{ "_id": "6ad014c0e2d863f662953fa2", "Package": "bixverse", "Title": "Rust-accelerated standard bioinformatics, computational biology\nworkflows", "Version": "0.7.0", "Authors@R": "c(\nperson(\ngiven = \"Gregor\",\nfamily = \"Lueg\",\nemail = \"gregorlueg@me.com\",\nrole = c(\"aut\", \"cre\")\n),\nperson(\ngiven = \"Liesbeth\",\nfamily = \"Francois\",\nemail = \"francois.liesbeth@gmail.com\",\nrole = c(\"ctb\")\n),\nperson(\ngiven = \"Andrew\",\nfamily = \"Skelton\",\nemail = \"Ajskelton@proton.me\",\nrole = c(\"ctb\")\n),\nperson(\ngiven = \"Grant\",\nfamily = \"Neilson\",\nemail = \"grant.neilson5@gmail.com\",\nrole = c(\"ctb\")\n)\n)", "Description": "Your favourite methods used in bioinformatics and\ncomputational biology made VERY fast via Rust. Contains various\ngene set enrichment methods, graph- based methods,\nco-expression module detection and much more. Additionally, has\nnow a single cell analysis suite, enabling million cell scale\nanalysis on local compute.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "URL": "https://gregorlueg.github.io/bixverse", "Roxygen": "list(markdown = TRUE)", "Config/rextendr/version": "0.5.0", "biocViews": "", "SystemRequirements": "Cargo (Rust's package manager), rustc >= 1.90, xz", "LazyData": "true", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "cmake libglpk-dev libicu-dev libxml2-dev libssl-dev\nxz-utils libclang-dev", "Repository": "https://gregorlueg.r-universe.dev", "Date/Publication": "2026-10-14 21:51:11 UTC", "RemoteUrl": "https://github.com/GregorLueg/bixverse", "RemoteRef": "main", "RemoteSha": "d543471b7464b6dddc9a3e660abfca3252166593", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-14 22:57:51 UTC", "User": "root" }, "Author": "Gregor Lueg [aut, cre],\nLiesbeth Francois [ctb],\nAndrew Skelton [ctb],\nGrant Neilson [ctb]", "Maintainer": "Gregor Lueg ", "_user": "gregorlueg", "_type": "src", "_file": "bixverse_0.7.0.tar.gz", "_fileid": "https://r2.ropensci.org/3b601a4aa2becd6c9be0ec4a3a9af16b9c553099c6e4781eca8856372b15d8d0", "_filesize": 9980455, "_sha256": "3b601a4aa2becd6c9be0ec4a3a9af16b9c553099c6e4781eca8856372b15d8d0", "_expires": "2027-01-22T23:48:13.000Z", "_created": "2026-10-14T22:57:51.000Z", "_published": "2026-10-14T23:48:16.388Z", "_buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 116153377551, "time": 930, "config": "linux-devel-arm64", "r": "4.7.0", "check": "NOTE", "artifact": "11935917400" }, { "job": 116153377577, "time": 1133, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "11935383238" }, { "job": 116153377529, "time": 923, "config": "linux-release-arm64", "r": "4.6.1", "check": "NOTE", "artifact": "11935902830" }, { "job": 116153377661, "time": 1096, "config": "linux-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "11935104380" }, { "job": 116153377561, "time": 1121, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "11935129183" }, { "job": 116153377609, "time": 2388, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "NOTE", "artifact": "11936482319" }, { "job": 116153377495, "time": 975, "config": "macos-release-arm64", "r": "4.6.1", "check": "NOTE", "artifact": "11935393637" }, { "job": 116153377602, "time": 2334, "config": "macos-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "11936552481" }, { "job": 116147880717, "time": 948, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11934709265" }, { "job": 116153377487, "time": 419, "config": "wasm-release", "r": "4.6.0", "check": "FAIL", "artifact": "" }, { "job": 116153377557, "time": 1343, "config": "windows-devel-arm64", "r": "4.7.0", "check": "NOTE", "artifact": "11935749234" }, { "job": 116153377809, "time": 1835, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "11936233696" }, { "job": 116153377537, "time": 1180, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "NOTE", "artifact": "11935798056" }, { "job": 116153377813, "time": 1044, "config": "windows-release-arm64", "r": "4.6.1", "check": "NOTE", "artifact": "11935962822" }, { "job": 116153377682, "time": 1716, "config": "windows-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "11935934905" } ], "_upstream": "https://github.com/GregorLueg/bixverse", "_commit": { "id": "d543471b7464b6dddc9a3e660abfca3252166593", "author": "Gregor Lueg <93532511+GregorLueg@users.noreply.github.com>", "committer": "GitHub ", "message": "Merge pull request #259 from GregorLueg/release-0.7.0\n\nRelease: 0.7.0", "time": 1792014671 }, "_maintainer": { "name": "Gregor Lueg", "email": "gregorlueg@me.com", "login": "gregorlueg", "linkedin": "in/gregor-lueg-474437a7", "description": "Data science, machine learning, and computational biology for drug discovery", "uuid": 93532511 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.2", "role": "Depends" }, { "package": "arrow", "role": "Imports" }, { "package": "checkmate", "role": "Imports" }, { "package": "cli", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "DBI", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "duckdb", "version": ">= 1.5.5", "role": "Imports" }, { "package": "fastcluster", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "igraph", "role": "Imports" }, { "package": "jsonlite", "role": "Imports" }, { "package": "lifecycle", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "manifoldsR", "version": ">= 0.3.5", "role": "Imports" }, { "package": "matrixStats", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "mirai", "role": "Imports" }, { "package": "patchwork", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "R.utils", "role": "Imports" }, { "package": "R6", "role": "Imports" }, { "package": "rextendr", "role": "Imports" }, { "package": "rhdf5", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "S7", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "zeallot", "role": "Imports" }, { "package": "airway", "role": "Suggests" }, { "package": "biomaRt", "role": "Suggests" }, { "package": "BiocParallel", "role": "Suggests" }, { "package": "bixverse.plots", "role": "Suggests" }, { "package": "cluster", "role": "Suggests" }, { "package": "e1071", "role": "Suggests" }, { "package": "edgeR", "role": "Suggests" }, { "package": "fgsea", "role": "Suggests" }, { "package": "GSVA", "role": "Suggests" }, { "package": "infotheo", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "limma", "role": "Suggests" }, { "package": "microbenchmark", "role": "Suggests" }, { "package": "mitch", "role": "Suggests" }, { "package": "methods", "role": "Suggests" }, { "package": "ontologyIndex", "role": "Suggests" }, { "package": "ontologySimilarity", "role": "Suggests" }, { "package": "qs2", "role": "Suggests" }, { "package": "quarto", "role": "Suggests" }, { "package": "singscore", "role": "Suggests" }, { "package": "scrapper", "role": "Suggests" }, { "package": "SingleCellExperiment", "role": "Suggests" }, { "package": "SummarizedExperiment", "role": "Suggests" }, { "package": "StatMatch", "role": "Suggests" }, { "package": "Seurat", "role": "Suggests" }, { "package": "tictoc", "role": "Suggests" }, { "package": "tinytest", "role": "Suggests" }, { "package": "furrr", "role": "Suggests" }, { "package": "future", "role": "Suggests" } ], "_owner": "gregorlueg", "_selfowned": true, "_usedby": 2, "_updates": [ { "week": "2025-44", "n": 2 }, { "week": "2025-48", "n": 2 }, { "week": "2026-13", "n": 1 }, { "week": "2026-14", "n": 3 }, { "week": "2026-20", "n": 2 }, { "week": "2026-25", "n": 1 }, { "week": "2026-26", "n": 3 }, { "week": "2026-27", "n": 4 }, { "week": "2026-28", "n": 1 }, { "week": "2026-29", "n": 1 }, { "week": "2026-31", "n": 2 }, { "week": "2026-32", "n": 1 }, { "week": "2026-33", "n": 7 }, { "week": "2026-35", "n": 1 }, { "week": "2026-36", "n": 7 }, { "week": "2026-39", "n": 3 }, { "week": "2026-40", "n": 6 }, { "week": "2026-41", "n": 6 }, { "week": "2026-42", "n": 1 } ], "_tags": [ { "name": "v0.0.2.3", "date": "2025-11-01" }, { "name": "v0.3.0", "date": "2026-03-26" }, { "name": "v0.3.1", "date": "2026-04-03" }, { "name": "v0.3.2", "date": "2026-05-11" }, { "name": "v0.3.3", "date": "2026-06-15" }, { "name": "v0.4.0", "date": "2026-06-27" }, { "name": "v0.4.1", "date": "2026-06-30" }, { "name": "v0.4.2", "date": "2026-07-03" }, { "name": "v0.4.3", "date": "2026-07-10" }, { "name": "v0.4.4", "date": "2026-07-31" }, { "name": "v0.4.5", "date": "2026-08-01" }, { "name": "v0.4.6", "date": "2026-08-06" }, { "name": "v0.4.7", "date": "2026-08-11" }, { "name": "v0.4.8", "date": "2026-08-13" }, { "name": "v0.4.9", "date": "2026-08-16" }, { "name": "v0.4.10", "date": "2026-08-26" }, { "name": "v0.4.11", "date": "2026-09-01" }, { "name": "v0.5.0", "date": "2026-09-03" }, { "name": "v0.5.1", "date": "2026-09-06" }, { "name": "v0.5.2", "date": "2026-09-24" }, { "name": "v0.5.3", "date": "2026-09-28" }, { "name": "v0.5.4", "date": "2026-09-29" }, { "name": "v0.5.5", "date": "2026-10-01" }, { "name": "v0.5.6", "date": "2026-10-02" }, { "name": "v0.5.7", "date": "2026-10-03" }, { "name": "v0.6.0", "date": "2026-10-07" }, { "name": "v0.6.1", "date": "2026-10-08" }, { "name": "v0.6.2", "date": "2026-10-09" }, { "name": "v0.6.3", "date": "2026-10-10" }, { "name": "v0.7.0", "date": "2026-10-14" } ], "_topics": [ "bioinformatics", "computational-biology", "r-language", "single-cell", "rust", "cargo" ], "_stars": 37, "_contributors": [ { "user": "gregorlueg", "count": 813, "uuid": 93532511 }, { "user": "elysheba", "count": 40, "uuid": 36504369 }, { "user": "grantn5", "count": 36, "uuid": 127763095 }, { "user": "btraven00", "count": 9, "uuid": 128150520 }, { "user": "andrewskelton", "count": 4, "uuid": 6512858 } ], "_userbio": { "uuid": 93532511, "type": "user", "name": "Gregor Lueg", "followers": 23, "description": "Data science, machine learning, and computational biology for drug discovery" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/bixverse" }, "_devurl": "https://github.com/gregorlueg/bixverse", "_pkgdown": "https://gregorlueg.github.io/bixverse", "_searchresults": 53, "_cargo": true, "_rbuild": "4.6.1", "_assets": [ "extra/bixverse.