Package index

General getters

All types of general getters that work across various classes related to co-expression module detection, graph-based clustering, etc.

get_metadata(): Return the metadata
get_params() get_params.Hotspot() get_params.miloR() get_params.ScenicGrn(): Get the parameters that were used.
get_results(): Get the final results from the class

Gene set enrichment helpers

Everything and anything you need to do various types of gene set enrichments; hypergeometric tests, GSVA, (ss)GSEA.

gse_hypergeometric(): Gene set enrichment (GSE) based on a hypergeometric test.
gse_hypergeometric_list(): Gene set enrichment (GSE) based on a hypergeometric test over a list.
calc_fgsea(): Bixverse implementation of the fgsea algorithm
calc_fgsea_simple(): Bixverse implementation of the simple fgsea algorithm
calc_gsea_traditional(): Bixverse implementation of the traditional GSEA algorithm
calc_mitch(): Calculate a mitch gene set enrichments on contrast
calc_gsva(): Bixverse implementation of GSVA
calc_ssgsea(): Bixverse implementation of ssGSEA
params_gsea(): Wrapper function to generate GSEA parameters
params_gsva(): Wrapper function to generate GSVA parameters
params_ssgsea(): Wrapper function to generate ssGSEA parameters

GSE methods specifically designed with the DAG of the Gene Ontology in mind, identifying the most relevant Gene Ontology terms.

load_go_human_data(): Get the Gene Ontology data human
get_go_data_human(): Wrapper function to load and process the gene ontology data.
process_go_data(): Process Gene Ontology data into the right format
GeneOntologyElim(): Gene Ontology data
gse_go_elim_method(): Run gene ontology enrichment with elimination method.
gse_go_elim_method_list(): Run gene ontology enrichment with elimination method over a list.
fgsea_go_elim(): Run GO enrichment with elimination method over a continuous vectors
fgsea_simple_go_elim(): Run GO enrichment with elimination with fgsea simple
simplify_hypergeom_res(): Simplify gene set results via ontologies

Functions related to CisTarget enrichment.

download_cistarget_hg38(): Download CisTarget reference files for human (hg38)
read_motif_annotation_file(): Read in the motif annotation file
read_motif_ranking(): Read in the motif rankings and transform them into a matrix
run_cistarget(): Main function to run CisTarget
params_cistarget(): Wrapper function to CisTarget parameters

Correlation-based gene module detections

Methods to identify co-expression modules via correlations in the data. Includes methods for differential correlations, some graph-based and some hierarchical clustering based ones.

BulkCoExp(): Bulk RNAseq co-expression modules
preprocess_bulk_coexp(): Process the raw data
cor_module_check_epsilon(): Iterate through different epsilon parameters
cor_module_coremo_clustering(): Generates CoReMo-based gene modules
cor_module_coremo_cor_sign(): Split CoReMo modules by correlation sign
cor_module_coremo_eigengene(): Calculate Eigengenes for CoReMo modules
cor_module_coremo_stability(): Assesses CoReMo-based gene module stability
cor_module_graph_check_res(): Iterate through Leiden resolutions for graph-based community detection.
cor_module_graph_final_modules(): Identify correlation-based gene modules via graphs
cor_module_processing(): Prepare correlation-based module detection
cor_module_tom(): Update the correlation matrix to a TOM
diffcor_module_processing(): Prepare differential correlation-based module detection
get_cor_graph(): Get correlation-based graph
get_diffcor_graph(): Get differential correlation-based graph
get_epsilon_res(): Return the epsilon data
get_resolution_res(): Return the resolution results
get_outputs(): Return the outputs
params_cor_graph(): Wrapper function for graph generation
params_coremo(): Wrapper function to generate CoReMo parameters

Matrix factorisation methods

Methods to identify co-expression via matrix factorisations. Uses the same class as the correlation-based ones. You have contrastive PCA, ICA and dual graph-regularised dictionary learning as methods.

