| Append a step to a pipeline | %>>% |
| Add ADT counts to 'SingleCellsMultiModal' | add_adt_counts_sc |
| Replace the meta data | add_new_metadata |
| Add neighbourhood info on majority cell type | add_nhoods_info add_nhoods_info.miloR |
| Add an obs table derived from a method to the SingleCells. | add_sc_new_obs |
| Add a data modality for SNF generation | add_snf_data_modality |
| Add labels to a Symphony reference post-hoc | add_symphony_labels |
| Class for Anndata | AnnDataParser |
| Apply a pipeline to a single cell object | apply_pipeline |
| Apply a pipeline independently to each group of a 'SingleCells' object | apply_pipeline_per_group |
| Archive the on-disk counts for cold storage | archive_sc_exp |
| Wrap an existing ligand-target matrix as a 'LigandTargetInfluence' | as_ligand_target_influence |
| Assert that cached artefacts still match the object's state | assert_sc_state |
| Assign cell types per cell based on ScType | assign_sc_type |
| Calculate AUC scores (akin to AUCell) | aucell_sc |
| Run a linear batch correction | batch_correction_bulk_dge |
| Run BBKNN | bbknn_sc |
| Binarise regulon activity into on/off calls | binarise_regulon_activity |
| Calibrate the blitzGSEA null model for a signature | blitzgsea_calibrate |
| Build a Bonsai tree over the cells | bonsai_sc |
| Build the final regulons | build_regulons build_regulons.ScenicGrn |
| Build a Symphony reference from a SingleCells object | build_symphony_ref |
| Wrapper function to generate BulkDge object from h5ad | bulk_dge_from_h5ad |
| Bulk RNAseq co-expression modules | BulkCoExp |
| Bulk RNAseq differential gene expression class | BulkDge |
| Plot various alphas for the contrastive PCA | c_pca_plot_alphas |
| Bixverse implementation of the blitzGSEA algorithm | calc_blitzgsea |
| Calculate diffusion coordinates | calc_diffusion_coordinates |
| Bixverse implementation of the fgsea algorithm | calc_fgsea |
| Bixverse implementation of the simple fgsea algorithm | calc_fgsea_simple |
| Bixverse implementation of the traditional GSEA algorithm | calc_gsea_traditional |
| Bixverse implementation of GSVA | calc_gsva |
| Calculate recall at k and distance ratio | calc_knn_metrics |
| ULM / MLM activity scores (decoupler-style) | calc_lm_activity |
| Calculate manifold metrics | calc_manifold_metrics |
| Meta cell compactness in a diffusion map | calc_meta_cell_compactness |
| Calculate meta cell purity | calc_meta_cell_purity |
| Meta cell separation in a diffusion map | calc_meta_cell_separation |
| Calculate a mitch gene set enrichments on contrast | calc_mitch |
| Calculate ScType scores per cell | calc_sc_type_scores |
| Bixverse implementation of singscore (single gene set) | calc_singscore |
| Bixverse implementation of singscore (multiple gene sets) | calc_singscore_multi |
| Rank an expression matrix for singscore | calc_singscore_rank |
| Bixverse implementation of ssGSEA | calc_ssgsea |
| Calculate all possible DGE variants (DEPRECATED!) | calculate_all_dges |
| Calculate batch average silhouette width | calculate_batch_asw_sc |
| Calculate cell type average silhouette width | calculate_cell_type_asw_sc |
| Calculates the critical value | calculate_critical_value |
| Calculates the Hedge's G effect size | calculate_dge_hedges |
| Calculates the Limma Voom DGE | calculate_dge_limma |
| Calculate the AUROC for a diffusion score | calculate_diffusion_auc |
| Calculate the Hedge's G effect between two matrices | calculate_effect_size |
| Calculate the graph connectivity per cell type | calculate_graph_connectivity_sc |
| Calculate the information content for each ontology term | calculate_information_content |
| Calculate a summary of integration metrics | calculate_integration_metrics_sc |
| Calculate kBET scores | calculate_kbet_sc |
| Calculate LISI scores (iLISI or cLISI) | calculate_lisi_sc |
| Calculate the PCA on top of the normalised ADT counts | calculate_pca_adt_sc |
| Calculate PCA on the expression. | calculate_pca_bulk_dge |
| Run PCA for single cell | calculate_pca_sc |
| Calculate the principal component regression on batch | calculate_pcr_sc |
| RPKM calculation | calculate_rpkm |
| Calculate the Resnik or Lin semantic similarity | calculate_semantic_sim |
| Calculate the Resnik or Lin semantic similarity matrix | calculate_semantic_sim_mat |
| Calculate the Resnik or Lin semantic similarity for an ontology. | calculate_semantic_sim_onto |
| Helper function to calculate the induced sparsity | calculate_sparsity_stats |
| Calculate the TOM from a correlation matrix | calculate_tom |
| Calculate the TOM from an expression matrix | calculate_tom_from_exp |
| TPM calculation | calculate_tpm |
| Calculate the Wang similarities between terms | calculate_wang_sim |
| Calculate the Wang similarity matrix | calculate_wang_sim_mat |
| Calculate the Wang similarity for an ontology. | calculate_wang_sim_onto |
| Manually readjust Scrublet doublet call thresholds | call_doublets_manual |
| Cell cycle genes | cell_cycle_genes |
| CellPhoneDB ligand-receptor analysis | cellphonedb_sc |
| Remove ambient and bulk contamination with CellSweep | cellsweep_sc |
| Change the primary gene identifier of BulkDge | change_gene_identifier |
| Check that cached artefacts still match the object's state | check_sc_state |
| Identify privileged communities based on a given diffusion vector | community_detection |
| Compute per-cluster mean expression and expressing fraction for a gene set | compute_expression_info_sc |
| Run consensus NMF on a BulkCoExp | consensus_nmf_bulk |
| Run consensus NMF on single cell or meta cell data | consensus_nmf_sc |
| Constrained personalised page rank | constrained_page_rank |
| Constrained personalised page rank over a list | constrained_page_rank_ls |
| Apply contrastive PCA. | contrastive_pca |
