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

## Documentation for package ‘scFeatures’ version 1.0.0

- 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
makeSeurat | Format data into Seurat object structured for scFeatures functions
process_data | data pre-processing
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