_ratio=0.1)

# choose model and solve the problem
# deconvolution
model = NNLS()
model.deconv(x, y, spatial_loss=spatial_loss, lambda_spatial_loss=1, ...)

# dimension reduction
model = PCA(num_feature = y.shape[0], num_pc = 10)
model.reduce(y, ...)

References:

Su, Jiayu, et al. "A Unified Modular Framework to Incorporate Structural Dependency in Spatial Omics Data." bioRxiv (2022): 2022-10. https://www.biorxiv.org/content/10.1101/2022.10.25.513785v2