Smoother: A Unified and Modular Framework to Incorporate Structural Dependency in Spatial Omics Data
Check the notes and documentations for method details.
Smoother can be directly installed using pip
pip install git+https://github.com/JiayuSuPKU/Smoother.git#egg=smootherHowever, 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-guided data imputation in the DLPFC dataset
- Smoother-guided cell-type deconvolution in the DLPFC dataset
- Smoother-guided dimension reduction in the DLPFC dataset
- Spatial transcriptomics data simulation
# 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, ...)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