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.LdaResult() get_params.LdaKSweepResult() get_params.Hotspot() get_params.miloR() get_params.ScenicGrn() get_params.NmfResult() get_params.StabilisedNmfResult() get_params.ConsensusNmfResult() get_params.DialogueResult() get_params.ScNebula(): Get the parameters that were used.
get_results() get_results.DialogueResult(): 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, blitzGSEA.

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_blitzgsea(): Bixverse implementation of the blitzGSEA algorithm
blitzgsea_calibrate(): Calibrate the blitzGSEA null model for a signature
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
calc_singscore(): Bixverse implementation of singscore (single gene set)
calc_singscore_multi(): Bixverse implementation of singscore (multiple gene sets)
calc_singscore_rank(): Rank an expression matrix for singscore
params_blitzgsea(): Wrapper function to generate blitzGSEA parameters
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

Co-expression methods for bulk RNAseq

Methods to identify co-expression modules via correlations or matrix factorisations.

BulkCoExp(): Bulk RNAseq co-expression modules
preprocess_bulk_coexp(): Process the raw data
get_diagnostics(): Get diagnostics from a BulkModuleResult
get_factors(): Get factor matrices from a BulkModuleResult
get_nmf_gene_loadings(): Get the NMF gene loadings
get_nmf_modules(): Get the NMF module membership data.table
get_nmf_sample_activity(): Get the NMF sample activity
get_nmf_stability(): Get the multi-run NMF diagnostics
get_c_pca_factors(): Get the contrastive PCA factors
get_c_pca_loadings(): Get the contrastive PCA loadings
get_ica_stability_res(): Get the ICA component data (stability, convergence, nMI)
get_grid_search_res(): Get the grid search results
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
get_modules(): Get the module membership from a BulkModuleResult
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
contrastive_pca_processing(): Prepare class for contrastive PCA
c_pca_plot_alphas(): Plot various alphas for the contrastive PCA
contrastive_pca(): Apply contrastive PCA.
dgrdl_grid_search(): Grid search over DGRDL parameters
dgrdl_result(): Run DGRDL with the specified parameters
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
nmf_bulk(): Run non-negative matrix factorisation on a BulkCoExp
stabilised_nmf_bulk(): Run stabilised (multi-restart) NMF on a BulkCoExp
consensus_nmf_bulk(): Run consensus NMF on a BulkCoExp
nmf_k_sweep_bulk(): Sweep k for consensus NMF on a BulkCoExp
modules_from_loadings(): Derive sparse module membership from a loading matrix
params_cor_graph(): Wrapper function for graph generation
params_coremo(): Wrapper function to generate CoReMo parameters
params_dgrdl(): Wrapper function to generate DGRDL parameters
params_module_membership(): Wrapper function to generate module membership 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
run_edger_ql(): Run the edgeR quasi-likelihood workflow
pseudobulk_dge_sc(): Run the edgeR quasi-likelihood workflow on pseudo-bulked single cells
params_edger_ql(): Wrapper function for parameters for the edgeR quasi-likelihood workflow
params_limma_voom(): Wrapper function for parameters for the limma-voom workflow
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() deprecated: 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 class and getters

THE single cell class with a large number of getters.

SingleCells(): bixverse SingleCells class
SingleCellCountData $.SingleCellCountData experimental: Single cell count data handler
add_sc_new_obs(): Add an obs table derived from a method to the SingleCells.
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
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
reset_cells_to_keep(): Reset the cells to keep
get_sc_cache_status(): Status of everything held in a single cell object's caches
check_sc_state(): Check that cached artefacts still match the object's state
assert_sc_state(): Assert that cached artefacts still match the object's state
get_embedding(): Get the embedding
get_gene_indices(): Get the index position for a gene
get_gene_names(): Get the gene names
get_cell_info(): Get the cell idx (R-based) and cell names
get_hvg(): Get the HVG
get_knn_mat(): Get the KNN matrix
get_knn_obj(): Get the KNN object
get_magic(): Get the MAGIC imputed layer
set_magic(): Set/add the MAGIC imputed layer
remove_magic(): Remove the MAGIC imputed layer
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
drop_cols_sc(): Drop columns from the obs or var table

Single cell subsets and pipelines

Sub-clustering a group of cells and running the same chain per group.

