Package index • tiltedCCA

All functions

compute_snns(): Include SNN graphs to multiSVD

compute_synchrony(): Compute the synchrony based on Tilted-CCA

consensus_pca(): Computing Consensus PCA

construct_celltype_subsample(): Construct cell-type subsamples

create_SeuratDim(): Create Seurat dimension reduction objects of Tilted-CCA

create_multiSVD(): Create the object to initialize Tilted-CCA

differential_expression(): Compute the differential expression across each pair of cell types

.cca(): Perform CCA

.compute_cca_aggregate_matrix(): Helper function with the CCA function

.compute_distinct_score(): Compute the distinct scores

.compute_prob_mat(): Compute probability matrix

.compute_unnormalized_scores(): Using the CCA solution, compute the score matrices.

.generate_adjaceny_mat(): Simulate adjacency matrix

.project_vec2vec(): Projection of vector onto another vector

.tiltedCCA_common_score(): Main workhorse of dcca_factor

fine_tuning(): Fine tune the common tilts, one for each latent dimension

form_metacells(): Include meta-cell information to multiSVD

generate_random_orthogonal(): Generate orthogonal matrices via Gaussian noise

generate_sbm_orthogonal(): Generate orthogonal matrices correspond to an SBM

plot_alignment(): Plot alignment of variables

plot_cell_enrichment(): Plot cell enrichment

plot_clisi(): Making the plot for local enrichment

plot_clisi_legend(): Plot the cLISI legend

plot_heatmat(): Heatmap of the data

plot_scores(): Side-by-side plot of the canonical scores, colored by membership

plot_scores_heatmap.dcca(): Side-by-side plot of the canonical scores as heatmaps

plot_scores_heatmap.list(): Side-by-side plot of the canonical scores as heatmaps

plot_summary(): Plot summary of D-CCA

postprocess_cell_enrichment(): Computing cell enrichments (generic)

postprocess_cell_enrichment(<default>): Computing cell enrichments for matrix object

postprocess_cell_enrichment(<multiSVD>): Computing cell enrichments for multiSVD object

postprocess_depvalue(): Compute the negative log-10 p-values across all cell types

postprocess_marker_variables(): Find the marker genes the separate one cell type from all other cell types

postprocess_modality_alignment(): Compute how aligned the common component for a feature is with that feature in the original data modality

rotate_seurat_embeddings(): Rotate embedding in a Seurat object

tiltedCCA(): Tilted-CCA Factorization

tiltedCCA_decomposition(): Tilted-CCA Decomposition