contrastive_pca_processing(): Prepare class for contrastive PCA
c_pca_plot_alphas(): Plot various alphas for the contrastive PCA
contrastive_pca(): Apply contrastive PCA.
get_c_pca_factors(): Get the contrastive PCA factors
get_c_pca_loadings(): Get the contrastive PCA loadings
dgrdl_grid_search(): Grid search over DGRDL parameters
dgrdl_result(): Run DGRDL with the specified parameters
get_ica_stability_res(): Get the ICA component data (stability, convergence, nMI)
get_grid_search_res(): Get the grid search results
ica_evaluate_comp(): Iterate over different ncomp parameters for ICA
ica_optimal_ncomp(): Identify stability inflection point
ica_processing(): Prepare class for ICA
ica_stabilised_results(): Run stabilised ICA with a given number of components
params_dgrdl(): Wrapper function to generate DGRDL parameters
params_ica_general(): Wrapper function for standard ICA parameters
params_ica_ncomp(): Wrapper function for ICA ncomp iterations
params_ica_randomisation(): Wrapper function for ICA randomisation

Helpers for differential gene expression

Methods to help out with differential gene expression analyses in a structured way. Useful when you have to analyse 10’s to 100’s of differential gene expression results.

BulkDge(): Bulk RNAseq differential gene expression class
add_new_metadata(): Replace the meta data
change_gene_identifier(): Change the primary gene identifier of BulkDge
update_metadata_values(): Replace values in a metadata column
fix_meta_data_column(): Helper to fix meta-data columns to be R conform
remove_samples(): Remove samples from object
qc_bulk_dge(): QC on the bulk dge data
preprocess_bulk_dge(): QC on the bulk dge data (DEPRECATED!)
normalise_bulk_dge(): Normalise the count data for DGE.
batch_correction_bulk_dge(): Run a linear batch correction
bulk_dge_from_h5ad(): Wrapper function to generate BulkDge object from h5ad
calculate_dge_hedges(): Calculates the Hedge's G effect size
calculate_all_dges(): Calculate all possible DGE variants (DEPRECATED!)
calculate_dge_limma(): Calculates the Limma Voom DGE
calculate_pca_bulk_dge(): Calculate PCA on the expression.
calculate_rpkm(): RPKM calculation
calculate_tpm(): TPM calculation
run_limma_voom(): Wrapper for a Limma Voom analysis
hedges_g_dge(): Calculate the effect size
get_dge_effect_sizes(): Return the effect size results
get_dge_limma_voom(): Return the Limma Voom results
get_dge_list(): Return the DGEList
get_dge_qc_plot(): Return QC plots
get_fpkm_counts(): Return the FPKM-normalised counts
get_gene_lengths(): Get the gene lengths
get_model_fit(): Get the fitted model
get_tpm_counts(): Return the TPM-normalised counts

Biomedical ontologies

For dealing with ontologies and calculating (semantic) similarities in disease, phenotype or gene ontologies.

OntologySim(): OntologySim class
pre_process_sim_onto(): Pre-process data for subsequent ontology similarity
calculate_information_content(): Calculate the information content for each ontology term
calculate_semantic_sim(): Calculate the Resnik or Lin semantic similarity
calculate_semantic_sim_mat(): Calculate the Resnik or Lin semantic similarity matrix
calculate_semantic_sim_onto(): Calculate the Resnik or Lin semantic similarity for an ontology.
calculate_wang_sim(): Calculate the Wang similarities between terms
calculate_wang_sim_mat(): Calculate the Wang similarity matrix
calculate_wang_sim_onto(): Calculate the Wang similarity for an ontology.
filter_similarities(): Filter the calculated similarities
calculate_critical_value(): Calculates the critical value
get_sim_matrix(): Get the similarity matrix
get_ontology_ancestry(): Return ancestry terms from an ontology

Helpers for graph-based analysis

Different methods working on graphs; diffuse information over a network and identify communities, generate reciprocal best hit graphs from correlations or set similarities, fuse networks together via similarity network fusion.