SingleCellsSubset(): bixverse single cell subset class
merge_subset_obs(): Merge obs columns from subsets back into the parent object
sc_pipeline(): Construct an empty single cell pipeline
`%>>%`: Append a step to a pipeline
apply_pipeline(): Apply a pipeline to a single cell object
apply_pipeline_per_group(): Apply a pipeline independently to each group of a SingleCells object
validate_pipeline(): Check that a pipeline can run on a given class
meta_cells_per_group(): Generate source-pure meta cells and merge them
step_hvg_sc(): Pipeline step: identify highly variable genes
step_pca_sc(): Pipeline step: PCA
step_neighbours_sc(): Pipeline step: nearest neighbours
step_clusters_sc(): Pipeline step: graph-based clustering
step_bbknn_sc(): Pipeline step: BBKNN batch correction
step_fast_mnn_sc(): Pipeline step: fastMNN batch correction
step_harmony_sc(): Pipeline step: Harmony batch correction
step_harmony_v2_sc(): Pipeline step: Harmony v2 batch correction
step_metacells_sc(): Pipeline step: generate meta cells

This version allows you to also work ADT counts

SingleCellsMultiModal(): bixverse SingleCells (multi modal) class
new_adt_counts_clr(): Generates a new ADTCounts class
new_adt_counts_dsb(): Generates a new ADTCounts class via DSB normalisation
add_adt_counts_sc(): Add ADT counts to SingleCellsMultiModal
detect_adt_isotypes(): Detect likely isotype-control features by name pattern
get_adt_feature_info(): Get the ADT feature info
get_adt_names(): Get the ADT feature names
get_adt_sample_info(): Get the ADT sample info
params_sc_dsb(): Default parameters for DSB ADT normalisation
read_multi_tenx_h5_adt(): Read in 10x h5 ADT data from multiple files
read_tenx_h5_adt(): Read in 10x h5 ADT data
remove_adt_isotypes(): Return the ADT feature names removing the isotypes

Generating meta cells.