| Prepare class for contrastive PCA | contrastive_pca_processing |
| Iterate through different epsilon parameters | cor_module_check_epsilon |
| Generates CoReMo-based gene modules | cor_module_coremo_clustering |
| Split CoReMo modules by correlation sign | cor_module_coremo_cor_sign |
| Calculate Eigengenes for CoReMo modules | cor_module_coremo_eigengene |
| Assesses CoReMo-based gene module stability | cor_module_coremo_stability |
| Iterate through Leiden resolutions for graph-based community detection. | cor_module_graph_check_res |
| Identify correlation-based gene modules via graphs | cor_module_graph_final_modules |
| Prepare correlation-based module detection | cor_module_processing |
| Update the correlation matrix to a TOM | cor_module_tom |
| Ready-made 'SingleCells' object for examples and tests | demo_single_cells |
| Run densMAP on a SingleCells/MetaCells object | densmap_sc |
| Run den-SNE on a SingleCells/MetaCells object | densne_sc |
| Detect likely isotype-control features by name pattern | detect_adt_isotypes |
| Grid search over DGRDL parameters | dgrdl_grid_search |
| Run DGRDL with the specified parameters | dgrdl_result |
| Find multicellular programmes with DIALOGUE | dialogue_sc |
| Prepare differential correlation-based module detection | diffcor_module_processing |
| Diffuse seed genes over a network | diffuse_seed_nodes |
| Doublet detection with boosted doublet classification | doublet_detection_boost_sc |
| Download the CD34 example data from SEACells | download_cd34_data |
| Download CisTarget reference files for human (hg38) | download_cistarget_hg38 |
| Download PBMCs with demuxlet doublet information | download_demuxlet_pbmc |
| Download the ulcerative colitis example data for DIALOGUE | download_dialogue_uc |
| Download the Kang, et al. IFN-beta stimulated PBMC data | download_kang_pbmc |
| Download the marrow CD34 example data from Palantir | download_marrow_cd34 |
| Download the raw PBMC 1k 5' matrix from 10x Genomics | download_pbmc_1k_5p |
| Download two different PBMC data sets for batch correction testing | download_pbmc_batches |
| Download the PBMC TotalSeq data with ADT counts | download_pbmc_totalseq_data |
| Download PBMC3K data from Zenodo | download_pbmc3k |
| Download PBMC8K data from Zenodo | download_pbmc8k |
| Download the Baran-Gale, et al. ageing thymus data | download_thymus_ageing |
| Drop columns from the obs or var table | drop_cols_sc |
| Extract plot-ready data for a binary heatmap | extract_binary_heatmap_data |
| Extract grouped gene statistics for dot plots | extract_dot_plot_data |
| Extract embedding coordinates for plotting | extract_embedding_data |
| Extract a pair of features for scatter / hex plots | extract_feature_pair |
| Extract per-cell expression mapped onto an embedding | extract_feature_plot_data |
| Extract normalised gene expression for plotting | extract_gene_expression |
| Extract per-cell expression grouped for violin plots | extract_gene_violin_data |
| Extract the miloR neighbourhood graph positioned on an embedding | extract_milo_plot_data |
| Extract the PAGA graph positioned on an embedding | extract_paga_plot_data |
| F1 scores on top of a confusion matrix | f1_score_confusion_mat |
| Run fast clustering on a SingleCells object | fast_cluster_sc |
| Fast ICA via Rust | fast_ica_rust |
| Fast ICA via Rust from processed data | fast_ica_rust_helper |
| Run fastMNN | fast_mnn_sc |
| Run GO enrichment with elimination method over a continuous vectors | fgsea_go_elim |
| Run GO enrichment with elimination with fgsea simple | fgsea_simple_go_elim |
| Filter the calculated similarities | filter_similarities |
| Find all markers | find_all_markers_sc |
| Graph-based clustering of cells on the neighbour graph | find_clusters_sc |
| Identify HVGs (batch aware) | find_hvg_batch_aware_sc |
| Identify HVGs | find_hvg_sc |
| Calculate DGE between two cell groups | find_markers_sc |
| Find the neighbours for single cell. | find_neighbours_sc |
| Find RBH communities | find_rbh_communities |
| Find markers that are specific to a cell group | find_specific_markers_sc |
| Find a threshold via the Otsu method | find_threshold_otsu |
| Fit a residual model for single cell data | fit_residuals_sc |
| Helper to fix meta-data columns to be R conform | fix_meta_data_column |
| Add hard-threshold flags to a CellQc object | flag_cells |
| Run ForceAtlas2 on a SingleCells/MetaCells object | forceatlas2_sc |
| Calculate the proportions of reads for specific gene sets | gene_set_proportions_sc |
| Gene Ontology data | GeneOntologyElim |
| Generate meta cells based on hdWGCNA and return a 'MetaCells' object | generate_bt_meta_cells_sc |
| Single cell test data with a planted ambient profile | generate_cellsweep_test_data |
| Single cell test data with a planted multicellular programme | generate_dialogue_test_data |
| Generates synthetic gene module data. | generate_gene_module_data |
| Identify hotspot gene clusters | generate_hotspot_membership generate_hotspot_membership.Hotspot |
| Generate a 'SingleCellNearestNeighbour' from a single cell class | generate_knn_sc |
| Generate the ligand to target influence matrix | generate_ligand_target_influence |
| Generate meta cells via MetaCells2 (divide and conquer) | generate_metacells2_sc |
| Helper function to create personalisation vectors | generate_personalisation_vec |
| Generate an RBH graph. | generate_rbh_graph |
| Generate a new SingleCellNearestNeighbour from data | generate_sc_knn |
| Generate meta cells based on SEACells and return a 'MetaCells' object | generate_seacells_sc |
| Single cell test data | generate_single_cell_test_data |
| Generate SuperCells and return a 'MetaCells' object | generate_supercells_sc |
| Generate a weighted nearest neighbour (WNN) graph | generate_wnn_graph_sc |
| Get the ADT feature info | get_adt_feature_info get_adt_feature_info.ADTCounts |
