Package index • MAYA

All functions
`MAYA_pathway_analysis()`	Run MAYA for unsupervised pathway acitivity analysis
`MAYA_predict_cell_types()`	Run MAYA for unsupervised cell annotation with cell type
`annotate_cells()`	Annotate individual cells
`average_by_cluster()`	Average matrix by column
`build_activity_mat()`	Extract activity matrix from PCA object
`.activity_assignmentThreshold()`	Auxiliary function to determine if a vector is informative.
`generate_cell_type_annotation()`	Generate cell type annotation for each cell and provide complementary results to interpret results
`generate_palette()`	Generate custom color palette
`generate_summary_specific_modules()`	Generate summary of specific pathways and top contributing genes given an annotation
`homogeneity_table()`	Compute homogeneity table given activity score matrix
`jaccard()`	Compute Jaccard distance from kNN matrix
`knn_jaccard()`	Compute Jaccard distance from kNN results
`logcpmNormalization()`	Transform raw count matrix into logCPM matrix
`plot_heatmap_activity_mat()`	Plots heatmap showing activity scores for each activation mode
`plot_heatmap_pathway_genes()`	Plots heatmap showing individual gene expression of a pathway
`plot_heatmap_pathway_top_contrib_genes()`	Plots heatmap of top contributing genes for each mode
`plot_pathway_specificity()`	Plots barplot colored by cluster showing distribution of specificity between clusters for each activation mode
`plot_umap_annot()`	Plots UMAP colored by cell annotation
`plot_umap_gene()`	Plots UMAP colored by expression level of a gene
`plot_umap_pathway_activity()`	Plots UMAP colored by activity level of a module
`read_gmt()`	Read GMT file
`run_activity_analysis()`	Compute pathway analysis using PCA spotting PCs driven by only one gene.
`run_umap()`	Run UMAP with fixed seed for reproducibility
`scale_0_1()`	Scale matrix rows between 0 and 1
`specificity_table()`	Compute specificity table given activity score matrix
`study_pathways()`	Build activity matrix and compute UMAP

Reference

All functions