MetaCells(): bixverse meta cell class
calc_diffusion_coordinates(): Calculate diffusion coordinates
calc_manifold_metrics(): Calculate manifold metrics
calc_meta_cell_purity(): Calculate meta cell purity
get_meta_cell_purity(): Calculate meta cell purity without mutating object state
generate_supercells_sc(): Generate SuperCells and return a MetaCells object
generate_bt_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
merge_meta_cells(): Merge meta cell objects into one
nebula_mc(): Run NEBULA on meta cells
params_sc_supercell(): Wrapper function for parameters for SuperCell generation
params_sc_bt_metacells(): Wrapper function for parameters for bootstrapped 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 storage format
prescan_h5ad_files(): Pre-scan multiple h5ad files for multi-sample loading
prescan_mtx_dirs(): Prescan multiple mtx directories for a multi-load
prescan_tenx_h5_files(): Pre-scan multiple 10x CellRanger h5 files for multi-sample loading
read_tenx_h5_metadata(): Read barcode and feature tables and metadata from a 10x h5 file
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_mtx(): Load multiple mtx directories into a single SingleCells
load_multi_h5ad(): Load multiple h5ad files into a single SingleCells
stream_h5ad(): Stream in h5ad to SingleCells (alias)
load_r_data(): Load in data directly from R objects.
load_seurat(): Load in Seurat to SingleCells
load_sce(): Load in data from a SingleCellExperiment
load_tenx_h5(): Load in a 10x CellRanger h5 file to SingleCells
load_multi_tenx_h5(): Load multiple 10x CellRanger h5 files into a single 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
merge_sc_experiments(): Merge multiple SingleCells experiments into one
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(): Manually readjust Scrublet doublet call thresholds
doublet_detection_boost_sc(): Doublet detection with boosted doublet classification
scdblfinder_sc(): Run scDblFinder doublet detection on a SingleCells object
gene_set_proportions_sc(): Calculate the proportions of reads for specific gene sets
cellsweep_sc(): Remove ambient and bulk contamination with CellSweep
per_cell_qc_outlier(): Use MAD outlier detection on per-cell QC metrics
run_cell_qc(): Run outlier detection on per-cell QC metrics
run_cell_qc_fixed(): Fixed-threshold cell QC
rescue_cells(): Rescue MAD-flagged cells that fall within safe bounds
flag_cells(): Add hard-threshold flags to a CellQc object
find_hvg_sc(): Identify HVGs
find_hvg_batch_aware_sc(): Identify HVGs (batch aware)
get_hvg_data_sc(): Identify HVGs without mutating object state
calculate_pca_sc(): Run PCA for single cell
fit_residuals_sc(): Fit a residual model for single cell data
sct_corrected_counts_sc(): Write scTransform-corrected counts to a new store
get_residual_fit(): Get the fitted residual model
set_residual_fit(): Set/add the fitted residual model
remove_residual_fit(): Remove the fitted residual model
generate_sc_knn(): Generate a new SingleCellNearestNeighbour from data
find_neighbours_sc(): Find the neighbours for single cell.
run_magic_sc(): Impute a subset of genes with MAGIC
top_genes_perc_sc(): Calculate the proportions of reads for the Top N genes
params_sc_magic(): Wrapper function for MAGIC imputation parameters
params_sc_cellsweep(): Default parameters for CellSweep denoising
params_sc_empty_droplets(): Parameters for identifying empty droplets
params_norm_doublets_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_sc_pca(): Wrapper for PCA specifically designed for single cells
params_sc_sctransform(): Wrapper function for scTransform (v2) parameters
params_sc_apr(): Wrapper function for analytic Pearson residual parameters
params_scrublet(): Wrapper function for Scrublet doublet detection parameters
params_sc_fast_cluster(): Fast single cell clustering parameters
params_sc_neighbours(): Wrapper function for parameters for neighbour identification in single cell
params_scdblfinder(): Wrapper function for scDblFinder doublet detection parameters
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
params_sc_knn(): Parameters for single cell kNN searches
params_kmeans_defaults(): K-mean parameter defaults.
params_fast_cluster_default(): Helper function to generate default parameters for the fast clustering for the doublet detection methods

Single cell batch correction methods

Batch correction methods and metrics for single cell

fast_mnn_sc(): Run fastMNN
harmony_sc(): Run Harmony
harmony_v2_sc(): Run Harmony v2
bbknn_sc(): Run BBKNN
seurat_cca_sc(): Run Seurat CCA integration
seurat_rpca_sc(): Run Seurat rPCA integration
calculate_kbet_sc(): Calculate kBET scores
calculate_batch_asw_sc(): Calculate batch average silhouette width
calculate_lisi_sc(): Calculate LISI scores (iLISI or cLISI)
calculate_pcr_sc(): Calculate the principal component regression on batch
calculate_cell_type_asw_sc(): Calculate cell type average silhouette width
calculate_graph_connectivity_sc(): Calculate the graph connectivity per cell type
calculate_integration_metrics_sc(): Calculate a summary of integration metrics
params_sc_fastmnn(): Wrapper function for the fastMNN parameters
params_sc_harmony(): Default parameters for Harmony batch correction
params_sc_harmony_v2(): Default parameters for Harmony v2 batch correction
params_sc_bbknn(): Wrapper function for the BBKNN parameters
params_sc_seurat_cca(): Wrapper function for the Seurat CCA parameters
params_sc_seurat_rpca(): Wrapper function for the Seurat rPCA parameters