| Get the ADT feature names | get_adt_names get_adt_names.ADTCounts |
| Get the ADT sample info | get_adt_sample_info get_adt_sample_info.ADTCounts |
| Get the available embeddings | get_available_embeddings get_available_embeddings.MetaCells get_available_embeddings.ScCache get_available_embeddings.SingleCells get_available_embeddings.SingleCellsSubset |
| Get the selected model from an LDA topic count sweep | get_best_model get_best_model.LdaKSweepResult |
| Get the best run from a stabilised NMF result | get_best_run get_best_run.StabilisedNmfResult |
| Get the contrastive PCA factors | get_c_pca_factors |
| Get the contrastive PCA loadings | get_c_pca_loadings |
| Get the index position for a gene | get_cell_indices get_cell_indices.MetaCells get_cell_indices.ScMap get_cell_indices.SingleCells get_cell_indices.SingleCellsSubset |
| Get the cell idx (R-based) and cell names | get_cell_info get_cell_info.ScMap get_cell_info.SingleCells |
| Get the cell names | get_cell_names get_cell_names.ScMap get_cell_names.SingleCells get_cell_names.SingleCellsSubset |
| Helper to generate cell ranger input parameters | get_cell_ranger_params |
| Download the CellPhoneDB ligand-receptor database | get_cellphonedb_db |
| Get the cells to keep | get_cells_to_keep get_cells_to_keep.ScMap get_cells_to_keep.SingleCells get_cells_to_keep.SingleCellsSubset |
| Get k-means centroids from a fast cluster result | get_centroids_sc get_centroids_sc.SingleCellFastClusters |
| Extract the TF to gene data from the ScenicGrn object | get_cistarget_res get_cistarget_res.ScenicGrn |
| Get the neighbour graph | get_connectivities get_connectivities.MetaCells get_connectivities.ScCache get_connectivities.SingleCells get_connectivities.SingleCellsSubset |
| Get correlation-based graph | get_cor_graph |
| Get the ready obs data from various sub method | get_data get_data.BoostRes get_data.CellQc get_data.ConsensusNmfResult get_data.NmfResult get_data.ScDblFinderRes get_data.ScListRes get_data.ScMatrixRes get_data.ScrubletRes get_data.SingleCellFastClusters |
| Return the effect size results | get_dge_effect_sizes |
| Return the Limma Voom results | get_dge_limma_voom |
| Return the DGEList | get_dge_list |
| Return QC plots | get_dge_qc_plot |
| Get diagnostics from a BulkModuleResult | get_diagnostics |
| Get differential correlation-based graph | get_diffcor_graph |
| Get the differential abundance results | get_differential_abundance_res get_differential_abundance_res.miloR |
| Get the diffusion permutations | get_diffusion_perms |
| Get the diffusion vector | get_diffusion_vector |
| Get the embedding | get_embedding get_embedding.MetaCells get_embedding.ScCache get_embedding.SingleCells get_embedding.SingleCellsSubset |
| Return the epsilon data | get_epsilon_res |
| Get factor matrices from a BulkModuleResult | get_factors |
| Get the feature matrix used for the classifier | get_feature_mat get_feature_mat.ScDblFinderRes |
| Return the FPKM-normalised counts | get_fpkm_counts |
| Get the index position for a gene | get_gene_indices get_gene_indices.MetaCells get_gene_indices.ScMap get_gene_indices.SingleCells get_gene_indices.SingleCellsSubset |
| Get the gene lengths | get_gene_lengths get_gene_lengths,bixverse::BulkCoExp-method |
| Get the gene names | get_gene_names get_gene_names.ScMap get_gene_names.SingleCells get_gene_names.SingleCellsSubset |
| Get the gene names based on the gene idx | get_gene_names_from_idx get_gene_names_from_idx.ScMap get_gene_names_from_idx.SingleCells get_gene_names_from_idx.SingleCellsSubset |
| Wrapper function to load and process the gene ontology data. | get_go_data_human |
| Get the grid search results | get_grid_search_res |
| Get the H (cell activations) matrix | get_h get_h.ConsensusNmfResult get_h.NmfResult get_h.StabilisedNmfResult |
| Helper function to get the dimensions and storage format | get_h5ad_dimensions |
| Get the hotspot gene membership table | get_hotspot_membership get_hotspot_membership.Hotspot |
| Get the HVG | get_hvg get_hvg.MetaCells get_hvg.ScMap get_hvg.SingleCells get_hvg.SingleCellsSubset |
| Identify HVGs without mutating object state | get_hvg_data_sc |
| Get the ICA component data (stability, convergence, redundancy) | get_ica_stability_res |
| Get the index cells | get_index_cells get_index_cells.miloR |
| Identify the inflection point for elbow-like data | get_inflection_point |
| Get the ligand-target influence matrix | get_influence get_influence.LigandTargetInfluence |
| Get k-means cluster assignments from a fast cluster result | get_kmeans_clusters get_kmeans_clusters.SingleCellFastClusters |
| Get the KNN distance | get_knn_dist get_knn_dist.MetaCells get_knn_dist.ScCache get_knn_dist.SingleCellNearestNeighbour get_knn_dist.SingleCells get_knn_dist.SingleCellsSubset |
| Get the KNN matrix | get_knn_mat get_knn_mat.MetaCells get_knn_mat.ScCache get_knn_mat.SingleCellNearestNeighbour get_knn_mat.SingleCells get_knn_mat.SingleCellsSubset |
| Get the KNN object | get_knn_obj get_knn_obj.MetaCells get_knn_obj.ScCache get_knn_obj.SingleCells get_knn_obj.SingleCellsSubset |
| Get the MAGIC imputed layer | get_magic get_magic.ScCache get_magic.SingleCells get_magic.SingleCellsSubset |
| Get the per-rival marker statistics | get_marker_comparisons get_marker_comparisons.ScSpecificMarkers |
| Get the per-gene marker summaries across all rivals | get_marker_summary get_marker_summary.ScSpecificMarkers |
| Calculate meta cell purity without mutating object state | get_meta_cell_purity |
| Return the metadata | get_metadata |
| Generate an miloR abundance object for differential abundance testing | get_miloR_abundances_sc |
| Get the fitted model | get_model_fit get_model_fit.miloR |
| Get the module membership from a BulkModuleResult | get_modules |