NetworkDiffusions(): Network diffusion class
calculate_diffusion_auc(): Calculate the AUROC for a diffusion score
community_detection(): Identify privileged communities based on a given diffusion vector
constrained_page_rank(): Constrained personalised page rank
constrained_page_rank_ls(): Constrained personalised page rank over a list
diffuse_seed_nodes(): Diffuse seed genes over a network
permute_seed_nodes(): Generate permuation scores for the diffusion
get_diffusion_perms(): Get the diffusion permutations
generate_personalisation_vec(): Helper function to create personalisation vectors
tied_diffusion(): Diffuse seed genes in a tied manner over a network
RbhGraph(): Reciprocal best hit graph
find_rbh_communities(): Find RBH communities
generate_rbh_graph(): Generate an RBH graph.
get_diffusion_vector(): Get the diffusion vector
get_rbh_res(): Get the RBH results
SimilarityNetworkFusion(): Similarity network fusion
add_snf_data_modality(): Add a data modality for SNF generation
get_snf_params(): Get the SNF params
get_snf_final_mat(): Get the final SNF matrix
get_snf_adjcacency_mat(): Get an individual affinity matrix
params_graph_resolution(): Wrapper function to generate resolution parameters for Leiden or Louvain clustering.
run_snf(): Run the SNF algorithm
params_community_detection(): Wrapper function to generate community detection parameters
params_snf(): Wrapper function to generate SNF parameters

Single cell classe and getters

THE single cell class with a large number of getters.

SingleCells(): bixverse single cell class
get_sc_obs(): Getter the obs table
get_sc_var(): Getter the var table
get_sc_counts(): Getter the counts
get_available_embeddings(): Get the available embeddings from the cache
get_cell_indices(): Get the index position for a gene
get_cell_names(): Get the cell names
get_cells_to_keep(): Get the cells to keep
get_embedding(): Get the embedding from the cache
get_gene_indices(): Get the index position for a gene
get_gene_names(): Get the gene names
get_hvg(): Get the HVG
get_knn_mat(): Get the KNN matrix
get_knn_obj(): Get the KNN object
get_pca_singular_val(): Get the PCA singular values
get_pca_loadings(): Get the PCA loadings
get_pca_factors(): Get the PCA factors
get_snn_graph(): Get the sNN graph
get_gene_names_from_idx(): Get the gene names based on the gene idx
get_sc_available_features(): Returns the available features for single cell applications
setnames_sc(): Rename columns in the obs or var table
set_sc_new_obs_col(): Add a new column to the obs table
set_sc_new_obs_col_multiple(): Add multiple new columns to the obs table
set_sc_new_var_cols(): Add a new column to the var table

Generating meta cells.

MetaCells(): bixverse meta cell class
calc_meta_cell_purity(): Calculate meta cell purity
generate_supercells_sc(): Generate SuperCells and return a MetaCells object
generate_meta_cells_sc(): Generate meta cells based on hdWGCNA and return a MetaCells object
generate_seacells_sc(): Generate meta cells based on SEACells and return a MetaCells object
params_sc_supercell(): Wrapper function for parameters for SuperCell generation
params_sc_metacells(): Wrapper function for parameters for meta cell generation
params_sc_seacells(): Wrapper function for the SEACells parameters

Single cell i/o

I/O functions for single cell. Load in h5ad, mtx, Seurat or R data into Rust and the DuckDB supporting the metadata.