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
fast_cluster_sc(): Run fast Louvain clustering on a SingleCells object
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
find_specific_markers_sc(): Find markers that are specific to a cell group
get_pseudobulked_sc(): Generate pseudo-bulked matrices
generate_knn_sc(): Generate a SingleCellNearestNeighbour from a single cell class
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
meld_sc(): Run MELD signal smoothing for differential abundance estimation
nebula_sc(): Run NEBULA on single cells
run_palantir_sc(): Run Palantir trajectory inference
run_paga_sc(): Run PAGA graph abstraction
run_gene_trends_sc(): Fit gene trends over Palantir pseudotime
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
binarise_regulon_activity(): Binarise regulon activity into on/off calls
build_regulons(): Build the final regulons
nmf_sc(): Run single-run NMF on single cell or meta cell data
stabilised_nmf_sc(): Run stabilised (multi-run) NMF on single cell or meta cell data
consensus_nmf_sc(): Run consensus NMF on single cell or meta cell data
nmf_k_sweep_sc(): Sweep k for consensus NMF on single cell or meta cell data
run_lda(): Fit a latent Dirichlet allocation model
lda_k_sweep(): Sweep the topic count for LDA
dialogue_sc(): Find multicellular programmes with DIALOGUE
get_best_run(): Get the best run from a stabilised NMF result
get_best_model(): Get the selected model from an LDA topic count sweep
get_stability(): Get the consensus NMF stability diagnostics
get_top_terms(): Get the highest-probability terms per topic
get_w(): Get the W (gene loadings) matrix
get_h(): Get the H (cell activations) matrix
plot(<NmfKSweepResult>): Plot a consensus NMF k sweep
plot(<LdaKSweepResult>): Plot the LDA topic count sweep
params_sc_aucell(): Wrapper function for parameters for AUCell
params_sc_hotspot(): Wrapper function for parameters for HotSpot
params_sc_miloR(): Wrapper function for parameters for MiloR
params_nebula(): Wrapper function for parameters for NEBULA
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
params_scenic_binarise(): Wrapper function for parameters for the SCENIC binarisation
params_meld(): Constructor for MELD parameters
params_sc_palantir(): Wrapper function for Palantir parameters
params_sc_branch_selection(): Wrapper function for the branch cell selection parameters
params_sc_gene_trends(): Wrapper function for gene trend parameters
params_nmf_hals(): Wrapper function for NMF (HALS) parameters
params_nmf_consensus(): Wrapper function for consensus NMF parameters
params_lda(): Wrapper function for the LDA parameters
params_dialogue_pmd(): Wrapper function for the DIALOGUE decomposition parameters
params_dialogue_hlm(): Wrapper function for the DIALOGUE mixed model parameters
params_dialogue_refine(): Wrapper function for the DIALOGUE refinement parameters

Methods to analyse multi-modal single cell data

calculate_pca_adt_sc(): Calculate the PCA on top of the normalised ADT counts
generate_wnn_graph_sc(): Generate a weighted nearest neighbour (WNN) graph
params_sc_wnn(): Wrapper function for WNN parameters

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

calc_knn_metrics(): Calculate recall at k and distance ratio
get_centroids_sc(): Get k-means centroids from a fast cluster result
get_feature_mat(): Get the feature matrix used for the classifier
get_kmeans_clusters(): Get k-means cluster assignments from a fast cluster result
get_knn_dist(): Get the KNN distance
get_marker_summary(): Get the per-gene marker summaries across all rivals
get_marker_comparisons(): Get the per-rival marker statistics
get_data(): Get the ready obs data from various sub method
get_scores(): Get scores
new_sc_knn(): Helper function to generate kNN data with distances

Reference mapping and cell type annotations in single cell

Helpers to do reference mapping and cell type identification in single cell

SymphonyReference(): bixverse SymphonyReference class
add_symphony_labels(): Add labels to a Symphony reference post-hoc
build_symphony_ref(): Build a Symphony reference from a SingleCells object
transfer_labels_symphony(): Transfer labels from a Symphony reference to a query via kNN majority vote
get_symphony_hvg_names(): Getter for the HVG gene names of a Symphony reference
get_symphony_labels(): Getter for the stored labels of a Symphony reference
get_symphony_loadings(): Getter for the PCA loadings of a Symphony reference
get_symphony_z_corr(): Getter for the corrected embedding of a Symphony reference
map_symphony_query(): Map a SingleCells query onto a Symphony reference
prepare_cell_markers(): Helper function to prepare cell markers
calc_sc_type_scores(): Calculate ScType scores per cell
score_clusters(): Score clusters based on ScType
assign_sc_type(): Assign cell types per cell based on ScType
params_sctype_cells(): Parameters for the per-cell ScType assignment
params_symphony_map(): Default parameters for Symphony query mapping