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "LICENSE" ], "_cranurl": false, "_exports": [ "%>>%", "add_adt_counts_sc", "add_new_metadata", "add_nhoods_info", "add_sc_new_obs", "add_snf_data_modality", "add_symphony_labels", "AnnDataParser", "apply_pipeline", "apply_pipeline_per_group", "archive_sc_exp", "as_ligand_target_influence", "assert_sc_state", "assign_sc_type", "aucell_sc", "batch_correction_bulk_dge", "bbknn_sc", "binarise_regulon_activity", "BixverseBaseClass", "blitzgsea_calibrate", "bonsai_sc", "build_regulons", "build_symphony_ref", "bulk_coexp", "bulk_dge", "bulk_dge_from_h5ad", "BulkCoExp", "BulkDge", "c_pca_plot_alphas", "calc_blitzgsea", "calc_diffusion_coordinates", "calc_fgsea", "calc_fgsea_simple", "calc_gsea_traditional", "calc_gsva", "calc_knn_metrics", "calc_manifold_metrics", "calc_meta_cell_compactness", "calc_meta_cell_purity", "calc_meta_cell_separation", "calc_mitch", "calc_sc_type_scores", "calc_singscore", "calc_singscore_multi", "calc_singscore_rank", "calc_ssgsea", "calculate_all_dges", "calculate_batch_asw_sc", "calculate_cell_type_asw_sc", "calculate_critical_value", "calculate_dge_hedges", "calculate_dge_limma", "calculate_diffusion_auc", "calculate_effect_size", "calculate_graph_connectivity_sc", "calculate_information_content", "calculate_integration_metrics_sc", "calculate_kbet_sc", "calculate_lisi_sc", "calculate_pca_adt_sc", "calculate_pca_bulk_dge", "calculate_pca_sc", "calculate_pcr_sc", "calculate_rpkm", "calculate_semantic_sim", "calculate_semantic_sim_mat", "calculate_semantic_sim_onto", "calculate_sparsity_stats", "calculate_tom", "calculate_tom_from_exp", "calculate_tpm", "calculate_wang_sim", "calculate_wang_sim_mat", "calculate_wang_sim_onto", "call_doublets_manual", "cellphonedb_sc", "cellsweep_sc", "change_gene_identifier", "check_sc_state", "community_detection", "compute_expression_info_sc", "consensus_nmf_bulk", "consensus_nmf_sc", "constrained_page_rank", "constrained_page_rank_ls", "contrastive_pca", "contrastive_pca_processing", "cor_module_check_epsilon", "cor_module_coremo_clustering", "cor_module_coremo_cor_sign", "cor_module_coremo_eigengene", "cor_module_coremo_stability", "cor_module_graph_check_res", "cor_module_graph_final_modules", "cor_module_processing", "cor_module_tom", "demo_single_cells", "densmap_sc", "densne_sc", "detect_adt_isotypes", "detect_raw_count_slot", "dgrdl_grid_search", "dgrdl_result", "dialogue_sc", "diffcor_module_processing", "diffuse_seed_nodes", "doublet_detection_boost_sc", "download_cd34_data", "download_cistarget_hg38", "download_demuxlet_pbmc", "download_dialogue_uc", "download_kang_pbmc", "download_marrow_cd34", "download_pbmc_1k_5p", "download_pbmc_batches", "download_pbmc_totalseq_data", "download_pbmc3k", "download_pbmc8k", "download_thymus_ageing", "drop_cols_sc", "extract_binary_heatmap_data", "extract_dot_plot_data", "extract_embedding_data", "extract_feature_pair", "extract_feature_plot_data", "extract_gene_expression", "extract_gene_violin_data", "extract_milo_plot_data", "extract_paga_plot_data", "f1_score_confusion_mat", "fast_cluster_sc", "fast_ica_rust", "fast_ica_rust_helper", "fast_mnn_sc", "fgsea_go_elim", "fgsea_simple_go_elim", "filter_similarities", "find_all_markers_sc", "find_clusters_sc", "find_hvg_batch_aware_sc", "find_hvg_sc", "find_markers_sc", "find_neighbours_sc", "find_rbh_communities", "find_specific_markers_sc", "find_threshold_otsu", "fit_residuals_sc", "fix_meta_data_column", "flag_cells", "forceatlas2_sc", "gene_ontology_data", "gene_set_proportions_sc", "GeneOntologyElim", "generate_bt_meta_cells_sc", "generate_cellsweep_test_data", "generate_dialogue_test_data", "generate_gene_module_data", "generate_hotspot_membership", "generate_knn_sc", "generate_ligand_target_influence", "generate_personalisation_vec", "generate_rbh_graph", "generate_sc_knn", "generate_seacells_sc", "generate_single_cell_test_data", "generate_single_cell_test_data_adt", "generate_supercells_sc", "generate_wnn_graph_sc", "get_adt_feature_info", "get_adt_names", "get_adt_sample_info", "get_available_embeddings", "get_best_model", "get_best_run", "get_c_pca_factors", "get_c_pca_loadings", "get_cell_indices", "get_cell_info", "get_cell_names", "get_cell_ranger_params", "get_cellphonedb_db", "get_cells_to_keep", "get_centroids_sc", "get_cistarget_res", "get_cor_graph", "get_data", "get_dge_effect_sizes", "get_dge_limma_voom", "get_dge_list", "get_dge_qc_plot", "get_diagnostics", "get_diffcor_graph", "get_differential_abundance_res", "get_diffusion_perms", "get_diffusion_vector", "get_embedding", "get_epsilon_res", "get_factors", "get_feature_mat", "get_fpkm_counts", "get_gene_indices", "get_gene_lengths", "get_gene_names", "get_gene_names_from_idx", "get_go_data_human", "get_grid_search_res", "get_h", "get_h5ad_dimensions", "get_hotspot_membership", "get_hvg", "get_hvg_data_sc", "get_ica_stability_res", "get_index_cells", "get_inflection_point", "get_influence", "get_kmeans_clusters", "get_knn_dist", "get_knn_mat", "get_knn_obj", "get_magic", "get_marker_comparisons", "get_marker_summary", "get_meta_cell_purity", "get_metadata", "get_miloR_abundances_sc", "get_model_fit", "get_modules", "get_nmf_gene_loadings", "get_nmf_modules", "get_nmf_sample_activity", "get_nmf_stability", "get_ontology_ancestry", "get_outputs", "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_pca_factors", "get_pca_loadings", "get_pca_singular_val", "get_pseudobulked_sc", "get_rbh_res", "get_residual_fit", "get_resolution_res", "get_results", "get_results.DialogueResult", "get_sc_available_features", "get_sc_cache", "get_sc_cache_status", "get_sc_counts", "get_sc_duckdb", "get_sc_map", "get_sc_obs", "get_sc_rust_ptr", "get_sc_var", "get_scores", "get_sim_matrix", "get_snf_adjcacency_mat", "get_snf_final_mat", "get_snf_params", "get_snn_graph", "get_stability", "get_symphony_hvg_names", "get_symphony_labels", "get_symphony_loadings", "get_symphony_z_corr", "get_tf_to_gene", "get_top_terms", "get_tpm_counts", "get_w", "gse_go_elim_method", "gse_go_elim_method_list", "gse_hypergeometric", "gse_hypergeometric_list", "harmony_sc", "harmony_v2_sc", "hedges_g_dge", "hotspot_autocor_sc", "hotspot_gene_cor_sc", "ica_evaluate_comp", "ica_optimal_ncomp", "ica_processing", "ica_stabilised_results", "identify_tf_to_genes", "install_agent_skill", "knn_graph_label_propagation", "lda_k_sweep", "leiden_clustering", "ligand_activity_scores", "load_existing", "load_go_human_data", "load_h5ad", "load_h5ad_norm", "load_mtx", "load_multi_h5ad", "load_multi_mtx", "load_multi_tenx_h5", "load_r_data", "load_sce", "load_seurat", "load_tenx_h5", "map_symphony_query", "mc_counts_to_list", "mc_get_clr_offsets", "meld_sc", "merge_meta_cells", "merge_sc_experiments", "merge_subset_obs", "meta_cells", "meta_cells_per_group", "MetaCells", "module_scores_sc", "modules_from_loadings", "nebula_mc", "nebula_sc", "network_diffusions", "NetworkDiffusions", "new_adt_counts_clr", "new_adt_counts_dsb", "new_bonsai_tree", "new_consensus_nmf_result", "new_dialogue_result", "new_gene_trends_res", "new_lda_k_sweep_result", "new_lda_result", "new_nmf_k_sweep_result", "new_nmf_result", "new_paga_res", "new_palantir_res", "new_sc_cache", "new_sc_hotspot_res", "new_sc_knn", "new_sc_list", "new_sc_magic", "new_sc_mapper", "new_sc_matrix", "new_sc_specific_markers", "new_scenic_grn", "new_stabilised_nmf_result", "nmf_bulk", "nmf_k_sweep_bulk", "nmf_k_sweep_sc", "nmf_sc", "normalise_bulk_dge", "ontology", "OntologySim", "ot_harmonic_score", "params_binary_heatmap", "params_blitzgsea", "params_boost", "params_bpca", "params_bulk_sparsity", "params_cistarget", "params_community_detection", "params_cor_graph", "params_coremo", "params_dgrdl", "params_dialogue_hlm", "params_dialogue_pmd", "params_dialogue_refine", "params_edger_ql", "params_fast_cluster_default", "params_graph_resolution", "params_gsea", "params_gsva", "params_hvg_defaults", "params_hvg_scran_defaults", "params_ica_general", "params_ica_ncomp", "params_ica_randomisation", "params_kmeans_defaults", "params_knn_defaults", "params_label_propagation", "params_lda", "params_ligand_target", "params_limma_voom", "params_meld", "params_module_membership", "params_nebula", "params_nmf_consensus", "params_nmf_hals", "params_norm_doublets_defaults", "params