Package index • scrattch.hicat

All functions
`CCA()`	CCA
`CanonCor()`	Canon cor
`DE_genes_cat_by_cl()`	Title
`DE_genes_pairs()`	reverse-compatibility wrapper around de_selected_pairs()
`DE_genes_pw()`	reverse-compatibility wrapper around de_all_pairs()
`RunUMAP(<default>)`	Adapt from Seurat package
`adjust_color()`	Correct duplicated colors
`angle()`	Title
`avgangle()`	Title
`batch_process()`	Batch process
`build_dend()`	Build dend
`build_dend_harmonize()`	Build dend harmonize
`build_dend_with_means()`	Title
`build_tsne()`	Title
`calc_tau()`	Calculate Tau scores for each gene
`check_donor()`	Find donor clusters
`check_neun()`	Check whether clusters are Neun+ or Neun-
`check_outlier()`	Check for outlier clusters
`check_qc()`	Check qc of numeric metric
`clean_outliers()`	Title
`collect_co_matrix()`	Collect co-clustering matrix from results files
`comb_de_result()`	Title
`combine_cl()`	combine cl
`compare_annotate()`	Compare two sets of cluster assignments for the same set of cells
`compare_plot()`	Compare and plot two sets of cluster assignments
`compute_knn()`	compute knn
`compute_vg_stats()`	Compute gene expression variance statistics
`convert_pair_matrix()`	Convert paired cluster comparison values to a matrix
`convert_pair_matrix_str()`	Convert paired cluster comparison values to a matrix
`cpm()`	Convert a matrix of raw counts to a matrix of Counts per Million values
`create_pairs()`	Title
`cutree_dend()`	Cut tree dendrogram
`de_all_pairs()`	Perform all pairwise differential expression comparison between clusters
`de_genes_pairs_multiple()`	Title
`de_pair()`	reverse-compatibility wrapper around score_pair_stats()
`de_pair_chisq()`	Perform pairwise differential detection tests using Chi-Squared for a single pair of clusters
`de_pair_limma()`	Perform pairwise DE tests using limma for a single pair of clusters
`de_param()`	Set differential expression (DE) parameters for genes and clusters.
`de_score()`	reverse-compatibility wrapper around de_all_pairs_stats()
`de_score_pairs()`	reverse-compatibility wrapper around de_selected_pairs_stats()
`de_selected_pairs()`	Perform pairwise differential gene expression tests between main pairs of clusters in parallel
`de_stats_all_pairs()`	Compute differential expression summary statistics for all pairs of clusters based on de_param()
`de_stats_pair()`	Compute differential expression summary statistics based on a differential results data.frame and de_param().
`de_stats_selected_pairs()`	Compute differential expression summary statistics for selected pairs of clusters based on de_param().
`dend_lca()`	Dend lca
`dend_list()`	Dendrogram list
`dend_match()`	Dendrogram match
`display_cl()`	Display cluster plot
`display_cl4()`	Title
`display_cl_markers_co.ratio()`	Title
`display_cl_one_vs_others()`	Title
`doubletFinder()`	Doublet detection in single-cell RNA sequencing data
`edgeMaker()`	function to draw (curved) line between two points
`fast_tsne()`	Title
`findVG()`	reverse-compatibility wrapper around find_vg()
`find_doublet()`	Find doublet
`find_low_quality_cl()`	Use DEGenes to identify clusters with few differentially expressed genes
`find_vg()`	Identify high variability genes using Brennecke's method
`gene_dispersion()`	Compute dispersion for each gene in a gene x sample matrix
`gene_gene_cor_conservation()`	Title
`gene_loess_fit()`	Compute dispersion-mean Loess-fit for each gene in a gene x sample matrix
`gene_loess_fit_z()`	Compute dispersion z scores for the residuals of a loess fit
`gene_means()`	Compute means for each gene in a gene x sample matrix
