Usage

Note

For tutorials on using SDePER, please refer to the simulated and real data analysis examples in our GitHub repository SDePER_Analysis.

Cell type deconvolution

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 with default settings, run:

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

docker run -it --rm -v <path>:/data az7jh2/sdeper:2.0.0 runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv

singularity exec -B <path>:/data sdeper-2.0.0.sif runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv

<path> is the valid and absolute path of the folder in the host machine where all input files locate, and this fold will be mounted in the Docker/Singularity image for data exchanging.

Important

Please check out the deconvolution input-related options page for file format requirements of each input file, and deconvolution output-related options for descriptions of output files containing the result of cell type deconvotion.

Examples of input and output files can also be found in our GitHub repository SDePER_Analysis.

Imputation

SDePER requires 3 input files for imputation:

raw nUMI counts of spatial transcriptomics data (spots × genes): spatial.csv

row/column integer index (x, y) of spots in spatial transcriptomics data (spots × 2): spatial_loc.csv

cell type proportions of spots in spatial transcriptomics data (spots × cell types): spatial_prop.csv

To start imputation with default settings, run:

runImputation -q spatial.csv -l spatial_loc.csv -p spatial_prop.csv

docker run -it --rm -v <path>:/data az7jh2/sdeper:2.0.0 runImputation -q spatial.csv -l spatial_loc.csv -p spatial_prop.csv

singularity exec -B <path>:/data sdeper-2.0.0.sif runImputation -q spatial.csv -l spatial_loc.csv -p spatial_prop.csv

<path> is the valid and absolute path of the folder in the host machine where all input files locate, and this fold will be mounted in the Docker/Singularity image for data exchanging.

Important

Please check out the imputation input-related options page for file format requirements of each input file, and imputation output-related options for descriptions of output files containing the results of imputation.

Examples of input and output files can also be found in our GitHub repository SDePER_Analysis.

Tip

Imputation can also be run together with cell type deconvolution. To start cell type deconvolution followed by imputation with default settings, run:

runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv -l spatial_loc.csv --use_imputation true

docker run -it --rm -v <path>:/data az7jh2/sdeper:2.0.0 runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv -l spatial_loc.csv --use_imputation true

singularity exec -B <path>:/data sdeper-2.0.0.sif runDeconvolution -q spatial.csv -r scrna_ref.csv -c scrna_anno.csv -a adjacency.csv -l spatial_loc.csv --use_imputation true

<path> is the valid and absolute path of the folder in the host machine where all input files locate, and this fold will be mounted in the Docker/Singularity image for data exchanging.