_pca_defaults", "params_ppca", "params_sc_apr", "params_sc_aucell", "params_sc_bbknn", "params_sc_bonsai", "params_sc_branch_selection", "params_sc_bt_metacells", "params_sc_cellphonedb", "params_sc_cellsweep", "params_sc_dsb", "params_sc_empty_droplets", "params_sc_fast_cluster", "params_sc_fastmnn", "params_sc_gene_trends", "params_sc_harmony", "params_sc_harmony_v2", "params_sc_hotspot", "params_sc_hvg", "params_sc_knn", "params_sc_magic", "params_sc_miloR", "params_sc_min_quality", "params_sc_mtx_io", "params_sc_neighbours", "params_sc_palantir", "params_sc_pca", "params_sc_sctransform", "params_sc_seacells", "params_sc_seurat_cca", "params_sc_seurat_rpca", "params_sc_supercell", "params_sc_synthetic_cellsweep", "params_sc_synthetic_data", "params_sc_synthetic_data_adt", "params_sc_synthetic_dialogue", "params_sc_vision", "params_sc_wnn", "params_scdblfinder", "params_scenic", "params_scenic_binarise", "params_scenic_extra_trees_defaults", "params_scenic_gradient_boosting_defaults", "params_scenic_random_forest_defaults", "params_scrublet", "params_sctype_cells", "params_snf", "params_ssgsea", "params_symphony_map", "params_synthetic_bulk_rnaseq", "per_cell_qc_outlier", "permute_seed_nodes", "phate_sc", "plot_boxplot_normalization", "plot_epsilon_res", "plot_hvgs", "plot_ica_ncomp_params", "plot_ica_stability_individual", "plot_optimal_cuts", "plot_pca", "plot_pca_res", "plot_preprocessing_genes", "plot_preprocessing_outliers", "plot_rbf_impact", "plot_resolution_res", "plot_voom_normalization", "pre_process_sim_onto", "prep_data_gower_hamming_dist", "prepare_cell_markers", "preprocess_bulk_coexp", "preprocess_bulk_dge", "prescan_h5ad_files", "prescan_mtx_dirs", "prescan_tenx_h5_files", "prioritise_interactions", "process_go_data", "pseudobulk_dge_sc", "qc_bulk_dge", "rbh_graph", "RbhGraph", "read_h5ad_metadata", "read_h5ad_x_summary", "read_motif_annotation_file", "read_motif_ranking", "read_multi_tenx_h5_adt", "read_tenx_h5_adt", "read_tenx_h5_metadata", "relayout_bonsai", "remove_adt_isotypes", "remove_knn", "remove_magic", "remove_residual_fit", "remove_samples", "remove_snn_graph", "rescue_cells", "reset_cells_to_keep", "robust_scale", "rs_2d_loess", "rs_adt_clr", "rs_aucell", "rs_batch_silhouette_width", "rs_bbknn", "rs_bbknn_filtering", "rs_blitzgsea_calibrate", "rs_blitzgsea_score", "rs_bonsai_layout", "rs_bpca", "rs_build_symphony_ref", "rs_calc_es", "rs_calc_gsea_stat_cumulative_batch", "rs_calc_gsea_stat_traditional_batch", "rs_calc_gsea_stats", "rs_calc_multi_level", "rs_calc_norm_factors", "rs_calculate_dge_mann_whitney", "rs_calculate_dge_one_vs_many", "rs_calculate_dge_one_vs_rest", "rs_cell_type_asw", "rs_cistarget", "rs_compare_knn", "rs_compute_cluster_expr_stats", "rs_constrained_page_rank", "rs_constrained_page_rank_list", "rs_contrastive_pca", "rs_cor", "rs_cor_upper_triangle", "rs_cor2", "rs_cos", "rs_count_zeroes", "rs_cov2cor", "rs_covariance", "rs_cpm", "rs_create_random_aucs", "rs_critval", "rs_critval_mat", "rs_dense_to_upper_triangle", "rs_dialogue_sc", "rs_differential_cor", "rs_dist", "rs_dsb", "rs_edger_ql", "rs_extract_counts_plots", "rs_extract_grouped_gene_stats", "rs_extract_several_genes_plots", "rs_fast_auc", "rs_fast_cluster_sc", "rs_fast_cluster_sc_grid", "rs_fast_ica", "rs_fdr_adjustment", "rs_filter_by_expr", "rs_filter_onto_sim", "rs_gene_trends", "rs_generate_bulk_rnaseq", "rs_generate_ligand_target_influence", "rs_geom_elim_fgsea_simple", "rs_get_gs_indices", "rs_get_metacells_bootstrapped", "rs_get_seacells", "rs_gower_dist", "rs_graph_connectivity", "rs_gse_geom_elim", "rs_gse_geom_elim_list", "rs_gsva", "rs_h5ad_data", "rs_hamming_dist", "rs_harmony", "rs_harmony_v2", "rs_hedges_g", "rs_hotspot_autocor", "rs_hotspot_cluster_genes", "rs_hotspot_gene_cor", "rs_hypergeom_test", "rs_hypergeom_test_list", "rs_ica_iters", "rs_ica_iters_cv", "rs_importance_threshold", "rs_jaccard_row_integers", "rs_kbet", "rs_knn_label_propagation", "rs_knn_mat_to_edge_list", "rs_knn_mat_to_edge_pairs", "rs_lda", "rs_lda_k_sweep", "rs_leiden_clustering", "rs_ligand_activity_scores", "rs_limma_voom", "rs_lisi", "rs_mad_outlier", "rs_magic_impute", "rs_make_milor_nhoods", "rs_mc_aucell", "rs_mc_bonsai", "rs_mc_dialogue", "rs_mc_fit_residuals", "rs_mc_hotspot_autocor", "rs_mc_hotspot_gene_cor", "rs_mc_hvg", "rs_mc_pca", "rs_mc_pca_residuals", "rs_mc_residual_variance", "rs_mc_scenic", "rs_mc_vision", "rs_mc_vision_with_autocorrelation", "rs_meld_sc", "rs_metacell_compactness", "rs_metacell_density", "rs_metacell_separation", "rs_mitch_calc", "rs_mnn", "rs_module_scoring", "rs_mutual_info", "rs_nebula_mc", "rs_nebula_sc", "rs_nmf_consensus_bulk", "rs_nmf_consensus_mc", "rs_nmf_consensus_sc", "rs_nmf_k_sweep_bulk", "rs_nmf_k_sweep_mc", "rs_nmf_k_sweep_sc", "rs_nmf_multi_bulk", "rs_nmf_multi_mc", "rs_nmf_multi_sc", "rs_nmf_single_bulk", "rs_nmf_single_mc", "rs_nmf_single_sc", "rs_onto_semantic_sim", "rs_onto_semantic_sim_mat", "rs_onto_sim_wang", "rs_onto_sim_wang_mat", "rs_ot_harmonic_sum", "rs_paga", "rs_page_rank", "rs_page_rank_parallel", "rs_pairwise_gene_cors", "rs_pairwise_gene_cors_mc", "rs_palantir", "rs_pcr", "rs_phyper", "rs_pointwise_mutual_info", "rs_ppca", "rs_prcomp", "rs_prepare_gsva_gs", "rs_prepare_whitening", "rs_pseudobulk_cells_dense", "rs_pseudobulk_cells_sparse", "rs_random_svd", "rs_range_norm", "rs_rank_matrix_col", "rs_rank_matrix_col_stable", "rs_rbf_function", "rs_rbf_function_mat", "rs_rbf_iterate_epsilons", "rs_rbh_cor", "rs_rbh_sets", "rs_read_tenx_h5_modality", "rs_regulon_thresholds", "rs_remove_batch_effect", "rs_sample_ids_for_cell_types", "rs_save_h5ad", "rs_sc_bonsai", "rs_sc_cellphonedb", "rs_sc_doublet_detection", "rs_sc_fit_residuals", "rs_sc_gene_store_to_cell_store", "rs_sc_get_gene_set_perc", "rs_sc_get_top_genes_perc", "rs_sc_hvg", "rs_sc_hvg_batch_aware", "rs_sc_infer_empty_droplets", "rs_sc_knn", "rs_sc_knn_w_dist", "rs_sc_otsu_method", "rs_sc_pca", "rs_sc_pca_residuals", "rs_sc_pca_sparse", "rs_sc_residual_variance", "rs_sc_scdblfinder", "rs_sc_scrublet", "rs_sc_snn", "rs_sc_type", "rs_sc_type_assign_cells", "rs_sc_type_cluster_assignment", "rs_scenic_gene_filter", "rs_scenic_grn", "rs_scenic_grn_streaming", "rs_sct_corrected_counts", "rs_set_similarity", "rs_set_similarity_list", "rs_set_similarity_list2", "rs_seurat_cca", "rs_seurat_rpca", "rs_simple_and_multi_err", "rs_simulate_dropouts", "rs_singscore_multi", "rs_singscore_permutation_test", "rs_singscore_single", "rs_snf", "rs_snf_affinity_cat", "rs_snf_affinity_continuous", "rs_snf_affinity_mixed", "rs_sparse_dict_dgrdl", "rs_sparse_dict_dgrdl_grid_search", "rs_spatial_fdr", "rs_spectral_clustering", "rs_spectral_clustering_sim", "rs_split_cor_signs", "rs_ssgsea", "rs_supercell", "rs_symphony_map_query", "rs_synthetic_sc_adt_with_cell_types", "rs_synthetic_sc_cellsweep_data", "rs_synthetic_sc_data_with_cell_types", "rs_synthetic_sc_dialogue_data", "rs_tied_diffusion_parallel", "rs_tom", "rs_top_k_targets", "rs_transfer_labels_symphony", "rs_upper_triangle_to_dense", "rs_upper_triangle_to_sparse", "rs_vision", "rs_vision_with_autocorrelation", "rs_voom_normalise", "rs_wnn", "run_bpca", "run_cell_qc", "run_cell_qc_fixed", "run_cistarget", "run_edger_ql", "run_gene_trends_sc", "run_lda", "run_limma_voom", "run_magic_sc", "run_paga_sc", "run_palantir_sc", "run_ppca", "run_snf", "save_h5ad", "save_sc_exp_to_disk", "sc_knn_to_nearest_neighbours", "sc_pipeline", "scdblfinder_sc", "scenic_gene_filter_sc", "scenic_grn_sc", "score_clusters", "scrublet_sc", "sct_corrected_counts_sc", "set_bonsai_embedding", "set_cell_mapping", "set_cells_to_keep", "set_embedding", "set_gene_mapping", "set_hvg", "set_knn", "set_magic", "set_pca_factors", "set_pca_loadings", "set_pca_singular_vals", "set_residual_fit", "set_sc_new_obs_col", "set_sc_new_obs_col_multiple", "set_sc_new_var_cols", "set_snn_graph", "setnames_sc", "seurat_cca_sc", "seurat_rpca_sc", "SimilarityNetworkFusion", "simplify_hypergeom_res", "simulate_dropouts", "single_cell_exp", "SingleCellCountData", "SingleCellDuckDB", "SingleCellDuckDBBase", "SingleCells", "SingleCellsMultiModal", "SingleCellsSubset", "snf", "sparse_list_to_mat", "stabilised_nmf_bulk", "stabilised_nmf_sc", "step_bbknn_sc", "step_clusters_sc", "step_fast_mnn_sc", "step_harmony_sc", "step_harmony_v2_sc", "step_hvg_sc", "step_metacells_sc", "step_neighbours_sc", "step_pca_sc", "stream_h5ad", "SymphonyReference", "synthetic_bulk_cor_matrix", "synthetic_c_pca_data", "synthetic_signal_matrix", "test_nhoods", "tf_to_genes_correlations", "tf_to_genes_motif_enrichment", "tied_diffusion", "to_snake_case", "top_genes_perc_sc", "transfer_labels_symphony", "tsne_sc", "umap_sc", "update_metadata_values", "upper_triangle_to_sparse", "upper_triangular_sym_mat", "validate_pipeline", "vision_sc", "vision_w_autocor_sc", "write_cellranger_output", "write_h5ad_sc", "write_h5ad_sc_dense", "write_tenx_h5_sc" ], "_datasets": [ { "name": "cell_cycle_genes", "title": "Cell cycle genes", "object": "cell_cycle_genes", "class": [ "data.table", "data.frame" ], "fields": [ "hgnc_symbol", "ensembl_gene_id", "set" ], "rows": 99, "table": true, "tojson": true } ], "_help": [ { "page": "grapes-greater-than-greater-than-grapes", "title": "Append a step to a pipeline", "topics": [ "%>>%" ] }, { "page": "add_adt_counts_sc", "title": "Add ADT counts to 'SingleCellsMultiModal'", "topics": [ "add_adt_counts_sc" ] }, { "page": "add_new_metadata", "title": "Replace the meta data", "topics": [ "add_new_metadata" ] }, { "page": "add_nhoods_info", "title": "Add neighbourhood info on majority cell type", "topics": [ "add_nhoods_info", "add_nhoods_info.miloR" ] }, { "page": "add_sc_new_obs", "title": "Add an obs table derived from a method to the SingleCells.", "topics": [ "add_sc_new_obs" ] }, { "page": "add_snf_data_modality", "title": "Add a data modality for SNF generation", "topics": [ "add_snf_data_modality" ] }, { "page": "add_symphony_labels", "title": "Add labels to a Symphony reference post-hoc", "topics": [ "add_symphony_labels" ] }, { "page": "AnnDataParser", "title": "Class for Anndata", "topics": [ "AnnDataParser" ] }, { "page": "apply_pipeline", "title": "Apply a pipeline to a single cell object", "topics": [ "apply_pipeline" ] }, { "page": "apply_pipeline_per_group", "title": "Apply a pipeline independently to each group of a 'SingleCells' object", "topics": [ "apply_pipeline_per_group" ] }, { "page": "archive_sc_exp", "title": "Archive the on-disk counts for cold storage", "topics": [ "archive_sc_exp" ] }, { "page": "as_ligand_target_influence", "title": "Wrap an existing ligand-target matrix as a 'LigandTargetInfluence'", "topics": [ "as_ligand_target_influence" ] }, { "page": "assert_sc_state", "title": "Assert that cached artefacts still match the object's state", "topics": [ "assert_sc_state" ] }, { "page": "assign_sc_type", "title": "Assign cell types per cell based on ScType", "topics": [ "assign_sc_type" ] }, { "page": "aucell_sc", "title": "Calculate AUC scores (akin to AUCell)", "topics": [ "aucell_sc" ] }, { "page": "batch_correction_bulk_dge", "title": "Run a linear batch correction", "topics": [ "batch_correction_bulk_dge" ] }, { "page": "bbknn_sc", "title": "Run BBKNN", "topics": [ "bbknn_sc" ] }, { "page": "binarise_regulon_activity", "title": "Binarise regulon activity into on/off calls", "topics": [ "binarise_regulon_activity" ] }, { "page": "blitzgsea_calibrate", "title": "Calibrate the blitzGSEA null model for a signature", "topics": [ "blitzgsea_calibrate" ] }, { "page": "bonsai_sc", "title": "Build a Bonsai tree over the cells", "topics": [ "bonsai_sc" ] }, { "page": "build_regulons", "title": "Build the final regulons", "topics": [ "build_regulons", "build_regulons.ScenicGrn" ] }, { "page": "build_symphony_ref", "title": "Build a Symphony reference from a SingleCells object", "topics": [ "build_symphony_ref" ] }, { "page": "bulk_dge_from_h5ad", "title": "Wrapper function to generate BulkDge object from h5ad", "topics": [ "bulk_dge_from_h5ad" ] }, { "page": "BulkCoExp", "title": "Bulk RNAseq co-expression modules", "topics": [ "BulkCoExp" ] }, { "page": "BulkDge", "title": "Bulk RNAseq differential gene expression class", "topics": [ "BulkDge" ] }, { "page": "c_pca_plot_alphas", "title": "Plot various alphas for the contrastive PCA", "topics": [ "c_pca_plot_alphas" ] }, { "page": "calc_blitzgsea", "title": "Bixverse implementation of the blitzGSEA algorithm", "topics": [ "calc_blitzgsea" ] }, { "page": "calc_diffusion_coordinates", "title": "Calculate diffusion coordinates", "topics": [ "calc_diffusion_coordinates" ] }, { "page": "calc_fgsea", "title": "Bixverse implementation of the fgsea algorithm", "topics": [ "calc_fgsea" ] }, { "page": "calc_fgsea_simple", "title": "Bixverse implementation of the simple fgsea algorithm", "topics": [ "calc_fgsea_simple" ] }, { "page": "calc_gsea_traditional", "title": "Bixverse implementation of the traditional GSEA algorithm", "topics": [ "calc_gsea_traditional" ] }, { "page": "calc_gsva", "title": "Bixverse implementation of GSVA", "topics": [ "calc_gsva" ] }, { "page": "calc_knn_metrics", "title": "Calculate recall at k and distance ratio", "topics": [ "calc_knn_metrics" ] }, { "page": "calc_manifold_metrics", "title": "Calculate manifold metrics", "topics": [ "calc_manifold_metrics" ] }, { "page": "calc_meta_cell_compactness", "title": "Meta cell compactness in a diffusion map", "topics": [ "calc_meta_cell_compactness" ] }, { "page": "calc_meta_cell_purity", "title": "Calculate meta cell purity", "topics": [ "calc_meta_cell_purity" ] }, { "page": "calc_meta_cell_separation", "title": "Meta cell separation in a diffusion map", "topics": [ "calc_meta_cell_separation" ] }, { "page": "calc_mitch", "title": "Calculate a mitch gene set enrichments on contrast", "topics": [ "calc_mitch" ] }, { "page": "calc_sc_type_scores", "title": "Calculate ScType scores per cell", "topics": [ "calc_sc_type_scores" ] }, { "page": "calc_singscore", "title": "Bixverse implementation of singscore (single gene set)", "topics": [ "calc_singscore" ] }, { "page": "calc_singscore_multi", "title": "Bixverse implementation of singscore (multiple gene sets)", "topics": [ "calc_singscore_multi" ] }, { "page": "calc_singscore_rank", "title": "Rank an expression matrix for singscore", "topics": [ "calc_singscore_rank" ] }, { "page": "calc_ssgsea", "title": "Bixverse implementation of ssGSEA", "topics": [ "calc_ssgsea" ] }, { "page": "calculate_all_dges", "title": "Calculate all possible DGE variants (DEPRECATED!)", "topics": [ "calculate_all_dges" ] }, { "page": "calculate_batch_asw_sc", "title": "Calculate batch average silhouette width", "topics": [ "calculate_batch_asw_sc" ] }, { "page": "calculate_cell_type_asw_sc", "title": "Calculate cell type average silhouette width", "topics": [ "calculate_cell_type_asw_sc" ] }, { "page": "calculate_critical_value", "title": "Calculates the critical value", "topics": [ "calculate_critical_value" ] }, { "page": "calculate_dge_hedges", "title": "Calculates the Hedge's G effect size", "topics": [ "calculate_dge_hedges" ] }, { "page": "calculate_dge_limma", "title": "Calculates the Limma Voom DGE", "topics": [ "calculate_dge_limma" ] }, { "page": "calculate_diffusion_auc", "title": "Calculate the AUROC for a diffusion score", "topics": [ "calculate_diffusion_auc" ] }, { "page": "calculate_effect_size", "title": "Calculate the Hedge's G effect between two matrices", "topics": [ "calculate_effect_size" ] }, { "page": "calculate_graph_connectivity_sc", "title": "Calculate the graph connectivity per cell type", "topics": [ "calculate_graph_connectivity_sc" ] }, { "page": "calculate_information_content", "title": "Calculate the information content for each ontology term", "topics": [ "calculate_information_content" ] }, { "page": "calculate_integration_metrics_sc", "title": "Calculate a summary of integration metrics", "topics": [ "calculate_integration_metrics_sc" ] }, { "page": "calculate_kbet_sc", "title": "Calculate kBET scores", "topics": [ "calculate_kbet_sc" ] }, { "page": "calculate_lisi_sc", "title": "Calculate LISI scores (iLISI or cLISI)", "topics": [ "calculate_lisi_sc" ] }, { "page": "calculate_pca_adt_sc", "title": "Calculate the PCA on top of the normalised ADT counts", "topics": [ "calculate_pca_adt_sc" ] }, { "page": "calculate_pca_bulk_dge", "title": "Calculate PCA on the expression.", "topics": [ "calculate_pca_bulk_dge" ] }, { "page": "calculate_pca_sc", "title": "Run PCA for single cell", "topics": [ "calculate_pca_sc" ] }, { "page": "calculate_pcr_sc", "title": "Calculate the principal component regression on batch", "topics": [ "calculate_pcr_sc" ] }, { "page": "calculate_rpkm", "title": "RPKM calculation", "topics": [ "calculate_rpkm" ] }, { "page": "calculate_semantic_sim", "title": "Calculate the Resnik or Lin semantic similarity", "topics": [ "calculate_semantic_sim" ] }, { "page": "calculate_semantic_sim_mat", "title": "Calculate the Resnik or Lin semantic similarity matrix", "topics": [ "calculate_semantic_sim_mat" ] }, { "page": "calculate_semantic_sim_onto", "title": "Calculate the Resnik or Lin semantic similarity for an ontology.", "topics": [ "calculate_semantic_sim_onto" ] }, { "page": "calculate_sparsity_stats", "title": "Helper function to calculate the induced sparsity", "topics": [ "calculate_sparsity_stats" ] }, { "page": "calculate_tom", "title": "Calculate the TOM from a correlation matrix", "topics": [ "calculate_tom" ] }, { "page": "calculate_tom_from_exp", "title": "Calculate the TOM from an expression matrix", "topics": [ "calculate_tom_from_exp" ] }, { "page": "calculate_tpm", "title": "TPM calculation", "topics": [ "calculate_tpm" ] }, { "page": "calculate_wang_sim", "title": "Calculate the Wang similarities between terms", "topics": [ "calculate_wang_sim" ] }, { "page": "calculate_wang_sim_mat", "title": "Calculate the Wang similarity matrix", "topics": [ "calculate_wang_sim_mat" ] }, { "page": "calculate_wang_sim_onto", "title": "Calculate the Wang similarity for an ontology.", "topics": [ "calculate_wang_sim_onto" ] }, { "page": "call_doublets_manual", "title": "Manually readjust Scrublet doublet call thresholds", "topics": [ "call_doublets_manual" ] }, { "page": "cell_cycle_genes", "title": "Cell cycle genes", "topics": [ "cell_cycle_genes" ] }, { "page": "cellphonedb_sc", "title": "CellPhoneDB ligand-receptor analysis", "topics": [ "cellphonedb_sc" ] }, { "page": "cellsweep_sc", "title": "Remove ambient and bulk contamination with CellSweep", "topics": [ "cellsweep_sc" ] }, { "page": "change_gene_identifier", "title": "Change the primary gene identifier of BulkDge", "topics": [ "change_gene_identifier" ] }, { "page": "check_sc_state", "title": "Check that cached artefacts still match the object's state", "topics": [ "check_sc_state" ] }, { "page": "community_detection", "title": "Identify privileged communities based on a given diffusion vector", "topics": [ "community_detection" ] }, { "page": "compute_expression_info_sc", "title": "Compute per-cluster mean expression and expressing fraction for a gene set", "topics": [ "compute_expression_info_sc" ] }, { "page": "consensus_nmf_bulk", "title": "Run consensus NMF on a BulkCoExp", "topics": [ "consensus_nmf_bulk" ] }, { "page": "consensus_nmf_sc", "title": "Run consensus NMF on single cell or meta cell data", "topics": [ "consensus_nmf_sc" ] }, { "page": "constrained_page_rank", "title": "Constrained personalised page rank", "topics": [ "constrained_page_rank" ] }, { "page": "constrained_page_rank_ls", "title": "Constrained personalised page rank over a list", "topics": [ "constrained_page_rank_ls" ] }, { "page": "contrastive_pca", "title": "Apply contrastive PCA.", "topics": [ "contrastive_pca" ] }, { "page": "contrastive_pca_processing", "title": "Prepare class for contrastive PCA", "topics": [ "contrastive_pca_processing" ] }, { "page": "cor_module_check_epsilon", "title": "Iterate through different epsilon parameters", "topics": [ "cor_module_check_epsilon" ] }, { "page": "cor_module_coremo_clustering", "title": "Generates CoReMo-based gene modules", "topics": [ "cor_module_coremo_clustering" ] }, { "page": "cor_module_coremo_cor_sign", "title": "Split CoReMo modules by correlation sign", "topics": [ "cor_module_coremo_cor_sign" ] }, { "page": "cor_module_coremo_eigengene", "title": "Calculate Eigengenes for CoReMo modules", "topics": [ "cor_module_coremo_eigengene" ] }, { "page": "cor_module_coremo_stability", "title": "Assesses CoReMo-based gene module stability", "topics": [ "cor_module_coremo_stability" ] }, { "page": "cor_module_graph_check_res", "title": "Iterate through Leiden resolutions for graph-based community detection.", "topics": [ "cor_module_graph_check_res" ] }, { "page": "cor_module_graph_final_modules", "title": "Identify correlation-based gene modules via graphs", "topics": [ "cor_module_graph_final_modules" ] }, { "page": "cor_module_processing", "title": "Prepare correlation-based module detection", "topics": [ "cor_module_processing" ] }, { "page": "cor_module_tom", "title": "Update the correlation matrix to a TOM", "topics": [ "cor_module_tom" ] }, { "page": "demo_single_cells", "title": "Ready-made 'SingleCells' object for examples and tests", "topics": [ "demo_single_cells" ] }, { "page": "densmap_sc", "title": "Run densMAP on a SingleCells/MetaCells object", "topics": [ "densmap_sc" ] }, { "page": "densne_sc", "title": "Run den-SNE on a SingleCells/MetaCells object", "topics": [ "densne_sc" ] }, { "page": "detect_adt_isotypes", "title": "Detect likely isotype-control features by name pattern", "topics": [ "detect_adt_isotypes" ] }, { "page": "dgrdl_grid_search", "title": "Grid search over DGRDL parameters", "topics": [ "dgrdl_grid_search" ] }, { "page": "dgrdl_result", "title": "Run DGRDL with the specified parameters", "topics": [ "dgrdl_result" ] }, { "page": "dialogue_sc", "title": "Find multicellular programmes with DIALOGUE", "topics": [ "dialogue_sc" ] }, { "page": "diffcor_module_processing", "title": "Prepare differential correlation-based module detection", "topics": [ "diffcor_module_processing" ] }, { "page": "diffuse_seed_nodes", "title": "Diffuse seed genes over a network", "topics": [ "diffuse_seed_nodes" ] }, { "page": "doublet_detection_boost_sc", "title": "Doublet detection with boosted doublet classification", "topics": [ "doublet_detection_boost_sc" ] }, { "page": "download_cd34_data", "title": "Download the CD34 example data from SEACells", "topics": [ "download_cd34_data" ] }, { "page": "download_cistarget_hg38", "title": "Download CisTarget reference files for human (hg38)", "topics": [ "download_cistarget_hg38" ] }, { "page": "download_demuxlet_pbmc", "title": "Download PBMCs with demuxlet doublet information", "topics": [ "download_demuxlet_pbmc" ] }, { "page": "download_dialogue_uc", "title": "Download the ulcerative colitis example data for DIALOGUE", "topics": [ "download_dialogue_uc" ] }, { "page": "download_kang_pbmc", "title": "Download the Kang, et al. IFN-beta stimulated PBMC data", "topics": [ "download_kang_pbmc" ] }, { "page": "download_marrow_cd34", "title": "Download the marrow CD34 example data from Palantir", "topics": [ "download_marrow_cd34" ] }, { "page": "download_pbmc_1k_5p", "title": "Download the raw PBMC 1k 5' matrix from 10x Genomics", "topics": [ "download_pbmc_1k_5p" ] }, { "page": "download_pbmc_batches", "title": "Download two different PBMC data sets for batch correction testing", "topics": [ "download_pbmc_batches" ] }, { "page": "download_pbmc_totalseq_data", "title": "Download the PBMC TotalSeq data with ADT counts", "topics": [ "download_pbmc_totalseq_data" ] }, { "page": "download_pbmc3k", "title": "Download PBMC3K data from Zenodo", "topics": [ "download_pbmc3k" ] }, { "page": "download_pbmc8k", "title": "Download PBMC8K data from Zenodo", "topics": [ "download_pbmc8k" ] }, { "page": "download_thymus_ageing", "title": "Download the Baran-Gale, et al. ageing thymus data", "topics": [ "download_thymus_ageing" ] }, { "page": "drop_cols_sc", "title": "Drop columns from the obs or var table", "topics": [ "drop_cols_sc" ] }, { "page": "extract_binary_heatmap_data", "title": "Extract plot-ready data for a binary heatmap", "topics": [ "extract_binary_heatmap_data" ] }, { "page": "extract_dot_plot_data", "title": "Extract grouped gene statistics for dot plots", "topics": [ "extract_dot_plot_data" ] }, { "page": "extract_embedding_data", "title": "Extract embedding coordinates for plotting", "topics": [ "extract_embedding_data" ] }, { "page": "extract_feature_pair", "title": "Extract a pair of features for scatter / hex plots", "topics": [ "extract_feature_pair" ] }, { "page": "extract_feature_plot_data", "title": "Extract per-cell expression mapped onto an embedding", "topics": [ "extract_feature_plot_data" ] }, { "page": "extract_gene_expression", "title": "Extract normalised gene expression for plotting", "topics": [ "extract_gene_expression" ] }, { "page": "extract_gene_violin_data", "title": "Extract per-cell expression grouped for violin plots", "topics": [ "extract_gene_violin_data" ] }, { "page": "extract_milo_plot_data", "title": "Extract the miloR neighbourhood graph positioned on an embedding", "topics": [ "extract_milo_plot_data" ] }, { "page": "extract_paga_plot_data", "title": "Extract the PAGA graph positioned on an embedding", "topics": [ "extract_paga_plot_data" ] }, { "page": "f1_score_confusion_mat", "title": "F1 scores on top of a confusion matrix", "topics": [ "f1_score_confusion_mat" ] }, { "page": "fast_cluster_sc", "title": "Run fast Louvain clustering on a SingleCells object", "topics": [ "fast_cluster_sc" ] }, { "page": "fast_ica_rust", "title": "Fast ICA via Rust", "topics": [ "fast_ica_rust" ] }, { "page": "fast_ica_rust_helper", "title": "Fast ICA via Rust from processed data", "topics": [ "fast_ica_rust_helper" ] }, { "page": "fast_mnn_sc", "title": "Run fastMNN", "topics": [ "fast_mnn_sc" ] }, { "page": "fgsea_go_elim", "title": "Run GO enrichment with elimination method over a continuous vectors", "topics": [ "fgsea_go_elim" ] }, { "page": "fgsea_simple_go_elim", "title": "Run GO enrichment with elimination with fgsea simple", "topics": [ "fgsea_simple_go_elim" ] }, { "page": "filter_similarities", "title": "Filter the calculated similarities", "topics": [ "filter_similarities" ] }, { "page": "find_all_markers_sc", "title": "Find all markers", "topics": [ "find_all_markers_sc" ] }, { "page": "find_clusters_sc", "title": "Graph-based clustering of cells on the sNN graph", "topics": [ "find_clusters_sc" ] }, { "page": "find_hvg_batch_aware_sc", "title": "Identify HVGs (batch aware)", "topics": [ "find_hvg_batch_aware_sc" ] }, { "page": "find_hvg_sc", "title": "Identify HVGs", "topics": [ "find_hvg_sc" ] }, { "page": "find_markers_sc", "title": "Calculate DGE between two cell groups", "topics": [ "find_markers_sc" ] }, { "page": "find_neighbours_sc", "title": "Find the neighbours for single cell.", "topics": [ "find_neighbours_sc" ] }, { "page": "find_rbh_communities", "title": "Find RBH communities", "topics": [ "find_rbh_communities" ] }, { "page": "find_specific_markers_sc", "title": "Find markers that are specific to a cell group", "topics": [ "find_specific_markers_sc" ] }, { "page": "find_threshold_otsu", "title": "Find a threshold via the Otsu method", "topics": [ "find_threshold_otsu" ] }, { "page": "fit_residuals_sc", "title": "Fit