`gene_vars()`	Compute variance for each gene in a gene x sample matrix
`gene_z()`	Compute dispersion z-scores for each gene in a gene x sample matrix
`get.markers.num()`	Title
`getTopMarkersByPropNew()`	Find specific marker gene for each cluster
`get_RD_cl_center()`	Title
`get_beta_score()`	Title
`get_cell.cl.co.ratio()`	Get cell co-clustering ratios
`get_cl_df()`	Generate an initial cl.df object based on cl
`get_cl_mat()`	Generate a sparse matrix one-hot representation of clusters x samples
`get_cl_means()`	Compute cluster means for each row in a matrix
`get_cl_means_list()`	Title
`get_cl_medians()`	Compute cluster medians for each row in a matrix
`get_cl_present_list()`	Title
`get_cl_prop()`	Compute cluster proportions for each row in a matrix
`get_cl_sim()`	Title
`get_cl_sim_multiple()`	Title
`get_cl_sums()`	Compute cluster sums for each row in a matrix
`get_core_intermediate()`	Get core intermediate
`get_de_genes_sym()`	Get de genes sym
`get_de_lfc_list()`	Title
`get_de_matrix()`	Generate a matrix of pairwise DE results
`get_de_pair()`	Get de pair
`get_de_result()`	Title
`get_de_score()`	Get de score
`get_dend_markers()`	get dendrogram precomputed markers
`get_dend_markers_direction()`	get dendrogram precomputed markers (direction)
`get_dend_parent()`	Get dendrogram parent
`get_eigen()`	Compute module eigen genes
`get_gene_cl_correlation()`	Get gene cl correlation
`get_gene_score()`	Title
`get_knn()`	Get KNN
`get_knn_batch()`	get knn batch
`get_knn_graph()`	get knn graph
`get_knn_weight()`	Title
`get_pair_matrix()`	Subset a matrix as a vector using row and column positions
`get_pair_matrix_coor()`	Convert matrix row/column positions to vector position
`get_pairs()`	Convert underscore_separated pair names to a data.frame
`group_cl()`	Assign clusters to a group
`group_specific_markers()`	Title
`harmonize()`	Harmonize
`heatmap.3()`	A modified version of heatmap.2 from the gplots package for cluster plotting
`heatmap.4()`	Title
`i_harmonize()`	i harmonize
`impute_knn()`	Title
`impute_knn_global()`	Title
`impute_val_cor()`	Impute val cor
`incrementHex()`	Adjust color vector
`iter_clust()`	Iterative clustering algorithm for single cell RNAseq dataset
`iter_consensus_clust()`	Iterative consensus clustering
`iter_impute_knn()`	Title
`jaccard()`	Compute jaccard distances between columns of a matrix
`jaccard_leiden.RANN()`	Perform Jaccard/Louvain clustering based on RANN
`jaccard_leiden()`	Perform Jaccard/Leiden clustering
`jaccard_louvain.RANN()`	Perform Jaccard/Louvain clustering based on RANN
`jaccard_louvain()`	Perform Jaccard/Louvain clustering using Rphenograph
`knn_cor()`	KNN cor
`knn_cosine()`	KNN cosine
`knn_jaccard_louvain()`	KNN Jaccard Louvain
`knn_joint()`	knn joint
`label_dend()`	Label dendrogram
`lm_matrix()`	Title
`lm_normalize()`	add batch substracted median
`logCPM()`	Convert a matrix of raw counts to a matrix of log2(Counts per Million + 1) values
`makeColorsUnique()`	Make a vector of unique colors
`makeLayerLabel()`	Numeric layer for annotation object
`makeRegionLabel()`	Split region from roi for annotation object
`map_by_cor()`	Map samples to a training dataset by correlation
`map_cl_summary()`	Map a dataset to a reference, and compare existing cluster calls to the reference comparison
`map_cv()`	Run a single round of cross-validation of cluster mapping using a subset of marker genes
`map_dend()`	Title
`map_dend_markers()`	Map dend markers
`map_dend_membership()`	Title
`map_sampling()`	Perform bootstrapped mapping using a fraction of provided marker genes.
