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 3.9.x and 3.10.x releases (3.11+ not yet supported).

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