SDePER

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SDePER (Spatial Deconvolution method with Platform Effect Removal) is a hybrid machine learning and regression method to deconvolve Spatial barcoding-based transcriptomic data using reference single-cell RNA sequencing data, considering platform effects removal, sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. SDePER is also able to impute cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution.

Quick Start

SDePER currently supports only Linux operating systems such as Ubuntu, and is compatible with Python versions 3.9 up to 3.10.

SDePER can be installed via conda

conda create -n sdeper-env -c bioconda -c conda-forge python=3.9.12 sdeper

or pip

conda create -n sdeper-env python=3.9.12
conda activate sdeper-env
pip install sdeper

SDePER supports an out-of-the-box feature, meaning that users only need to provide the required four input files for cell type deconvolution. The package manages all aspects of file reading, preprocessing, cell type-specific marker gene identification, and more internally. The required files are:

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; optional): adjacency.csv

To start cell type deconvolution using all default settings by running

runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv

Homepage: https://az7jh2.github.io/SDePER/.

Full Documentation for SDePER is available here.

Example data and Analysis using SDePER are summarized in this page.

Citation

If you use SDePER, please cite:

Yunqing Liu, Ningshan Li, Ji Qi et al. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 25, 271 (2024). https://doi.org/10.1186/s13059-024-03416-2