Single cell ligand receptor

Classes, functions and generics/methods for ligand receptor analysis for single cell.

compute_expression_info_sc(): Compute per-cluster mean expression and expressing fraction for a gene set
generate_ligand_target_influence(): Generate the ligand to target influence matrix
get_influence(): Get the ligand-target influence matrix
ligand_activity_scores(): Compute ligand activity scores against gene sets
params_ligand_target(): Parameters for ligand to target influence computation
prioritise_interactions(): Prioritise sender-ligand-receiver-receptor interactions

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/MetaCells object
tsne_sc(): Run t-SNE on a SingleCells/MetaCells object
phate_sc(): Run PHATE on a SingleCells/MetaCells object
extract_dot_plot_data(): Extract grouped gene statistics for dot plots
extract_gene_expression(): Extract normalised gene expression for plotting
extract_embedding_data(): Extract embedding coordinates for plotting
extract_feature_pair(): Extract a pair of features for scatter / hex plots
extract_feature_plot_data(): Extract per-cell expression mapped onto an embedding
extract_gene_violin_data(): Extract per-cell expression grouped for violin plots
extract_paga_plot_data(): Extract the PAGA graph positioned on an embedding

Statistical functions

Any types of functions that help with statistics

calculate_effect_size(): Calculate the Hedge's 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 normalised 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_cd34_data(): Download the CD34 example data from SEACells
download_dialogue_uc(): Download the ulcerative colitis example data for DIALOGUE
download_kang_pbmc(): Download the Kang, et al. IFN-beta stimulated PBMC data
download_thymus_ageing(): Download the Baran-Gale, et al. ageing thymus data
download_marrow_cd34(): Download the marrow CD34 example data from Palantir
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
download_pbmc_totalseq_data(): Download the PBMC TotalSeq data with ADT counts
download_pbmc8k(): Download PBMC8K data from Zenodo
download_pbmc_1k_5p(): Download the raw PBMC 1k 5' matrix from 10x Genomics
calculate_sparsity_stats(): Helper function to calculate the induced sparsity
demo_single_cells(): Ready-made SingleCells object for examples and tests
generate_gene_module_data(): Generates synthetic gene module data.
generate_single_cell_test_data(): Single cell test data
generate_dialogue_test_data(): Single cell test data with a planted multicellular programme
generate_cellsweep_test_data(): Single cell test data with a planted ambient profile
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
write_h5ad_sc_dense(): Helper function to write data to a dense h5ad file
params_sc_synthetic_data(): Default parameters for generation of synthetic single cell data (RNA)
params_sc_synthetic_data_adt(): Default parameters for generation of synthetic single cell data (ADT)
params_sc_synthetic_dialogue(): Default parameters for generation of synthetic DIALOGUE data
params_sc_synthetic_cellsweep(): Default parameters for generation of synthetic CellSweep data
params_synthetic_bulk_rnaseq(): Wrapper function to generate synthetic bulk RNAseq parameters
params_bulk_sparsity(): Wrapper function to generate bulk sparsification parameters
synthetic_signal_matrix(): Generates a simple synthetic, pseudo gene expression matrix
simulate_dropouts(): Simulate sequencing-depth dropouts 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
find_threshold_otsu(): Find a threshold via the Otsu method
install_agent_skill(): Install the bixverse agent skill
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! The ones exposed here are general enough to be useful in other packages. There is a lot more under the hood…