get_cell_ranger_params(): Helper to generate cell ranger input parameters
get_h5ad_dimensions(): Helper function to get the dimensions and compressed sparse format
prescan_h5ad_files(): Pre-scan multiple h5ad files for multi-sample loading
load_existing(): Load an existing SingleCells from disk
load_h5ad(): Load in h5ad to SingleCells
load_h5ad_norm(): Load in h5ad with normalised counts to SingleCells
load_mtx(): Load in mtx/plain text files to SingleCells
load_multi_h5ad(): Load multiple h5ad files into a single SingleCells
stream_h5ad(): Stream in h5ad to SingleCells
load_r_data(): Load in data directly from R objects.
load_seurat(): Load in Seurat to SingleCells
read_h5ad_metadata(): Read obs and var tables and metadata from an h5ad file
read_h5ad_x_summary(): Read summary statistics from the X slot of an h5ad file
save_sc_exp_to_disk(): Save memory-bound data to disk
sc_old_file_conversion(): Convert legacy v2 single-cell data files to v3 format
params_sc_min_quality(): Wrapper function to generate QC metric params for single cell
params_sc_mtx_io(): Wrapper function to provide data for mtx-based loading

Single cell processing

Helpers to process single cell data. Doublet detection, proportions of gene sets, HVG (batch-aware), PCA and batch corrections.

scrublet_sc(): Doublet detection with Scrublet
call_doublets_manual(): Helper function to manually readjust Scrublet thresholds
doublet_detection_boost_sc(): Doublet detection with boosted doublet classification
gene_set_proportions_sc(): Calculate the proportions of reads for specific gene sets
per_cell_qc_outlier(): Use MAD outlier detection on per-cell QC metrics
run_cell_qc(): Run MAD outlier detection on per-cell QC metrics
find_hvg_sc(): Identify HVGs
find_hvg_batch_aware_sc(): Identify HVGs (batch aware)
calculate_pca_sc(): Run PCA for single cell
generate_sc_knn(): Generate a new SingleCellNearestNeighbour from data
find_neighbours_sc(): Find the neighbours for single cell.
top_genes_perc_sc(): Calculate the proportions of reads for the Top N genes
fast_mnn_sc(): Run fastMNN
harmony_sc(): Run Harmony
bbknn_sc(): Run BBKNN
calculate_kbet_sc(): Calculate kBET scores
calculate_batch_asw_sc(): Calculate batch average silhouette width
calculate_batch_lisi_sc(): Calculate batch LISI scores
params_norm_doublet_detection_defaults(): Helper function to generate normalisation defaults for doublet detection.
params_boost(): Wrapper function for Boost parameters
params_sc_hvg(): Wrapper function for HVG detection parameters.
params_scrublet(): Wrapper function for Scrublet doublet detection parameters
params_sc_bbknn(): Wrapper function for the BBKNN parameters
params_sc_fastmnn(): Wrapper function for the fastMNN parameters
params_sc_harmony(): Default parameters for Harmony batch correction
params_sc_neighbours(): Wrapper function for parameters for neighbour identification in single cell
params_hvg_defaults(): Helper function to generate HVG defaults
params_pca_defaults(): Helper function to generate default parameters for PCA
params_knn_defaults(): Helper function to generate kNN defaults

Single cell analysis methods

A large number of different methods to extract insights from your single cell experiment. Gene set scoring, DGEs, kNN generations, pseudo-bulk count extraction, miloR, Hotspot, VISION and SCENIC.