| Get the NMF gene loadings | get_nmf_gene_loadings |
| Get the NMF module membership data.table | get_nmf_modules |
| Get the NMF sample activity | get_nmf_sample_activity |
| Get the multi-run NMF diagnostics | get_nmf_stability |
| Return ancestry terms from an ontology | get_ontology_ancestry |
| Return the outputs | get_outputs |
| Get the parameters that were used. | get_params get_params.ConsensusNmfResult get_params.DialogueResult get_params.Hotspot get_params.LdaKSweepResult get_params.LdaResult get_params.miloR get_params.NmfResult get_params.ScenicGrn get_params.ScNebula get_params.StabilisedNmfResult |
| Get the PCA factors | get_pca_factors get_pca_factors.MetaCells get_pca_factors.ScCache get_pca_factors.SingleCells get_pca_factors.SingleCellsSubset |
| Get the PCA loadings | get_pca_loadings get_pca_loadings.MetaCells get_pca_loadings.ScCache get_pca_loadings.SingleCells get_pca_loadings.SingleCellsSubset |
| Get the PCA singular values | get_pca_singular_val get_pca_singular_val.MetaCells get_pca_singular_val.ScCache get_pca_singular_val.SingleCells get_pca_singular_val.SingleCellsSubset |
| Generate pseudo-bulked matrices | get_pseudobulked_sc |
| Get the RBH results | get_rbh_res |
| Get the fitted residual model | get_residual_fit get_residual_fit.MetaCells get_residual_fit.ScCache get_residual_fit.SingleCells get_residual_fit.SingleCellsSubset |
| Return the resolution results | get_resolution_res |
| Get the final results from the class | get_results get_results.DialogueResult |
| Returns the available features for single cell applications | get_sc_available_features |
| Status of everything held in a single cell object's caches | get_sc_cache_status |
| Getter the counts | get_sc_counts |
| Getter the obs table | get_sc_obs |
| Getter the var table | get_sc_var |
| Get scores | get_scores get_scores.ScDblFinderRes get_scores.ScTypeCellResults get_scores.ScTypeResults |
| Get the similarity matrix | get_sim_matrix |
| Get an individual affinity matrix | get_snf_adjcacency_mat |
| Get the final SNF matrix | get_snf_final_mat |
| Get the SNF params | get_snf_params |
| Get the consensus NMF stability diagnostics | get_stability get_stability.ConsensusNmfResult |
| Getter for the HVG gene names of a Symphony reference | get_symphony_hvg_names |
| Getter for the stored labels of a Symphony reference | get_symphony_labels |
| Getter for the PCA loadings of a Symphony reference | get_symphony_loadings |
| Getter for the corrected embedding of a Symphony reference | get_symphony_z_corr |
| Extract the TF to gene data from the ScenicGrn object | get_tf_to_gene get_tf_to_gene.ScenicGrn |
| Get the highest-probability terms per topic | get_top_terms get_top_terms.LdaResult |
| Return the TPM-normalised counts | get_tpm_counts |
| Get the W (gene loadings) matrix | get_w get_w.ConsensusNmfResult get_w.NmfResult get_w.StabilisedNmfResult |
| Run gene ontology enrichment with elimination method. | gse_go_elim_method |
| Run gene ontology enrichment with elimination method over a list. | gse_go_elim_method_list |
| Gene set enrichment (GSE) based on a hypergeometric test. | gse_hypergeometric |
| Gene set enrichment (GSE) based on a hypergeometric test over a list. | gse_hypergeometric_list |
| Run Harmony | harmony_sc |
| Run Harmony v2 | harmony_v2_sc |
| Calculate the effect size | hedges_g_dge |
| Calculate the local auto-correlation of a gene | hotspot_autocor_sc |
| Calculate the local pairwise gene-gene correlation | hotspot_gene_cor_sc |
| Iterate over different ncomp parameters for ICA | ica_evaluate_comp |
| Identify stability inflection point | ica_optimal_ncomp |
| Prepare class for ICA | ica_processing |
| Run stabilised ICA with a given number of components | ica_stabilised_results |
| Identify the TF to gene regulation | identify_tf_to_genes identify_tf_to_genes.ScenicGrn |
| Install the bixverse agent skill | install_agent_skill |
| kNN-based graph label propagation | knn_graph_label_propagation |
| Sweep the topic count for LDA | lda_k_sweep |
| Leiden or Louvain community detection on an igraph | leiden_clustering |
| Compute ligand activity scores against gene sets | ligand_activity_scores |
| Calculate ULM / MLM activity scores per cell | lm_activity_sc |
| Load an existing SingleCells from disk | load_existing |
| Get the Gene Ontology data human | load_go_human_data |
| Load in h5ad to 'SingleCells' | load_h5ad |
| Load in h5ad with normalised counts to 'SingleCells' | load_h5ad_norm |
| Load in mtx/plain text files to 'SingleCells' | load_mtx |
| Load multiple h5ad files into a single 'SingleCells' | load_multi_h5ad |
| Load multiple mtx directories into a single 'SingleCells' | load_multi_mtx |
| Load multiple 10x CellRanger h5 files into a single 'SingleCells' | load_multi_tenx_h5 |
| Load in data directly from R objects. | load_r_data |
| Load in data from a 'SingleCellExperiment' | load_sce |
| Load the human (hg38) transcription factor list used by SCENIC | load_scenic_tfs_hg38 |
| Load in Seurat to 'SingleCells' | load_seurat |
| Load in a 10x CellRanger h5 file to 'SingleCells' | load_tenx_h5 |
| Map a SingleCells query onto a Symphony reference | map_symphony_query |
| Run MELD signal smoothing for differential abundance estimation | meld_sc |
| Merge meta cell objects into one | merge_meta_cells |
| Merge multiple 'SingleCells' experiments into one | merge_sc_experiments |
| Merge obs columns from subsets back into the parent object | merge_subset_obs |
| Generate source-pure meta cells and merge them | meta_cells_per_group |
| bixverse meta cell class | MetaCells |
| Calculate module activity scores | module_scores_sc |
| Derive sparse module membership from a loading matrix | modules_from_loadings |
| Run NEBULA on meta cells | nebula_mc |
| Run NEBULA on single cells | nebula_sc |