rs_2d_loess() experimental: Rust implementation of a Loess function
rs_batch_silhouette_width() experimental: Calculate batch silhouette width from an embedding
rs_blitzgsea_calibrate() experimental: Calibrate the blitzGSEA gamma null for a signature
rs_blitzgsea_score() experimental: Score gene sets against a calibrated blitzGSEA null
rs_calc_norm_factors() experimental: Calculate normalisation factors
rs_cell_type_asw() experimental: Calculate cell type silhouette width from an embedding
rs_cistarget() experimental: Run CisTarget motif enrichment analysis
rs_compare_knn() experimental: Helper to compare kNN graphs
rs_constrained_page_rank() experimental: Calculate a constrained page-rank score
rs_constrained_page_rank_list() experimental: Calculate a constrained page-rank score over a list.
rs_contrastive_pca() experimental: Calculate the contrastive PCA
rs_cor() experimental: Calculate the column wise correlations.
rs_cor2() experimental: Calculate the column wise correlations.
rs_cor_upper_triangle() experimental: Calculate the column wise correlations and returns the upper triangle
rs_cos() experimental: Calculate the column wise cosine similarities
rs_count_zeroes() experimental: Helper to get zero stats from a given matrix
rs_cov2cor() experimental: Calculates the correlation matrix from the co-variance matrix
rs_covariance() experimental: Calculate the column-wise co-variance.
rs_cpm() experimental: Counts per million
rs_dense_to_upper_triangle() experimental: Generate a vector-based representation of the upper triangle of a matrix
rs_differential_cor() experimental: Calculate the column wise differential correlation between two sets of data.
rs_dist() experimental: Calculate the pairwise column distance in a matrix
rs_fast_auc() experimental: Fast AUC calculation
rs_fast_ica() experimental: Run the Rust implementation of fast ICA.
rs_fdr_adjustment() experimental: Calculate a BH-based FDR
rs_filter_by_expr() experimental: Filter lowly expressed genes
rs_gene_trends() experimental: Fit Palantir gene trends over pseudotime
rs_geom_elim_fgsea_simple() experimental: Run fgsea simple method for gene ontology with elimination method
rs_gower_dist() experimental: Calculates the Gower distance for a given matrix
rs_graph_connectivity() experimental: Calculate graph connectivity per cell type
rs_gse_geom_elim() experimental: Run hypergeometric enrichment over the gene ontology
rs_gse_geom_elim_list() experimental: Run hypergeometric enrichment a list of target genes over the gene ontology
rs_gsva() experimental: Rust version of the GSVA algorithm
rs_h5ad_data() experimental: Load in h5ad data via Rust
rs_hamming_dist() experimental: Calculates the Hamming distance between categorical columns
rs_harmony() experimental: Harmony batch correction in Rust
rs_harmony_v2() experimental: Harmony batch correction in Rust (version 2)
rs_hedges_g() experimental: Calculate the Hedge's G effect
rs_hypergeom_test() experimental: Run a single hypergeometric test.
rs_hypergeom_test_list() experimental: Run a hypergeometric test over a list of target genes
rs_ica_iters() experimental: Run ICA over a given no_comp with random initialisations of w_init
rs_ica_iters_cv() experimental: Run ICA with cross-validation and random initialisation
rs_jaccard_row_integers() experimental: Calculate rapidly Jaccard similarities between rows
rs_kbet() experimental: Calculate kBET type scores
rs_knn_label_propagation() experimental: kNN label propagation
rs_knn_mat_to_edge_list() experimental: Flatten kNN matrix to edge list
rs_generate_ligand_target_influence() experimental: Generate the ligand to target influence matrices
rs_ligand_activity_scores() experimental: Calculate the NicheNet ligand activity scores
rs_limma_voom() experimental: Run the limma linear model chain on a count matrix
rs_lisi() experimental: Calculate LISI scores on any label
rs_mad_outlier() experimental: Calculate MAD outlier detection in Rust.
rs_edger_ql() experimental: Run the edgeR quasi-likelihood chain on a count matrix
rs_mc_aucell() experimental: Calculate AUCell in Rust (for meta cells)
rs_mc_dialogue() experimental: Run DIALOGUE over meta cells
rs_mc_hotspot_autocor() experimental: Calculate gene spatial auto-correlations (for meta cells)