aucell_sc(): Calculate AUC scores (akin to AUCell)
module_scores_sc(): Calculate module activity scores
find_clusters_sc(): Graph-based clustering of cells on the sNN graph
find_markers_sc(): Calculate DGE between two cell groups
find_all_markers_sc(): Find all markers
get_pseudobulked_sc(): Generate pseudo-bulked matrices
generate_knn_sc(): Generate the KNN data with distances
get_differential_abundance_res(): Get the differential abundance results
hotspot_autocor_sc(): Calculate the local auto-correlation of a gene
hotspot_gene_cor_sc(): Calculate the local pairwise gene-gene correlation
generate_hotspot_membership(): Identify hotspot gene clusters
get_hotspot_membership(): Get the hotspot gene membership table
get_miloR_abundances_sc(): Generate an miloR abundance object for differential abundance testing
get_index_cells(): Get the index cells
add_nhoods_info(): Add neighbourhood info on majority cell type
test_nhoods(): Test neighbourhoods for differential abundance
vision_sc(): Calculate VISION scores
vision_w_autocor_sc(): Calculate VISION scores (with auto-correlation scores)
identify_tf_to_genes(): Identify the TF to gene regulation
scenic_gene_filter_sc(): Filter genes for SCENIC GRN inference
scenic_grn_sc(): Run SCENIC GRN inference
get_cistarget_res(): Extract the TF to gene data from the ScenicGrn object
get_tf_to_gene(): Extract the TF to gene data from the ScenicGrn object
tf_to_genes_correlations(): Generate TF to gene correlations
tf_to_genes_motif_enrichment(): Run the SCENIC motif enrichment
params_sc_hotspot(): Wrapper function for parameters for HotSpot
params_sc_miloR(): Wrapper function for parameters for MiloR
params_sc_vision(): Wrapper function for parameters for VISION with auto-correlation
params_scenic(): Constructor for SCENIC parameters
params_scenic_extra_trees_defaults(): Default parameters for the SCENIC ExtraTrees regression learner
params_scenic_gradient_boosting_defaults(): Default parameters for the SCENIC GradientBoosting (GRNBoost2) regression learner
params_scenic_random_forest_defaults(): Default parameters for the SCENIC RandomForest regression learner

Additional helpers for specific small sub classes used in single cell.

get_knn_dist(): Get the KNN distance measures
get_obs_data(): Get the ready obs data from various sub method
new_sc_knn(): Helper function to generate kNN data with distances

Single-cell plotting stuff

Various helpers that generate data for plotting single cell, such as 2D embeddings, or extract specific columns from the metadata or genes (and their summaries) from the binary storage files.

sc_knn_to_nearest_neighbours(): Convert SingleCellNearestNeighbour to manifoldsR NearestNeighbours
umap_sc(): Run UMAP on a SingleCells object
tsne_sc(): Run t-SNE on a SingleCells object
phate_sc(): Run PHATE on a SingleCells object
extract_dot_plot_data(): Extract grouped gene statistics for dot plots
extract_gene_expression(): Extract normalised gene expression for plotting

Stastical functions

Any types of functions that help with statistics

calculate_effect_size(): Calculate the Hedge G effect between two matrices
calculate_tom(): Calculate the TOM from a correlation matrix
calculate_tom_from_exp(): Calculate the TOM from an expression matrix
f1_score_confusion_mat(): F1 scores on top of a confusion matrix
fast_ica_rust(): Fast ICA via Rust
fast_ica_rust_helper(): Fast ICA via Rust from processed data
get_inflection_point(): Identify the inflection point for elbow-like data
ot_harmonic_score(): Calculates a harmonic sum normalised between 0 to 1.
robust_scale(): Robust scaler.

Plotting helpers

Some core plotting helpers in the package (usually for QC). The ones to plot downstream results can be found in bixverse.plots.

plot_boxplot_normalization(): Helper plot function for boxplot of normalized data
plot_epsilon_res(): Plot the epsilon vs. power law goodness of fit result
plot_hvgs(): Plot the highly variable genes
plot_ica_ncomp_params(): Plot various parameters with no comp
plot_ica_stability_individual(): Plot the stability of the ICA components
plot_optimal_cuts(): Plot the k cuts vs median R2
plot_pca(): Helper plot function for pca with contrasts
plot_pca_res(): Plot the PCA data
plot_preprocessing_genes(): Helper plot function of distribution of genes by samples
plot_preprocessing_outliers(): Helper plot function for identification of outliers
plot_rbf_impact(): Helper function to plot distance to affinity relationship
plot_resolution_res(): Plot the resolution results.
plot_voom_normalization(): Helper plot function for Voom normalisation

