SpatialPrompt : Spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics
Article url: https://doi.org/10.1038/s42003-024-06349-5
SpatialPromptDB database url: https://swainasish.github.io/SpatialPrompt/
SpatialPrompt integrates gene expression, spatial location, and reference single cell RNA sequencing data for spatial deconvolution.
For spatial clustering, tool requires only gene expression with spatial locations.
Platforms: Ubuntu: 22.04/20.04, Windows: 10/11, Mac-OS: Ventura - Python: 3.10 Preferable
pip install spatialprompt
For spot deconvolutiom SpatialPrompt requires:
sc_array: Matrix of Single-cell data, where rows are the cells and columns are the genes.st_array: Matrix of Spatial data, where rows are the cells and columns are the genes.sc_genes: Gene names of thesc_arraymatrix.st_genes: Gene names of thest_arraymatrix.sc_labels: Cell type annotations ofsc_array.x_cord: X coordinate array of spatial data.y_cord: Y coordinate array of spatial data.
import scanpy as sc
import spatialprompt as sp
import gdown
gdown.download("https://drive.google.com/uc?id=1n3ACaWjfjXJ8P6IJhxSUax_vBagIUPLV","sc_m_cortex.h5ad", quiet=False)
gdown.download("https://drive.google.com/uc?id=1h7E5nPs2ga1ixOBDjLJKK7V8xJq93ez5","st_m_cortex.h5ad", quiet=False)
sc_data = sc.read_h5ad("sc_m_cortex.h5ad")
st_data = sc.read_h5ad("st_m_cortex.h5ad")
#Requires raw data
sc_array = sc_data.X
sc_genes = sc_data.var_names
sc_labels = sc_data.obs.loc[:,"label"]
st_array = st_data.X.toarray()
st_genes = st_data.var_names
x_cord = st_data.obs.array_row
y_cord = st_data.obs.array_col
deconv_model = sp.SpatialDeconvolution()
cortex_predict = deconv_model.predict_cell_prop(sc_array,st_array,
sc_genes,st_genes,
sc_labels,x_cord,y_cord )
for i in cortex_predict.columns:
st_data.obs.loc[:,i] = cortex_predict.loc[:,i].values
sc.pl.spatial(st_data,
color=("L2/3 IT", 'L4', 'L5 IT', 'L5 PT', 'L6 CT', 'L6b',"Oligo"),
library_id='V1_Mouse_Brain_Sagittal_Anterior')
For domain identification SpatialPrompt requires:
st_array: Matrix of Spatial data, where rows are the cells and columns are the genes.x_cord: X coordinate array of spatial data.y_cord: Y coordinate array of spatial data.
import scanpy as sc
import spatialprompt as sp
import gdown
gdown.download("https://drive.google.com/uc?id=1h7E5nPs2ga1ixOBDjLJKK7V8xJq93ez5","st_m_cortex.h5ad", quiet=False)
st_data = sc.read_h5ad("st_m_cortex.h5ad")
#Requires raw data
st_array = st_data.X.toarray()
x_cord = st_data.obs.array_row
y_cord = st_data.obs.array_col
clus_model = sp.SpatialCluster()
cortex_domains = clus_model.fit_predict(st_array = st_array,
x_cord = x_cord,
y_cord = y_cord,n_cluster=20)
st_data.obs.loc[:,"SpatialPrompt: clusters"] = cortex_domains
sc.pl.spatial(st_data,color=("SpatialPrompt: clusters"),
library_id='V1_Mouse_Brain_Sagittal_Anterior',
palette="tab20")
Cite the article :
@article{swain2024spatialprompt,
title={SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics},
author={Swain, Asish Kumar and Pandit, Vrushali and Sharma, Jyoti and Yadav, Pankaj},
journal={Communications Biology},
volume={7},
number={1},
pages={639},
year={2024},
publisher={Nature Publishing Group UK London}
}