Spatial Deconvolution method with Platform Effect Removal
Project description
SDePER
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 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 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
Homepage: https://az7jh2.github.io/SDePER/.
Full Documentation for SDePER is available on Read the Docs.
Example data and Analysis using SDePER are available in our GitHub repository SDePER_Analysis.
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