Data downloads and synthetic data generation

Functions and helpers to download or generate synthetic data.

download_pbmc3k(): Download PBMC3K data from Zenodo
download_demuxlet_pbmc(): Download PBMCs with demuxlet doublet information
download_pbmc_batches(): Download two different PBMC data sets for batch correction testing
calculate_sparsity_stats(): Helper function to calculate the induced sparsity
generate_gene_module_data(): Generates synthetic gene module data.
generate_single_cell_test_data(): Single cell test data
cell_cycle_genes: Cell cycle genes
write_cellranger_output(): Helper function to write data to a cell ranger like output
write_h5ad_sc(): Helper function to write data to h5ad format
params_sc_synthetic_data(): Default parameters for generation of synthetic data
synthetic_signal_matrix(): Generates a simple synthetic, pseudo gene expression matrix
simulate_dropouts(): Simulate dropouts via different functions on synthetic bulk data
synthetic_bulk_cor_matrix(): Generates synthetic bulk RNAseq data
synthetic_c_pca_data(): Generates synthetic data for contrastive PCA exploration.

Utils

All types of other random helpers without a clear pattern

AnnDataParser: Class for Anndata
calculate_sparsity_stats(): Helper function to calculate the induced sparsity
get_seurat_counts_to_list(): Transform Seurat raw counts into a List
knn_graph_label_propagation(): kNN-based graph label propagation
params_label_propagation(): Wrapper function to generate label propagation parameters
upper_triangle_to_sparse(): Transform an upper triangle-stored matrix to a sparse one
upper_triangular_sym_mat: Class for symmetric correlation matrices
to_snake_case(): Helper function to transform strings to snake_case

Rust wrappers

Everything rusty - only use this if you know what you are doing… Maybe useful for your own package? Use with care and read the documentation!