| Network diffusion class | NetworkDiffusions |
| Generates a new 'ADTCounts' class | new_adt_counts_clr |
| Generates a new 'ADTCounts' class via DSB normalisation | new_adt_counts_dsb |
| Helper function to generate kNN data with distances | new_sc_knn |
| Run non-negative matrix factorisation on a BulkCoExp | nmf_bulk |
| Sweep k for consensus NMF on a BulkCoExp | nmf_k_sweep_bulk |
| Sweep k for consensus NMF on single cell or meta cell data | nmf_k_sweep_sc |
| Run single-run NMF on single cell or meta cell data | nmf_sc |
| Normalise the count data for DGE. | normalise_bulk_dge |
| OntologySim class | OntologySim |
| Calculates a harmonic sum normalised between 0 to 1. | ot_harmonic_score |
| Wrapper function for parameters for binary heatmap data | params_binary_heatmap |
| Wrapper function to generate blitzGSEA parameters | params_blitzgsea |
| Wrapper function for Boost parameters | params_boost |
| Wrapper function for Bayesian PCA parameters | params_bpca |
| Wrapper function to generate bulk sparsification parameters | params_bulk_sparsity |
| Wrapper function to CisTarget parameters | params_cistarget |
| Wrapper function to generate community detection parameters | params_community_detection |
| Wrapper function for graph generation | params_cor_graph |
| Wrapper function to generate CoReMo parameters | params_coremo |
| Wrapper function to generate DGRDL parameters | params_dgrdl |
| Wrapper function for the DIALOGUE mixed model parameters | params_dialogue_hlm |
| Wrapper function for the DIALOGUE decomposition parameters | params_dialogue_pmd |
| Wrapper function for the DIALOGUE refinement parameters | params_dialogue_refine |
| Wrapper function for parameters for the edgeR quasi-likelihood workflow | params_edger_ql |
| Helper function to generate default parameters for the fast clustering for the doublet detection methods | params_fast_cluster_default |
| Wrapper function to generate resolution parameters for Leiden or Louvain clustering. | params_graph_resolution |
| Wrapper function to generate GSEA parameters | params_gsea |
| Wrapper function to generate GSVA parameters | params_gsva |
| Helper function to generate HVG defaults | params_hvg_defaults |
| Helper function to generate the scran HVG trend defaults | params_hvg_scran_defaults |
| Wrapper function for standard ICA parameters | params_ica_general |
| Wrapper function for ICA ncomp iterations | params_ica_ncomp |
| Wrapper function for ICA randomisation | params_ica_randomisation |
| K-mean parameter defaults. | params_kmeans_defaults |
| Helper function to generate kNN defaults | params_knn_defaults |
| Helper function to generate the kNN graph defaults | params_knn_graph_defaults |
| Wrapper function to generate label propagation parameters | params_label_propagation |
| Wrapper function for the LDA parameters | params_lda |
| Parameters for ligand to target influence computation | params_ligand_target |
| Wrapper function for parameters for the limma-voom workflow | params_limma_voom |
| Wrapper function to generate ULM / MLM activity parameters | params_lm_activity |
| Constructor for MELD parameters | params_meld |
| Wrapper function to generate module membership parameters | params_module_membership |
| Wrapper function for parameters for NEBULA | params_nebula |
| Wrapper function for consensus NMF parameters | params_nmf_consensus |
| Wrapper function for NMF (HALS) parameters | params_nmf_hals |
| Helper function to generate normalisation defaults for doublet detection. | params_norm_doublets_defaults |
| Helper function to generate default parameters for PCA | params_pca_defaults |
| Wrapper function for probabilistic PCA parameters | params_ppca |
| Wrapper function for analytic Pearson residual parameters | params_sc_apr |
| Wrapper function for parameters for AUCell | params_sc_aucell |
| Wrapper function for the BBKNN parameters | params_sc_bbknn |
| Wrapper function for Bonsai parameters | params_sc_bonsai |
| Wrapper function for the branch cell selection parameters | params_sc_branch_selection |
| Wrapper function for parameters for bootstrapped meta cell generation | params_sc_bt_metacells |
| Parameters for the CellPhoneDB analysis | params_sc_cellphonedb |
| Default parameters for CellSweep denoising | params_sc_cellsweep |
| Default parameters for DSB ADT normalisation | params_sc_dsb |
| Parameters for identifying empty droplets | params_sc_empty_droplets |
| Fast single cell clustering parameters | params_sc_fast_cluster |
| Wrapper function for the fastMNN parameters | params_sc_fastmnn |
| Wrapper function for gene trend parameters | params_sc_gene_trends |
| Default parameters for Harmony batch correction | params_sc_harmony |
| Default parameters for Harmony v2 batch correction | params_sc_harmony_v2 |
| Wrapper function for parameters for HotSpot | params_sc_hotspot |
| Wrapper function for HVG detection parameters. | params_sc_hvg |
| Parameters for single cell kNN searches | params_sc_knn |
| Wrapper function for MAGIC imputation parameters | params_sc_magic |
| Wrapper function for the MetaCells2 per-pile parameters | params_sc_metacells2 |
| Wrapper function for the MetaCells2 divide and conquer parameters | params_sc_metacells2_dac |
| Wrapper function for parameters for MiloR | params_sc_miloR |
| Wrapper function to generate QC metric params for single cell | params_sc_min_quality |
| Wrapper function to provide data for mtx-based loading | params_sc_mtx_io |
| Wrapper function for parameters for neighbour identification in single cell | params_sc_neighbours |
| Wrapper function for Palantir parameters | params_sc_palantir |
| Wrapper for PCA specifically designed for single cells | params_sc_pca |
| Wrapper function for scTransform (v2) parameters | params_sc_sctransform |
| Wrapper function for the SEACells parameters | params_sc_seacells |