`mapping()`	Title
`markers_max_tau()`	Title
`markers_tau_one_vs_other()`	Title
`match_cl()`	Compute correlations of clusters to a reference set, and get the best-correlated reference clusters
`merge_cl()`	Merge clusters based on pairwise differential expressed genes.
`merge_cl_multiple()`	Title
`merge_knn_result()`	merge knn result
`multiplot()`	Title
`mybgplot3d()`	Title
`node_specific_markers()`	Node specific markers
`node_vs_sibling_markers()`	Node vs sibling markers
`onestep_clust()`	One round of clustering in the iteractive clustering pipeline
`pair_cor()`	Compute correlations between each matching row or column of two matrices
`perp()`	Title
`perpEnd()`	Title
`perpMid()`	Title
`perpStart()`	Title
`plot_2d_select()`	Title
`plot_3d_label()`	Title
`plot_3d_label_multiple()`	Title
`plot_3d_select()`	Title
`plot_3d_val()`	Title
`plot_RD_cl()`	Title
`plot_RD_gene()`	Title
`plot_RD_meta()`	Title
`plot_cl_cells()`	Title
`plot_cl_heatmap()`	Title
`plot_cl_heatmap4()`	Title
`plot_cl_low()`	Plot cl low
`plot_cl_meta_barplot()`	Title
`plot_confusion()`	Title
`plot_de_lfc_num()`	Title
`plot_de_num()`	Title
`plot_dend()`	Plot dendrogram
`plot_doublet()`	Plot doublet
`plot_low_qc()`	Plot low-quality clusters
`plot_markers()`	Title
`plot_markers_cl_means()`	Plot markers cl means
`plot_pair_matrix()`	Title
`plot_tSNE_gene()`	Title
`plot_tSNE_meta()`	Title
`plot_tsne()`	Plot tsne
`plot_tsne_cl()`	Title
`plot_vg()`	Generate gene variance plots
`prcomp(<irlba>)`	prcomp irlba
`predict_annotate_cor()`	Predict annotations by cluster correlation
`predict_knn()`	Title
`predict_knn_new()`	Title
`prune_dend()`	Prune dendrogram
`pvclust_show_signif_gradient()`	Pv clust show significant gradient
`rd_PCA()`	Title
`refine_cl()`	Refine clusters
`renameAndOrderClusters()`	Rename clusters using genes and metadata
`reorder_cl()`	Reorder cluster based on hiearchical clustering of clusters based on average cluster values for the input data matrix
`reorder_dend()`	Reorder dendrogram
`reorder_factor()`	Reorder factors of one annotation to match another
`rescale_samples()`	Rescale a gene x sample matrix
`resolve_cl()`	Title
`run_consensus_clust()`	Wrapper function to repeatively run clustering on subsampled cells and infer consensus clusters
`sample_cells()`	Downsample cells from each cluster
`sample_sets_list()`	Sample sets lists
`select_N_markers()`	Title
`select_dend_markers()`	Select dend markers
`select_joint_genes()`	Select joint genes
`select_joint_markers()`	Title
`select_markers()`	Title
`select_markers_pair()`	Title
`select_markers_pair_direction()`	Title
`select_markers_pair_group()`	Title
`select_node_specific_markers()`	Select node specific markers
`select_pos_dend_markers()`	Select pos dend markers
`select_pos_markers()`	Title
`set_pair_matrix()`	Update a matrix with values from a 1d vector using row and column positions
`sim_knn()`	Sim knn
`simple_dend()`	Simple dendrogram
`sparse_cor()`	Compute correlation scores for columns of a sparse matrix
`summarize_cl()`	Title
`test_knn()`	Test knn
`test_merge()`	Title
`unbranch_by_conf()`	Unbranch by conf
`unbranch_by_length()`	Unbranch by length
`updateSampDat()`	Update cluster names in annotation data frame
`vec_chisq_test()`	Vectorized Chi-squared tests for differential gene detection
`within_group_specific_markers()`	Title

Reference

All functions