rs_aucell(): Calculate AUCell in Rust
rs_batch_lisi(): Calculate batch LISI scores
rs_batch_silhouette_width(): Calculate batch silhouette width from an embedding
rs_bbknn(): BBKNN implementation in Rust
rs_bbknn_filtering(): Reduce BBKNN results to Top X neighbours
rs_calc_es(): Calculates the traditional GSEA enrichment score
rs_calc_gsea_stat_cumulative_batch(): Helper function to generate fgsea simple-based permutations
rs_calc_gsea_stat_traditional_batch(): Helper function to generate traditional GSEA-based permutations
rs_calc_gsea_stats(): Rust implementation of the fgsea::calcGseaStat() function
rs_calc_multi_level(): Calculates p-values for pre-processed data
rs_calculate_dge_mann_whitney(): Calculate DGEs between cells based on Mann Whitney stats
rs_cistarget(): Run CisTarget motif enrichment analysis
rs_cluster_stability(): Helper function to assess cluster stability
rs_compare_knn(): Helper to compare kNN graphs
rs_constrained_page_rank(): Calculate a constrained page-rank score
rs_constrained_page_rank_list(): Calculate a constrained page-rank score over a list.
rs_contrastive_pca(): Calculate the contrastive PCA
rs_cor(): Calculate the column wise correlations.
rs_cor2(): Calculate the column wise correlations.
rs_coremo_quality(): Helper function to assess CoReMo cluster quality
rs_coremo_stability(): Helper function to assess CoReMo cluster stability
rs_cor_upper_triangle(): Calculate the column wise correlations.
rs_cos(): Calculate the column wise cosine similarities
rs_count_zeroes(): Helper to get zero stats from a given matrix
rs_cov2cor(): Calculates the correlation matrix from the co-variance matrix
rs_covariance(): Calculate the column-wise co-variance.
rs_create_random_aucs(): Create random AUCs
rs_critval(): Calculate the critical value
rs_critval_mat(): Calculate the critical value
rs_dense_to_upper_triangle(): Generate a vector-based representation of the upper triangle of a matrix
rs_differential_cor(): Calculate the column wise differential correlation between two sets of data.
rs_dist(): Calculate the pairwise column distance in a matrix
rs_extract_grouped_gene_stats(): Calculates the gene statistics for a set of cell groups and genes
rs_extract_several_genes_plots(): Helper to extract single cell counts for several genes
rs_extract_counts_plots(): Helper to extract single cell counts as a dense vector for plotting
rs_fast_auc(): Fast AUC calculation
rs_fast_ica(): Run the Rust implementation of fast ICA.
rs_fdr_adjustment(): Calculate a BH-based FDR
rs_filter_onto_sim(): Filter the term similarities for a specific critical value
rs_generate_bulk_rnaseq(): Generation of bulkRNAseq-like data with optional correlation structure
rs_geom_elim_fgsea_simple(): Run fgsea simple method for gene ontology with elimination method
rs_get_gs_indices(): Helper function to rapidly retrieve the indices of the gene set members
rs_get_metacells(): Generate meta cells (hdWGCNA method)
rs_get_seacells(): Generate SEACells
rs_gower_dist(): Calculates the Gower distance for a given matrix
rs_gse_geom_elim(): Run hypergeometric enrichment over the gene ontology
rs_gse_geom_elim_list(): Run hypergeometric enrichment a list of target genes over the gene ontology
rs_gsva(): Rust version of the GSVA algorithm
rs_h5ad_data(): Load in h5ad data via Rust
rs_hamming_dist(): Calculates the Hamming distance between categorical columns
rs_harmony(): Harmony batch correction in Rust
rs_hedges_g(): Calculate the Hedge's G effect
rs_hotspot_autocor(): Calculate gene spatial auto-correlations
rs_hotspot_cluster_genes(): Cluster the genes by Z-score together
rs_hotspot_gene_cor(): Calculate gene<>gene spatial correlations
rs_hypergeom_test(): Run a single hypergeometric test.
rs_hypergeom_test_list(): Run a hypergeometric test over a list of target genes
rs_ica_iters(): Run ICA over a given no_comp with random initilisations of w_init
rs_ica_iters_cv(): Run ICA with cross-validation and random initialsiation
rs_importance_threshold(): SCENIC: Select TF-gene pairs by per-gene importance threshold
rs_jaccard_row_integers(): Calculate rapidbly Jaccard similarities between rows
rs_kbet(): Calculate kBET type scores
rs_knn_label_propagation(): kNN label propagation
rs_knn_mat_to_edge_list(): Flatten kNN matrix to edge list
rs_knn_mat_to_edge_pairs(): Flatten kNN matrix to edge list
rs_mad_outlier(): Calculate MAD outlier detection in Rust.
rs_make_milor_nhoods(): Generate the neighbourhoods akin to the miloR approach
