scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

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Documentation for package ‘scFeatures’ version 1.9.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

example_scrnaseq	Example of scRNA-seq data
get_num_cell_per_spot	Estimate a relative number of cells per spot for spatial transcriptomics data
remove_mito_ribo	Remove mitochondrial and ribosomal genes, and other highly correlated genes
run_association_study_report	Create an association study report in HTML format
run_CCI	Generate cell cell communication score
run_celltype_interaction	Generate cell type interaction
run_gene_cor	Generate overall aggregated gene correlation
run_gene_cor_celltype	Generate cell type specific gene expression correlation
run_gene_mean	Generate overall aggregated mean expression
run_gene_mean_celltype	Generate cell type specific gene mean expression
run_gene_prop	Generate overall aggregated gene proportion expression
run_gene_prop_celltype	Generate cell type specific gene proportion expression
run_L_function	Generate L stats
run_Morans_I	Generate Moran's I
run_nn_correlation	Generate nearest neighbour correlation
run_pathway_gsva	Generate pathway score using gene set enrichement analysis
run_pathway_mean	Generate pathway score using expression level
run_pathway_prop	Generate pathway score using proportion of expression
run_proportion_logit	Generate cell type proportions, with logit transformation
run_proportion_ratio	Generate cell type proportion ratio
run_proportion_raw	Generate cell type proportion raw
scFeatures	Wrapper function to run all feature types in scFeatures
scfeatures_result	Example of scFeatures() output