Man pages for SydneyBioX/scFeatures
scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction
| example_scrnaseq | Example of scRNA-seq data |
| get_num_cell_per_spot | Estimate a relative number of cells per spot for spatial... |
| remove_mito_ribo | Remove mitochondrial and ribosomal genes, and other highly... |
| 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 |