rs_mitch_calc(): Calculate mitch enrichment leveraging Rust under the hood
rs_mnn(): FastMNN batch correction in Rust
rs_module_scoring(): Calculate module activity scores in Rust
rs_mutual_info(): Calculates the mutual information matrix
rs_onto_semantic_sim(): Calculate the semantic similarity in an ontology
rs_onto_semantic_sim_mat(): Calculate the semantic similarity in an ontology
rs_onto_sim_wang(): Calculate the Wang similarity for specific terms
rs_onto_sim_wang_mat(): Calculate the Wang similarity matrix for an ontology
rs_ot_harmonic_sum(): Calculate the OT harmonic sum
rs_page_rank(): Rust version of calcaluting the personalised page rank
rs_page_rank_parallel(): Calculate massively parallelised personalised page rank scores
rs_pairwise_gene_cors(): Calculates pairwise gene correlations in single cell
rs_phyper(): Calculate the hypergeometric rest in Rust
rs_pointwise_mutual_info(): Calculates the point wise mutual information
rs_prcomp(): Rust implementation of prcomp
rs_prepare_gsva_gs(): Prepare a pathway list for GSVA
rs_prepare_whitening(): Prepare the data for whitening
rs_pseudobulk_cells_dense(): Pseudo-bulk a set of cells (dense)
rs_pseudobulk_cells_sparse(): Pseudo-bulk a set of cells (sparse)
rs_random_svd(): Run randomised SVD over a matrix
rs_range_norm(): Apply a range normalisation on a vector.
rs_rbf_function(): Apply a Radial Basis Function
rs_rbf_function_mat(): Apply a Radial Basis Function (to a matrix)
rs_rbf_iterate_epsilons(): Helper to identify the right epsilon parameter
rs_rbh_cor(): Generate reciprocal best hits based on correlations
rs_rbh_sets(): Generate reciprocal best hits based on set similarities
rs_sample_ids_for_cell_types(): Helper function to generate sample identifiers based on cells
rs_sc_doublet_detection(): Detect Doublets via BoostClassifier (in Rust)
rs_sc_get_gene_set_perc(): Calculate the percentage of gene sets in the cells
rs_sc_get_top_genes_perc(): Calculates the cumulative proportion of the top X genes
rs_sc_hvg(): Calculate the percentage of gene sets in the cells
rs_sc_hvg_batch_aware(): Calculate HVG per batch
rs_sc_knn(): Generates the kNN graph
rs_sc_knn_w_dist(): Generates the kNN graph with additional distances
rs_sc_pca(): Calculates PCA for single cell
rs_sc_pca_sparse(): Calculates sparse PCA for single cell
rs_sc_scrublet(): Scrublet Rust interface
rs_sc_snn(): Generates the sNN graph for igraph
rs_scenic_gene_filter(): Identifies genes to include into a SCENIC analysis
rs_scenic_grn(): SCENIC: Generating gene-regulatory networks
rs_scenic_grn_streaming(): SCENIC: Generating gene-regulatory networks (streaming version)
rs_set_similarity(): Set similarities
rs_set_similarity_list(): Set similarities over one list
rs_set_similarity_list2(): Set similarities over two list
rs_simple_and_multi_err(): Calculates the simple and multi error for fgsea multi level
rs_simulate_dropouts(): Sparsify bulkRNAseq like data
rs_snf(): Similarity network fusion
rs_snf_affinity_cat(): Calculate the SNF affinity matrix for categorical values
rs_snf_affinity_continuous(): Calculate the SNF affinity matrix for continuous values
rs_snf_affinity_mixed(): Calculate the SNF affinity matrix for mixed values
rs_sparse_dict_dgrdl(): Generate a sparse dictionary with DGRDL
rs_sparse_dict_dgrdl_grid_search(): Generate a sparse dictionary with DGRDL
rs_spectral_clustering(): Rust implementation of spectral clustering
rs_spectral_clustering_sim(): Rust implementation of spectral clustering
rs_split_cor_signs(): Helper function to split correlation matrices by sign
rs_ssgsea(): Rust version of the ssGSEA algorithm
rs_supercell(): Generate SuperCells.
rs_synthetic_sc_data_csc(): Generate synthetic single cell data (Seurat type)
rs_synthetic_sc_data_csr(): Generate synthetic single cell data (h5ad type)
rs_synthetic_sc_data_with_cell_types(): Generates synthetic data for single cell
rs_tied_diffusion_parallel(): Calculate massively parallelised tied diffusion scores
rs_tom(): Calculates the TOM over an affinity matrix
rs_top_k_targets(): SCENIC: Select the Top TF <> Gene pairs
rs_upper_triangle_to_dense(): Reconstruct a matrix from a flattened upper triangle vector
rs_upper_triangle_to_sparse(): Generate sparse data from an upper triangle
rs_vision(): Calculate VISION pathway scores in Rust
rs_vision_with_autocorrelation(): Calculate VISION pathway scores in Rust with auto-correlation
rs_2d_loess(): Rust implementation of a Loess function