| Wrapper function for the Seurat CCA parameters | params_sc_seurat_cca |
| Wrapper function for the Seurat rPCA parameters | params_sc_seurat_rpca |
| Wrapper function for parameters for SuperCell generation | params_sc_supercell |
| Default parameters for generation of synthetic CellSweep data | params_sc_synthetic_cellsweep |
| Default parameters for generation of synthetic single cell data (RNA) | params_sc_synthetic_data |
| Default parameters for generation of synthetic single cell data (ADT) | params_sc_synthetic_data_adt |
| Default parameters for generation of synthetic DIALOGUE data | params_sc_synthetic_dialogue |
| Wrapper function for parameters for VISION with auto-correlation | params_sc_vision |
| Wrapper function for WNN parameters | params_sc_wnn |
| Wrapper function for scDblFinder doublet detection parameters | params_scdblfinder |
| Constructor for SCENIC parameters | params_scenic |
| Wrapper function for parameters for the SCENIC binarisation | params_scenic_binarise |
| Default parameters for the SCENIC ExtraTrees regression learner | params_scenic_extra_trees_defaults |
| Default parameters for the SCENIC GradientBoosting (GRNBoost2) regression learner | params_scenic_gradient_boosting_defaults |
| Default parameters for the SCENIC RandomForest regression learner | params_scenic_random_forest_defaults |
| Wrapper function for Scrublet doublet detection parameters | params_scrublet |
| Parameters for the per-cell ScType assignment | params_sctype_cells |
| Wrapper function to generate SNF parameters | params_snf |
| Wrapper function to generate ssGSEA parameters | params_ssgsea |
| Default parameters for Symphony query mapping | params_symphony_map |
| Wrapper function to generate synthetic bulk RNAseq parameters | params_synthetic_bulk_rnaseq |
| Use MAD outlier detection on per-cell QC metrics | per_cell_qc_outlier |
| Generate permuation scores for the diffusion | permute_seed_nodes |
| Run PHATE on a SingleCells/MetaCells object | phate_sc |
| Helper plot function for boxplot of normalised data | plot_boxplot_normalization |
| Plot the epsilon vs. power law goodness of fit result | plot_epsilon_res |
| Plot the highly variable genes | plot_hvgs |
| Plot various parameters with no comp | plot_ica_ncomp_params |
| Plot the stability of the ICA components | plot_ica_stability_individual |
| Plot the k cuts vs median R2 | plot_optimal_cuts |
| Helper plot function for pca with contrasts | plot_pca |
| Plot the PCA data | plot_pca_res |
| Helper plot function of distribution of genes by samples | plot_preprocessing_genes |
| Helper plot function for identification of outliers | plot_preprocessing_outliers |
| Helper function to plot distance to affinity relationship | plot_rbf_impact |
| Plot the resolution results. | plot_resolution_res |
| Helper plot function for Voom normalisation | plot_voom_normalization |
| Plot the LDA topic count sweep | plot.LdaKSweepResult |
| Plot a consensus NMF k sweep | plot.NmfKSweepResult |
| Pre-process data for subsequent ontology similarity | pre_process_sim_onto |
| Helper function to prepare cell markers | prepare_cell_markers |
| Process the raw data | preprocess_bulk_coexp |
| QC on the bulk dge data (DEPRECATED!) | preprocess_bulk_dge |
| Pre-scan multiple h5ad files for multi-sample loading | prescan_h5ad_files |
| Prescan multiple mtx directories for a multi-load | prescan_mtx_dirs |
| Pre-scan multiple 10x CellRanger h5 files for multi-sample loading | prescan_tenx_h5_files |
| Prioritise sender-ligand-receiver-receptor interactions | prioritise_interactions |
| Process Gene Ontology data into the right format | process_go_data |
| Run the edgeR quasi-likelihood workflow on pseudo-bulked single cells | pseudobulk_dge_sc |
| QC on the bulk dge data | qc_bulk_dge |
| Reciprocal best hit graph | RbhGraph |
| Read obs and var tables and metadata from an h5ad file | read_h5ad_metadata |
| Read summary statistics from the X slot of an h5ad file | read_h5ad_x_summary |
| Read in the motif annotation file | read_motif_annotation_file |
| Read in the motif rankings and transform them into a matrix | read_motif_ranking |
| Read in 10x h5 ADT data from multiple files | read_multi_tenx_h5_adt |
| Read in 10x h5 ADT data | read_tenx_h5_adt |
| Read barcode and feature tables and metadata from a 10x h5 file | read_tenx_h5_metadata |
| Recompute the layout of a Bonsai tree | relayout_bonsai |
| Return the ADT feature names removing the isotypes | remove_adt_isotypes |
| Remove the MAGIC imputed layer | remove_magic remove_magic.ScCache remove_magic.SingleCells remove_magic.SingleCellsSubset |
| Remove the fitted residual model | remove_residual_fit remove_residual_fit.MetaCells remove_residual_fit.ScCache remove_residual_fit.SingleCells remove_residual_fit.SingleCellsSubset |
| Remove samples from object | remove_samples |
| Rescue MAD-flagged cells that fall within safe bounds | rescue_cells |
| Reset the cells to keep | reset_cells_to_keep reset_cells_to_keep.MetaCells reset_cells_to_keep.SingleCells reset_cells_to_keep.SingleCellsMultiModal reset_cells_to_keep.SingleCellsSubset |
| Robust scaler. | robust_scale |
| Rust implementation of a Loess function | rs_2d_loess |
| Calculate batch silhouette width from an embedding | rs_batch_silhouette_width |
| Calibrate the blitzGSEA gamma null for a signature | rs_blitzgsea_calibrate |
| Score gene sets against a calibrated blitzGSEA null | rs_blitzgsea_score |
| Calculate normalisation factors | rs_calc_norm_factors |
| Calculate cell type silhouette width from an embedding | rs_cell_type_asw |
| Run CisTarget motif enrichment analysis | rs_cistarget |
| Helper to compare kNN graphs | rs_compare_knn |
| Calculate a constrained page-rank score | rs_constrained_page_rank |
| Calculate a constrained page-rank score over a list. | rs_constrained_page_rank_list |
