SDePER (Spatial Deconvolution method with Platform Effect Removal) is a two-step hybrid machine learning and regression method considering platform effect, spatial information and sparsity in deconvolution of spatial transcriptomics data using reference single-cell RNA sequencing data from same tissue type. It's also able to impute cell type compositions and gene expression at enhanced resolution.
SDePER can be installed via pip
conda create -n sdeper-env python=3.9.12
conda activate sdeper-env
pip install sdeper
SDePER requires 4 input files for cell type deconvolution:
- raw nUMI counts of spatial transcriptomics data (spots × genes):
spatial.csv
- raw nUMI counts of reference scRNA-seq data (cells × genes):
scrna_ref.csv
- cell type annotations for all cells in scRNA-seq data (cells × 1):
scrna_anno.csv
- adjacency matrix of spots in spatial transcriptomics data (spots × spots):
adjacency.csv
To start cell type deconvolution by running
runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv
Full Documentation for SDePER is available on Read the Docs.
Example Analysis with SDePER are available in our GitHub repository SDePER_Analysis.