rs_mc_hotspot_gene_cor() experimental: Calculate gene to gene spatial correlations (for meta cells)
rs_mc_fit_residuals() experimental: Fits a residual model for meta cells
rs_mc_hvg() experimental: Meta cells highly variable genes
rs_mc_pca() experimental: PCA on MetaCells (sparse data)
rs_mc_pca_residuals() experimental: Calculates PCA on Pearson residuals for meta cells
rs_mc_residual_variance() experimental: Residual variance and variable features for meta cells
rs_mc_scenic() experimental: SCENIC on MetaCells
rs_mc_vision() experimental: Calculate VISION pathway scores in Rust (for meta cells)
rs_mc_vision_with_autocorrelation() experimental: Calculate VISION pathway scores with auto-correlation (for meta cells)
rs_mitch_calc() experimental: Calculate mitch enrichment leveraging Rust under the hood
rs_nebula_mc() experimental: Fit the NEBULA negative binomial gamma mixed model over meta cells
rs_mutual_info() experimental: Calculates the mutual information matrix
rs_onto_semantic_sim() experimental: Calculate the semantic similarity in an ontology
rs_onto_semantic_sim_mat() experimental: Calculate the semantic similarity in an ontology
rs_onto_sim_wang() experimental: Calculate the Wang similarity for specific terms
rs_onto_sim_wang_mat() experimental: Calculate the Wang similarity matrix for an ontology
rs_ot_harmonic_sum() experimental: Calculate the OT harmonic sum
rs_page_rank() experimental: Rust version of calculating the personalised page rank
rs_page_rank_parallel() experimental: Calculate massively parallelised personalised page rank scores
rs_pcr() experimental: Principal component regression on batch
rs_phyper() experimental: Calculate the hypergeometric test in Rust
rs_pointwise_mutual_info() experimental: Calculates the point wise mutual information
rs_prcomp() experimental: Rust implementation of prcomp
rs_prepare_whitening() experimental: Prepare the data for whitening
rs_random_svd() experimental: Run randomised SVD over a matrix
rs_range_norm() experimental: Apply a range normalisation on a vector.
rs_rank_matrix_col() experimental: Gene rank matrix
rs_rank_matrix_col_stable() experimental: Stable-gene rank matrix
rs_rbf_function() experimental: Apply a Radial Basis Function
rs_rbf_function_mat() experimental: Apply a Radial Basis Function (to a matrix)
rs_rbh_cor() experimental: Generate reciprocal best hits based on correlations
rs_rbh_sets() experimental: Generate reciprocal best hits based on set similarities
rs_remove_batch_effect() experimental: Remove batch effects from a log-expression matrix
rs_sc_knn() experimental: Generates the kNN graph
rs_sc_knn_w_dist() experimental: Generates the kNN graph with additional distances
rs_sc_snn() experimental: Generates the sNN graph for igraph
rs_set_similarity() experimental: Set similarities
rs_set_similarity_list() experimental: Set similarities over one list
rs_set_similarity_list2() experimental: Set similarities over two list
rs_singscore_multi() experimental: Rust version of singscore for many gene sets
rs_singscore_permutation_test() experimental: Rust version of the singscore permutation test
rs_singscore_single() experimental: Rust version of singscore for a single gene set
rs_snf() experimental: Similarity network fusion
rs_spatial_fdr() experimental: Weighted Benjamini-Hochberg over overlapping neighbourhoods
rs_sparse_dict_dgrdl() experimental: Generate a sparse dictionary with DGRDL
rs_sparse_dict_dgrdl_grid_search() experimental: Generate a sparse dictionary with DGRDL
rs_spectral_clustering() experimental: Rust implementation of spectral clustering
rs_spectral_clustering_sim() experimental: Rust implementation of spectral clustering
rs_ssgsea() experimental: Rust version of the ssGSEA algorithm
rs_tied_diffusion_parallel() experimental: Calculate massively parallelised tied diffusion scores
rs_tom() experimental: Calculates the TOM over an affinity matrix
rs_upper_triangle_to_dense() experimental: Reconstruct a matrix from a flattened upper triangle vector
rs_upper_triangle_to_sparse() experimental: Generate sparse data from an upper triangle
rs_voom_normalise() experimental: Voom-transform a count matrix
rs_wnn() experimental: Run the weighted nearest neighbour algorithm