| Calculate the contrastive PCA | rs_contrastive_pca |
| Calculate the column wise correlations. | rs_cor |
| Calculate the column wise correlations and returns the upper triangle | rs_cor_upper_triangle |
| Calculate the column wise correlations. | rs_cor2 |
| Calculate the column wise cosine similarities | rs_cos |
| Helper to get zero stats from a given matrix | rs_count_zeroes |
| Calculates the correlation matrix from the co-variance matrix | rs_cov2cor |
| Calculate the column-wise co-variance. | rs_covariance |
| Counts per million | rs_cpm |
| Generate a vector-based representation of the upper triangle of a matrix | rs_dense_to_upper_triangle |
| Calculate the column wise differential correlation between two sets of data. | rs_differential_cor |
| Calculate the pairwise column distance in a matrix | rs_dist |
| Run the edgeR quasi-likelihood chain on a count matrix | rs_edger_ql |
| Fast AUC calculation | rs_fast_auc |
| Run the Rust implementation of fast ICA. | rs_fast_ica |
| Calculate a BH-based FDR | rs_fdr_adjustment |
| Filter lowly expressed genes | rs_filter_by_expr |
| Fit Palantir gene trends over pseudotime | rs_gene_trends |
| Generate the ligand to target influence matrices | rs_generate_ligand_target_influence |
| Run fgsea simple method for gene ontology with elimination method | rs_geom_elim_fgsea_simple |
| Calculates the Gower distance for a given matrix | rs_gower_dist |
| Calculate graph connectivity per cell type | rs_graph_connectivity |
| Run hypergeometric enrichment over the gene ontology | rs_gse_geom_elim |
| Run hypergeometric enrichment a list of target genes over the gene ontology | rs_gse_geom_elim_list |
| Rust version of the GSVA algorithm | rs_gsva |
| Load in h5ad data via Rust | rs_h5ad_data |
| Calculates the Hamming distance between categorical columns | rs_hamming_dist |
| Harmony batch correction in Rust | rs_harmony |
| Harmony batch correction in Rust (version 2) | rs_harmony_v2 |
| Calculate the Hedge's G effect | rs_hedges_g |
| Run a single hypergeometric test. | rs_hypergeom_test |
| Run a hypergeometric test over a list of target genes | rs_hypergeom_test_list |
| Run ICA over a given no_comp with random initialisations of w_init | rs_ica_iters |
| Run ICA with cross-validation and random initialisation | rs_ica_iters_cv |
| Calculate rapidly Jaccard similarities between rows | rs_jaccard_row_integers |
| Calculate kBET type scores | rs_kbet |
| kNN label propagation | rs_knn_label_propagation |
| Flatten kNN matrix to edge list | rs_knn_mat_to_edge_list |
| Rust implementation of Leiden and Louvain clustering | rs_leiden_clustering |
| Calculate the NicheNet ligand activity scores | rs_ligand_activity_scores |
| Run the limma linear model chain on a count matrix | rs_limma_voom |
| Calculate LISI scores on any label | rs_lisi |
| Calculate MAD outlier detection in Rust. | rs_mad_outlier |
| Calculate AUCell in Rust (for meta cells) | rs_mc_aucell |
| Run DIALOGUE over meta cells | rs_mc_dialogue |
| Calculate gene spatial auto-correlations (for meta cells) | rs_mc_hotspot_autocor |
| Calculate gene to gene spatial correlations (for meta cells) | rs_mc_hotspot_gene_cor |
| Meta cells highly variable genes | rs_mc_hvg |
| PCA on MetaCells (sparse data) | rs_mc_pca |
| SCENIC on MetaCells | rs_mc_scenic |
| Calculate VISION pathway scores in Rust (for meta cells) | rs_mc_vision |
| Calculate VISION pathway scores with auto-correlation (for meta cells) | rs_mc_vision_with_autocorrelation |
| Calculate mitch enrichment leveraging Rust under the hood | rs_mitch_calc |
| Calculates the mutual information matrix | rs_mutual_info |
| Fit the NEBULA negative binomial gamma mixed model over meta cells | rs_nebula_mc |
| Calculate the semantic similarity in an ontology | rs_onto_semantic_sim |
| Calculate the semantic similarity in an ontology | rs_onto_semantic_sim_mat |
| Calculate the Wang similarity for specific terms | rs_onto_sim_wang |
| Calculate the Wang similarity matrix for an ontology | rs_onto_sim_wang_mat |
| Calculate the OT harmonic sum | rs_ot_harmonic_sum |
| Rust version of calculating the personalised page rank | rs_page_rank |
| Calculate massively parallelised personalised page rank scores | rs_page_rank_parallel |
| Principal component regression on batch | rs_pcr |
| Calculate the hypergeometric test in Rust | rs_phyper |
| Calculates the point wise mutual information | rs_pointwise_mutual_info |
| Rust implementation of prcomp | rs_prcomp |
| Prepare the data for whitening | rs_prepare_whitening |
| Run randomised SVD over a matrix | rs_random_svd |
| Apply a range normalisation on a vector. | rs_range_norm |
| Gene rank matrix | rs_rank_matrix_col |
| Stable-gene rank matrix | rs_rank_matrix_col_stable |
| Apply a Radial Basis Function | rs_rbf_function |
| Apply a Radial Basis Function (to a matrix) | rs_rbf_function_mat |
| Generate reciprocal best hits based on correlations | rs_rbh_cor |
| Generate reciprocal best hits based on set similarities | rs_rbh_sets |
| Remove batch effects from a log-expression matrix | rs_remove_batch_effect |
| Generates the kNN graph | rs_sc_knn |
| Generates the neighbour graph for igraph | rs_sc_knn_graph |
| Generates the kNN graph with additional distances | rs_sc_knn_w_dist |
| Set similarities | rs_set_similarity |
| Set similarities over one list | rs_set_similarity_list |
| Set similarities over two list | rs_set_similarity_list2 |
| Rust version of singscore for many gene sets | rs_singscore_multi |
| Rust version of the singscore permutation test | rs_singscore_permutation_test |
| Rust version of singscore for a single gene set | rs_singscore_single |
| Similarity network fusion | rs_snf |
| Generate a sparse dictionary with DGRDL | rs_sparse_dict_dgrdl |
