SDePER

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 transcriptomic 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.

Quick Start

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:

1. raw nUMI counts of spatial transcriptomic data (spots × genes): spatial.csv
2. raw nUMI counts of reference scRNA-seq data (cells × genes): scrna_ref.csv
3. cell type annotations for all cells in scRNA-seq data (cells × 1): scrna_anno.csv
4. adjacency matrix of spots in spatial transcriptomic 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.