| Generate a sparse dictionary with DGRDL | rs_sparse_dict_dgrdl_grid_search |
| Weighted Benjamini-Hochberg over overlapping neighbourhoods | rs_spatial_fdr |
| Rust implementation of spectral clustering | rs_spectral_clustering |
| Rust implementation of spectral clustering | rs_spectral_clustering_sim |
| Rust version of the ssGSEA algorithm | rs_ssgsea |
| Calculate massively parallelised tied diffusion scores | rs_tied_diffusion_parallel |
| Calculates the TOM over an affinity matrix | rs_tom |
| Reconstruct a matrix from a flattened upper triangle vector | rs_upper_triangle_to_dense |
| Generate sparse data from an upper triangle | rs_upper_triangle_to_sparse |
| Voom-transform a count matrix | rs_voom_normalise |
| Run the weighted nearest neighbour algorithm | rs_wnn |
| Bayesian PCA on a matrix with missing values | run_bpca |
| Run outlier detection on per-cell QC metrics | run_cell_qc |
| Fixed-threshold cell QC | run_cell_qc_fixed |
| Main function to run CisTarget | run_cistarget |
| Run the edgeR quasi-likelihood workflow | run_edger_ql |
| Fit gene trends over Palantir pseudotime | run_gene_trends_sc |
| Fit a latent Dirichlet allocation model | run_lda |
| Wrapper for a Limma Voom analysis | run_limma_voom |
| Impute a subset of genes with MAGIC | run_magic_sc |
| Run PAGA graph abstraction | run_paga_sc |
| Run Palantir trajectory inference | run_palantir_sc |
| Probabilistic PCA on a matrix with missing values | run_ppca |
| Run the SNF algorithm | run_snf |
| Save a 'SingleCells' object to h5ad | save_h5ad |
| Save memory-bound data to disk | save_sc_exp_to_disk |
| Convert SingleCellNearestNeighbour to manifoldsR NearestNeighbours | sc_knn_to_nearest_neighbours |
| Construct an empty single cell pipeline | sc_pipeline |
| Run scDblFinder doublet detection on a SingleCells object | scdblfinder_sc |
| Filter genes for SCENIC GRN inference | scenic_gene_filter_sc |
| Run SCENIC GRN inference | scenic_grn_sc |
| Score clusters based on ScType | score_clusters score_clusters.ScTypeResults |
| Doublet detection with Scrublet | scrublet_sc |
| Write scTransform-corrected counts to a new store | sct_corrected_counts_sc |
| Store the leaf coordinates of a Bonsai tree as an embedding | set_bonsai_embedding |
| Set/add the MAGIC imputed layer | set_magic set_magic.ScCache set_magic.SingleCells set_magic.SingleCellsSubset |
| Set/add the fitted residual model | set_residual_fit set_residual_fit.MetaCells set_residual_fit.ScCache set_residual_fit.SingleCells set_residual_fit.SingleCellsSubset |
| Add a new column to the obs table | set_sc_new_obs_col |
| Add multiple new columns to the obs table | set_sc_new_obs_col_multiple |
| Add a new column to the var table | set_sc_new_var_cols |
| Rename columns in the obs or var table | setnames_sc |
| Run Seurat CCA integration | seurat_cca_sc |
| Run Seurat rPCA integration | seurat_rpca_sc |
| Similarity network fusion | SimilarityNetworkFusion |
| Simplify gene set results via ontologies | simplify_hypergeom_res |
| Simulate sequencing-depth dropouts on synthetic bulk data | simulate_dropouts |
| Single cell count data handler | $.SingleCellCountData SingleCellCountData |
| bixverse SingleCells class | SingleCells |
| bixverse SingleCells (multi modal) class | SingleCellsMultiModal |
| bixverse single cell subset class | SingleCellsSubset |
| Run stabilised (multi-restart) NMF on a BulkCoExp | stabilised_nmf_bulk |
| Run stabilised (multi-run) NMF on single cell or meta cell data | stabilised_nmf_sc |
| Pipeline step: BBKNN batch correction | step_bbknn_sc |
| Pipeline step: graph-based clustering | step_clusters_sc |
| Pipeline step: fastMNN batch correction | step_fast_mnn_sc |
| Pipeline step: Harmony batch correction | step_harmony_sc |
| Pipeline step: Harmony v2 batch correction | step_harmony_v2_sc |
| Pipeline step: identify highly variable genes | step_hvg_sc |
| Pipeline step: generate meta cells | step_metacells_sc |
| Pipeline step: nearest neighbours | step_neighbours_sc |
| Pipeline step: PCA | step_pca_sc |
| Stream in h5ad to 'SingleCells' (alias) | stream_h5ad |
| bixverse SymphonyReference class | SymphonyReference |
| Generates synthetic bulk RNAseq data | synthetic_bulk_cor_matrix |
| Generates synthetic data for contrastive PCA exploration. | synthetic_c_pca_data |
| Generates a simple synthetic, pseudo gene expression matrix | synthetic_signal_matrix |
| Test neighbourhoods for differential abundance | test_nhoods test_nhoods.miloR |
| Generate TF to gene correlations | tf_to_genes_correlations tf_to_genes_correlations.ScenicGrn |
| Run the SCENIC motif enrichment | tf_to_genes_motif_enrichment tf_to_genes_motif_enrichment.ScenicGrn |
| Diffuse seed genes in a tied manner over a network | tied_diffusion |
| Helper function to transform strings to snake_case | to_snake_case |
| Calculate the proportions of reads for the Top N genes | top_genes_perc_sc |
| Transfer labels from a Symphony reference to a query via kNN majority vote | transfer_labels_symphony |
| Run t-SNE on a SingleCells/MetaCells object | tsne_sc |
| Run UMAP on a SingleCells/MetaCells object | umap_sc |
| Replace values in a metadata column | update_metadata_values |
| Upgrade the on-disk counts to the v4 file format | upgrade_sc_store |
| Transform an upper triangle-stored matrix to a sparse one | upper_triangle_to_sparse |
| Class for symmetric correlation matrices | upper_triangular_sym_mat |
| Check that a pipeline can run on a given class | validate_pipeline |
| Calculate VISION scores | vision_sc |
| Calculate VISION scores (with auto-correlation scores) | vision_w_autocor_sc |
| Helper function to write data to a cell ranger like output | write_cellranger_output |
| Helper function to write data to h5ad format | write_h5ad_sc |
| Helper function to write data to a dense h5ad file | write_h5ad_sc_dense |