a residual model for single cell data", "topics": [ "fit_residuals_sc" ] }, { "page": "fix_meta_data_column", "title": "Helper to fix meta-data columns to be R conform", "topics": [ "fix_meta_data_column" ] }, { "page": "flag_cells", "title": "Add hard-threshold flags to a CellQc object", "topics": [ "flag_cells" ] }, { "page": "forceatlas2_sc", "title": "Run ForceAtlas2 on a SingleCells/MetaCells object", "topics": [ "forceatlas2_sc" ] }, { "page": "gene_set_proportions_sc", "title": "Calculate the proportions of reads for specific gene sets", "topics": [ "gene_set_proportions_sc" ] }, { "page": "GeneOntologyElim", "title": "Gene Ontology data", "topics": [ "GeneOntologyElim" ] }, { "page": "generate_bt_meta_cells_sc", "title": "Generate meta cells based on hdWGCNA and return a 'MetaCells' object", "topics": [ "generate_bt_meta_cells_sc" ] }, { "page": "generate_cellsweep_test_data", "title": "Single cell test data with a planted ambient profile", "topics": [ "generate_cellsweep_test_data" ] }, { "page": "generate_dialogue_test_data", "title": "Single cell test data with a planted multicellular programme", "topics": [ "generate_dialogue_test_data" ] }, { "page": "generate_gene_module_data", "title": "Generates synthetic gene module data.", "topics": [ "generate_gene_module_data" ] }, { "page": "generate_hotspot_membership", "title": "Identify hotspot gene clusters", "topics": [ "generate_hotspot_membership", "generate_hotspot_membership.Hotspot" ] }, { "page": "generate_knn_sc", "title": "Generate a 'SingleCellNearestNeighbour' from a single cell class", "topics": [ "generate_knn_sc" ] }, { "page": "generate_ligand_target_influence", "title": "Generate the ligand to target influence matrix", "topics": [ "generate_ligand_target_influence" ] }, { "page": "generate_personalisation_vec", "title": "Helper function to create personalisation vectors", "topics": [ "generate_personalisation_vec" ] }, { "page": "generate_rbh_graph", "title": "Generate an RBH graph.", "topics": [ "generate_rbh_graph" ] }, { "page": "generate_sc_knn", "title": "Generate a new SingleCellNearestNeighbour from data", "topics": [ "generate_sc_knn" ] }, { "page": "generate_seacells_sc", "title": "Generate meta cells based on SEACells and return a 'MetaCells' object", "topics": [ "generate_seacells_sc" ] }, { "page": "generate_single_cell_test_data", "title": "Single cell test data", "topics": [ "generate_single_cell_test_data" ] }, { "page": "generate_supercells_sc", "title": "Generate SuperCells and return a 'MetaCells' object", "topics": [ "generate_supercells_sc" ] }, { "page": "generate_wnn_graph_sc", "title": "Generate a weighted nearest neighbour (WNN) graph", "topics": [ "generate_wnn_graph_sc" ] }, { "page": "get_adt_feature_info", "title": "Get the ADT feature info", "topics": [ "get_adt_feature_info", "get_adt_feature_info.ADTCounts" ] }, { "page": "get_adt_names", "title": "Get the ADT feature names", "topics": [ "get_adt_names", "get_adt_names.ADTCounts" ] }, { "page": "get_adt_sample_info", "title": "Get the ADT sample info", "topics": [ "get_adt_sample_info", "get_adt_sample_info.ADTCounts" ] }, { "page": "get_available_embeddings", "title": "Get the available embeddings", "topics": [ "get_available_embeddings", "get_available_embeddings.MetaCells", "get_available_embeddings.ScCache", "get_available_embeddings.SingleCells", "get_available_embeddings.SingleCellsSubset" ] }, { "page": "get_best_model", "title": "Get the selected model from an LDA topic count sweep", "topics": [ "get_best_model", "get_best_model.LdaKSweepResult" ] }, { "page": "get_best_run", "title": "Get the best run from a stabilised NMF result", "topics": [ "get_best_run", "get_best_run.StabilisedNmfResult" ] }, { "page": "get_c_pca_factors", "title": "Get the contrastive PCA factors", "topics": [ "get_c_pca_factors" ] }, { "page": "get_c_pca_loadings", "title": "Get the contrastive PCA loadings", "topics": [ "get_c_pca_loadings" ] }, { "page": "get_cell_indices", "title": "Get the index position for a gene", "topics": [ "get_cell_indices", "get_cell_indices.MetaCells", "get_cell_indices.ScMap", "get_cell_indices.SingleCells", "get_cell_indices.SingleCellsSubset" ] }, { "page": "get_cell_info", "title": "Get the cell idx (R-based) and cell names", "topics": [ "get_cell_info", "get_cell_info.ScMap", "get_cell_info.SingleCells" ] }, { "page": "get_cell_names", "title": "Get the cell names", "topics": [ "get_cell_names", "get_cell_names.ScMap", "get_cell_names.SingleCells", "get_cell_names.SingleCellsSubset" ] }, { "page": "get_cell_ranger_params", "title": "Helper to generate cell ranger input parameters", "topics": [ "get_cell_ranger_params" ] }, { "page": "get_cellphonedb_db", "title": "Download the CellPhoneDB ligand-receptor database", "topics": [ "get_cellphonedb_db" ] }, { "page": "get_cells_to_keep", "title": "Get the cells to keep", "topics": [ "get_cells_to_keep", "get_cells_to_keep.ScMap", "get_cells_to_keep.SingleCells", "get_cells_to_keep.SingleCellsSubset" ] }, { "page": "get_centroids_sc", "title": "Get k-means centroids from a fast cluster result", "topics": [ "get_centroids_sc", "get_centroids_sc.SingleCellFastClusters" ] }, { "page": "get_cistarget_res", "title": "Extract the TF to gene data from the ScenicGrn object", "topics": [ "get_cistarget_res", "get_cistarget_res.ScenicGrn" ] }, { "page": "get_cor_graph", "title": "Get correlation-based graph", "topics": [ "get_cor_graph" ] }, { "page": "get_data", "title": "Get the ready obs data from various sub method", "topics": [ "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" ] }, { "page": "get_dge_effect_sizes", "title": "Return the effect size results", "topics": [ "get_dge_effect_sizes" ] }, { "page": "get_dge_limma_voom", "title": "Return the Limma Voom results", "topics": [ "get_dge_limma_voom" ] }, { "page": "get_dge_list", "title": "Return the DGEList", "topics": [ "get_dge_list" ] }, { "page": "get_dge_qc_plot", "title": "Return QC plots", "topics": [ "get_dge_qc_plot" ] }, { "page": "get_diagnostics", "title": "Get diagnostics from a BulkModuleResult", "topics": [ "get_diagnostics" ] }, { "page": "get_diffcor_graph", "title": "Get differential correlation-based graph", "topics": [ "get_diffcor_graph" ] }, { "page": "get_differential_abundance_res", "title": "Get the differential abundance results", "topics": [ "get_differential_abundance_res", "get_differential_abundance_res.miloR" ] }, { "page": "get_diffusion_perms", "title": "Get the diffusion permutations", "topics": [ "get_diffusion_perms" ] }, { "page": "get_diffusion_vector", "title": "Get the diffusion vector", "topics": [ "get_diffusion_vector" ] }, { "page": "get_embedding", "title": "Get the embedding", "topics": [ "get_embedding", "get_embedding.MetaCells", "get_embedding.ScCache", "get_embedding.SingleCells", "get_embedding.SingleCellsSubset" ] }, { "page": "get_epsilon_res", "title": "Return the epsilon data", "topics": [ "get_epsilon_res" ] }, { "page": "get_factors", "title": "Get factor matrices from a BulkModuleResult", "topics": [ "get_factors" ] }, { "page": "get_feature_mat", "title": "Get the feature matrix used for the classifier", "topics": [ "get_feature_mat", "get_feature_mat.ScDblFinderRes" ] }, { "page": "get_fpkm_counts", "title": "Return the FPKM-normalised counts", "topics": [ "get_fpkm_counts" ] }, { "page": "get_gene_indices", "title": "Get the index position for a gene", "topics": [ "get_gene_indices", "get_gene_indices.MetaCells", "get_gene_indices.ScMap", "get_gene_indices.SingleCells", "get_gene_indices.SingleCellsSubset" ] }, { "page": "get_gene_lengths", "title": "Get the gene lengths", "topics": [ "get_gene_lengths", "get_gene_lengths,bixverse::BulkCoExp-method" ] }, { "page": "get_gene_names", "title": "Get the gene names", "topics": [ "get_gene_names", "get_gene_names.ScMap", "get_gene_names.SingleCells", "get_gene_names.SingleCellsSubset" ] }, { "page": "get_gene_names_from_idx", "title": "Get the gene names based on the gene idx", "topics": [ "get_gene_names_from_idx", "get_gene_names_from_idx.ScMap", "get_gene_names_from_idx.SingleCells", "get_gene_names_from_idx.SingleCellsSubset" ] }, { "page": "get_go_data_human", "title": "Wrapper function to load and process the gene ontology data.", "topics": [ "get_go_data_human" ] }, { "page": "get_grid_search_res", "title": "Get the grid search results", "topics": [ "get_grid_search_res" ] }, { "page": "get_h", "title": "Get the H (cell activations) matrix", "topics": [ "get_h", "get_h.ConsensusNmfResult", "get_h.NmfResult", "get_h.StabilisedNmfResult" ] }, { "page": "get_h5ad_dimensions", "title": "Helper function to get the dimensions and storage format", "topics": [ "get_h5ad_dimensions" ] }, { "page": "get_hotspot_membership", "title": "Get the hotspot gene membership table", "topics": [ "get_hotspot_membership", "get_hotspot_membership.Hotspot" ] }, { "page": "get_hvg", "title": "Get the HVG", "topics": [ "get_hvg", "get_hvg.MetaCells", "get_hvg.ScMap", "get_hvg.SingleCells", "get_hvg.SingleCellsSubset" ] }, { "page": "get_hvg_data_sc", "title": "Identify HVGs without mutating object state", "topics": [ "get_hvg_data_sc" ] }, { "page": "get_ica_stability_res", "title": "Get the ICA component data (stability, convergence, redundancy)", "topics": [ "get_ica_stability_res" ] }, { "page": "get_index_cells", "title": "Get the index cells", "topics": [ "get_index_cells", "get_index_cells.miloR" ] }, { "page": "get_inflection_point", "title": "Identify the inflection point for elbow-like data", "topics": [ "get_inflection_point" ] }, { "page": "get_influence", "title": "Get the ligand-target influence matrix", "topics": [ "get_influence", "get_influence.LigandTargetInfluence" ] }, { "page": "get_kmeans_clusters", "title": "Get k-means cluster assignments from a fast cluster result", "topics": [ "get_kmeans_clusters", "get_kmeans_clusters.SingleCellFastClusters" ] }, { "page": "get_knn_dist", "title": "Get the KNN distance", "topics": [ "get_knn_dist", "get_knn_dist.MetaCells", "get_knn_dist.ScCache", "get_knn_dist.SingleCellNearestNeighbour", "get_knn_dist.SingleCells", "get_knn_dist.SingleCellsSubset" ] }, { "page": "get_knn_mat", "title": "Get the KNN matrix", "topics": [ "get_knn_mat", "get_knn_mat.MetaCells", "get_knn_mat.ScCache", "get_knn_mat.SingleCellNearestNeighbour", "get_knn_mat.SingleCells", "get_knn_mat.SingleCellsSubset" ] }, { "page": "get_knn_obj", "title": "Get the KNN object", "topics": [ "get_knn_obj", "get_knn_obj.MetaCells", "get_knn_obj.ScCache", "get_knn_obj.SingleCells", "get_knn_obj.SingleCellsSubset" ] }, { "page": "get_magic", "title": "Get the MAGIC imputed layer", "topics": [ "get_magic", "get_magic.ScCache", "get_magic.SingleCells", "get_magic.SingleCellsSubset" ] }, { "page": "get_marker_comparisons", "title": "Get the per-rival marker statistics", "topics": [ "get_marker_comparisons", "get_marker_comparisons.ScSpecificMarkers" ] }, { "page": "get_marker_summary", "title": "Get the per-gene marker summaries across all rivals", "topics": [ "get_marker_summary", "get_marker_summary.ScSpecificMarkers" ] }, { "page": "get_meta_cell_purity", "title": "Calculate meta cell purity without mutating object state", "topics": [ "get_meta_cell_purity" ] }, { "page": "get_metadata", "title": "Return the metadata", "topics": [ "get_metadata" ] }, { "page": "get_miloR_abundances_sc", "title": "Generate an miloR abundance object for differential abundance testing", "topics": [ "get_miloR_abundances_sc" ] }, { "page": "get_model_fit", "title": "Get the fitted model", "topics": [ "get_model_fit", "get_model_fit.miloR" ] }, { "page": "get_modules", "title": "Get the module membership from a BulkModuleResult", "topics": [ "get_modules" ] }, { "page": "get_nmf_gene_loadings", "title": "Get the NMF gene loadings", "topics": [ "get_nmf_gene_loadings" ] }, { "page": "get_nmf_modules", "title": "Get the NMF module membership data.table", "topics": [ "get_nmf_modules" ] }, { "page": "get_nmf_sample_activity", "title": "Get the NMF sample activity", "topics": [ "get_nmf_sample_activity" ] }, { "page": "get_nmf_stability", "title": "Get the multi-run NMF diagnostics", "topics": [ "get_nmf_stability" ] }, { "page": "get_ontology_ancestry", "title": "Return ancestry terms from an ontology", "topics": [ "get_ontology_ancestry" ] }, { "page": "get_outputs", "title": "Return the outputs", "topics": [ "get_outputs" ] }, { "page": "get_params", "title": "Get the parameters that were used.", "topics": [ "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" ] }, { "page": "get_pca_factors", "title": "Get the PCA factors", "topics": [ "get_pca_factors", "get_pca_factors.MetaCells", "get_pca_factors.ScCache", "get_pca_factors.SingleCells", "get_pca_factors.SingleCellsSubset" ] }, { "page": "get_pca_loadings", "title": "Get the PCA loadings", "topics": [ "get_pca_loadings", "get_pca_loadings.MetaCells", "get_pca_loadings.ScCache", "get_pca_loadings.SingleCells", "get_pca_loadings.SingleCellsSubset" ] }, { "page": "get_pca_singular_val", "title": "Get the PCA singular values", "topics": [ "get_pca_singular_val", "get_pca_singular_val.MetaCells", "get_pca_singular_val.ScCache", "get_pca_singular_val.SingleCells", "get_pca_singular_val.SingleCellsSubset" ] }, { "page": "get_pseudobulked_sc", "title": "Generate pseudo-bulked matrices", "topics": [ "get_pseudobulked_sc" ] }, { "page": "get_rbh_res", "title": "Get the RBH results", "topics": [ "get_rbh_res" ] }, { "page": "get_residual_fit", "title": "Get the fitted residual model", "topics": [ "get_residual_fit", "get_residual_fit.MetaCells", "get_residual_fit.ScCache", "get_residual_fit.SingleCells", "get_residual_fit.SingleCellsSubset" ] }, { "page": "get_resolution_res", "title": "Return the resolution results", "topics": [ "get_resolution_res" ] }, { "page": "get_results", "title": "Get the final results from the class", "topics": [ "get_results", "get_results.DialogueResult" ] }, { "page": "get_sc_available_features", "title": "Returns the available features for single cell applications", "topics": [ "get_sc_available_features" ] }, { "page": "get_sc_cache_status", "title": "Status of everything held in a single cell object's caches", "topics": [ "get_sc_cache_status" ] }, { "page": "get_sc_counts", "title": "Getter the counts", "topics": [ "get_sc_counts" ] }, { "page": "get_sc_obs", "title": "Getter the obs table", "topics": [ "get_sc_obs" ] }, { "page": "get_sc_var", "title": "Getter the var table", "topics": [ "get_sc_var" ] }, { "page": "get_scores", "title": "Get scores", "topics": [ "get_scores", "get_scores.ScDblFinderRes", "get_scores.ScTypeCellResults", "get_scores.ScTypeResults" ] }, { "page": "get_sim_matrix", "title": "Get the similarity matrix", "topics": [ "get_sim_matrix" ] }, { "page": "get_snf_adjcacency_mat", "title": "Get an individual affinity matrix", "topics": [ "get_snf_adjcacency_mat" ] }, { "page": "get_snf_final_mat", "title": "Get the final SNF matrix", "topics": [ "get_snf_final_mat" ] }, { "page": "get_snf_params", "title": "Get the SNF params", "topics": [ "get_snf_params" ] }, { "page": "get_snn_graph", "title": "Get the sNN graph", "topics": [ "get_snn_graph", "get_snn_graph.MetaCells", "get_snn_graph.ScCache", "get_snn_graph.SingleCells", "get_snn_graph.SingleCellsSubset" ] }, { "page": "get_stability", "title": "Get the consensus NMF stability diagnostics", "topics": [ "get_stability", "get_stability.ConsensusNmfResult" ] }, { "page": "get_symphony_hvg_names", "title": "Getter for the HVG gene names of a Symphony reference", "topics": [ "get_symphony_hvg_names" ] }, { "page": "get_symphony_labels", "title": "Getter for the stored labels of a Symphony reference", "topics": [ "get_symphony_labels" ] }, { "page": "get_symphony_loadings", "title": "Getter for the PCA loadings of a Symphony reference", "topics": [ "get_symphony_loadings" ] }, { "page": "get_symphony_z_corr", "title": "Getter for the corrected embedding of a Symphony reference", "topics": [ "get_symphony_z_corr" ] }, { "page": "get_tf_to_gene", "title": "Extract the TF to gene data from the ScenicGrn object", "topics": [ "get_tf_to_gene", "get_tf_to_gene.ScenicGrn" ] }, { "page": "get_top_terms", "title": "Get the highest-probability terms per topic", "topics": [ "get_top_terms", "get_top_terms.LdaResult" ] }, { "page": "get_tpm_counts", "title": "Return the TPM-normalised counts", "topics": [ "get_tpm_counts" ] }, { "page": "get_w", "title": "Get the W (gene loadings) matrix", "topics": [ "get_w", "get_w.ConsensusNmfResult", "get_w.NmfResult", "get_w.StabilisedNmfResult" ] }, { "page": "gse_go_elim_method", "title": "Run gene ontology enrichment with elimination method.", "topics": [ "gse_go_elim_method" ] }, { "page": "gse_go_elim_method_list", "title": "Run gene ontology enrichment with elimination method over a list.", "topics": [ "gse_go_elim_method_list" ] }, { "page": "gse_hypergeometric", "title": "Gene set enrichment (GSE) based on a hypergeometric test.", "topics": [ "gse_hypergeometric" ] }, { "page": "gse_hypergeometric_list", "title": "Gene set enrichment (GSE) based on a hypergeometric test over a list.", "topics": [ "gse_hypergeometric_list" ] }, { "page": "harmony_sc", "title": "Run Harmony", "topics": [ "harmony_sc" ] }, { "page": "harmony_v2_sc", "title": "Run Harmony v2", "topics": [ "harmony_v2_sc" ] }, { "page": "hedges_g_dge", "title": "Calculate the effect size", "topics": [ "hedges_g_dge" ] }, { "page": "hotspot_autocor_sc", "title": "Calculate the local auto-correlation of a gene", "topics": [ "hotspot_autocor_sc" ] }, { "page": "hotspot_gene_cor_sc", "title": "Calculate the local pairwise gene-gene correlation", "topics": [ "hotspot_gene_cor_sc" ] }, { "page": "ica_evaluate_comp", "title": "Iterate over different ncomp parameters for ICA", "topics": [ "ica_evaluate_comp" ] }, { "page": "ica_optimal_ncomp", "title": "Identify stability inflection point", "topics": [ "ica_optimal_ncomp" ] }, { "page": "ica_processing", "title": "Prepare class for ICA", "topics": [ "ica_processing" ] }, { "page": "ica_stabilised_results", "title": "Run stabilised ICA with a given number of components", "topics": [ "ica_stabilised_results" ] }, { "page": "identify_tf_to_genes", "title": "Identify the TF to gene regulation", "topics": [ "identify_tf_to_genes", "identify_tf_to_genes.ScenicGrn" ] }, { "page": "install_agent_skill", "title": "Install the bixverse agent skill", "topics": [ "install_agent_skill" ] }, { "page": "knn_graph_label_propagation", "title": "kNN-based graph label propagation", "topics": [ "knn_graph_label_propagation" ] }, { "page": "lda_k_sweep", "title": "Sweep the topic count for LDA", "topics": [ "lda_k_sweep" ] }, { "page": "leiden_clustering", "title": "Leiden or Louvain community detection on an igraph", "topics": [ "leiden_clustering" ] }, { "page": "ligand_activity_scores", "title": "Compute ligand activity scores against gene sets", "topics": [ "ligand_activity_scores" ] }, { "page": "load_existing", "title": "Load an existing SingleCells from disk", "topics": [ "load_existing" ] }, { "page": "load_go_human_data", "title": "Get the Gene Ontology data human", "topics": [ "load_go_human_data" ] }, { "page": "load_h5ad", "title": "Load in h5ad to 'SingleCells'", "topics": [ "load_h5ad" ] }, { "page": "load_h5ad_norm", "title": "Load in h5ad with normalised counts to 'SingleCells'", "topics": [ "load_h5ad_norm" ] }, { "page": "load_mtx", "title": "Load in mtx/plain text files to 'SingleCells'", "topics": [ "load_mtx" ] }, { "page": "load_multi_h5ad", "title": "Load multiple h5ad files into a single 'SingleCells'", "topics": [ "load_multi_h5ad" ] }, { "page": "load_multi_mtx", "title": "Load multiple mtx directories into a single 'SingleCells'", "topics": [ "load_multi_mtx" ] }, { "page": "load_multi_tenx_h5", "title": "Load multiple 10x CellRanger h5 files into a single 'SingleCells'", "topics": [ "load_multi_tenx_h5" ] }, { "page": "load_r_data", "title": "Load in data directly from R objects.", "topics": [ "load_r_data" ] }, { "page": "load_sce", "title": "Load in data from a 'SingleCellExperiment'", "topics": [ "load_sce" ] }, { "page": "load_seurat", "title": "Load in Seurat to 'SingleCells'", "topics": [ "load_seurat" ] }, { "page": "load_tenx_h5", "title": "Load in a 10x CellRanger h5 file to 'SingleCells'", "topics": [ "load_tenx_h5" ] }, { "page": "map_symphony_query", "title": "Map a SingleCells query onto a Symphony reference", "topics": [ "map_symphony_query" ] }, { "page": "meld_sc", "title": "Run MELD signal smoothing for differential abundance estimation", "topics": [ "meld_sc" ] }, { "page": "merge_meta_cells", "title": "Merge meta cell objects into one", "topics": [ "merge_meta_cells" ] }, { "page": "merge_sc_experiments", "title": "Merge multiple 'SingleCells' experiments into one", "topics": [ "merge_sc_experiments" ] }, { "page": "merge_subset_obs", "title": "Merge obs columns from subsets back into the parent object", "topics": [ "merge_subset_obs" ] }, { "page": "meta_cells_per_group", "title": "Generate source-pure meta cells and merge them", "topics": [ "meta_cells_per_group" ] }, { "page": "MetaCells", "title": "bixverse meta cell class", "topics": [ "MetaCells" ] }, { "page": "module_scores_sc", "title": "Calculate module activity scores", "topics": [ "module_scores_sc" ] }, { "page": "modules_from_loadings", "title": "Derive sparse module membership from a loading matrix", "topics": [ "modules_from_loadings" ] }, { "page": "nebula_mc", "title": "Run NEBULA on meta cells", "topics": [ "nebula_mc" ] }, { "page": "nebula_sc", "title": "Run NEBULA on single cells", "topics": [ "nebula_sc" ] }, { "page": "NetworkDiffusions", "title": "Network diffusion class", "topics": [ "NetworkDiffusions" ] }, { "page": "new_adt_counts_clr", "title": "Generates a new 'ADTCounts' class", "topics": [ "new_adt_counts_clr" ] }, { "page": "new_adt_counts_dsb", "title": "Generates a new 'ADTCounts' class via DSB normalisation", "topics": [ "new_adt_counts_dsb" ] }, { "page": "new_sc_knn", "title": "Helper function to generate kNN data with distances", "topics": [ "new_sc_knn" ] }, { "page": "nmf_bulk", "title": "Run non-negative matrix factorisation on a BulkCoExp", "topics": [ "nmf_bulk" ] }, { "page": "nmf_k_sweep_bulk", "title": "Sweep k for consensus NMF on a BulkCoExp", "topics": [ "nmf_k_sweep_bulk" ] }, { "page": "nmf_k_sweep_sc", "title": "Sweep k for consensus NMF on single cell or meta cell data", "topics": [ "nmf_k_sweep_sc" ] }, { "page": "nmf_sc", "title": "Run single-run NMF on single cell or meta cell data", "topics": [ "nmf_sc" ] }, { "page": "normalise_bulk_dge", "title": "Normalise the count data for DGE.", "topics": [ "normalise_bulk_dge" ] }, { "page": "OntologySim", "title": "OntologySim class", "topics": [ "OntologySim" ] }, { "page": "ot_harmonic_score", "title": "Calculates a harmonic sum normalised between 0 to 1.", "topics": [ "ot_harmonic_score" ] }, { "page": "params_binary_heatmap", "title": "Wrapper function for parameters for binary heatmap data", "topics": [ "params_binary_heatmap" ] }, { "page": "params_blitzgsea", "title": "Wrapper function to generate blitzGSEA parameters", "topics": [ "params_blitzgsea" ] }, { "page": "params_boost", "title": "Wrapper function for Boost parameters", "topics": [ "params_boost" ] }, { "page": "params_bpca", "title": "Wrapper function for Bayesian PCA parameters", "topics": [ "params_bpca" ] }, { "page": "params_bulk_sparsity", "title": "Wrapper function to generate bulk sparsification parameters", "topics": [ "params_bulk_sparsity" ] }, { "page": "params_cistarget", "title": "Wrapper function to CisTarget parameters", "topics": [ "params_cistarget" ] }, { "page": "params_community_detection", "title": "Wrapper function to generate community detection parameters", "topics": [ "params_community_detection" ] }, { "page": "params_cor_graph", "title": "Wrapper function for graph generation", "topics": [ "params_cor_graph" ] }, { "page": "params_coremo", "title": "Wrapper function to generate CoReMo parameters", "topics": [ "params_coremo" ] }, { "page": "params_dgrdl", "title": "Wrapper function to generate DGRDL parameters", "topics": [ "params_dgrdl" ] }, { "page": "params_dialogue_hlm", "title": "Wrapper function for the DIALOGUE mixed model parameters", "topics": [ "params_dialogue_hlm" ] }, { "page": "params_dialogue_pmd", "title": "Wrapper function for the DIALOGUE decomposition parameters", "topics": [ "params_dialogue_pmd" ] }, { "page": "params_dialogue_refine", "title": "Wrapper function for the DIALOGUE refinement parameters", "topics": [ "params_dialogue_refine" ] }, { "page": "params_edger_ql", "title": "Wrapper function for parameters for the edgeR quasi-likelihood workflow", "topics": [ "params_edger_ql" ] }, { "page": "params_fast_cluster_default", "title": "Helper function to generate default parameters for the fast clustering for the doublet detection methods", "topics": [ "params_fast_cluster_default" ] }, { "page": "params_graph_resolution", "title": "Wrapper function to generate resolution parameters for Leiden or Louvain clustering.", "topics": [ "params_graph_resolution" ] }, { "page": "params_gsea", "title": "Wrapper function to generate GSEA parameters", "topics": [ "params_gsea" ] }, { "page": "params_gsva", "title": "Wrapper function to generate GSVA parameters", "topics": [ "params_gsva" ] }, { "page": "params_hvg_defaults", "title": "Helper function to generate HVG defaults", "topics": [ "params_hvg_defaults" ] }, { "page": "params_hvg_scran_defaults", "title": "Helper function to generate the scran HVG trend defaults", "topics": [ "params_hvg_scran_defaults" ] }, { "page": "params_ica_general", "title": "Wrapper function for standard ICA parameters", "topics": [ "params_ica_general" ] }, { "page": "params_ica_ncomp", "title": "Wrapper function for ICA ncomp iterations", "topics": [ "params_ica_ncomp" ] }, { "page": "params_ica_randomisation", "title": "Wrapper function for ICA randomisation", "topics": [ "params_ica_randomisation" ] }, { "page": "params_kmeans_defaults", "title": "K-mean parameter defaults.", "topics": [ "params_kmeans_defaults" ] }, { "page": "params_knn_defaults", "title": "Helper function to generate kNN defaults", "topics": [ "params_knn_defaults" ] }, { "page": "params_label_propagation", "title": "Wrapper function to generate label propagation parameters", "topics": [ "params_label_propagation" ] }, { "page": "params_lda", "title": "Wrapper function for the LDA parameters", "topics": [ "params_lda" ] }, { "page": "params_ligand_target", "title": "Parameters for ligand to target influence computation", "topics": [ "params_ligand_target" ] }, { "page": "params_limma_voom", "title": "Wrapper function for parameters for the limma-voom workflow", "topics": [ "params_limma_voom" ] }, { "page": "params_meld", "title": "Constructor for MELD parameters", "topics": [ "params_meld" ] }, { "page": "params_module_membership", "title": "Wrapper function to generate module membership parameters", "topics": [ "params_module_membership" ] }, { "page": "params_nebula", "title": "Wrapper function for parameters for NEBULA", "topics": [ "params_nebula" ] }, { "page": "params_nmf_consensus", "title": "Wrapper function for consensus NMF parameters", "topics": [ "params_nmf_consensus" ] }, { "page": "params_nmf_hals", "title": "Wrapper function for NMF (HALS) parameters", "topics": [ "params_nmf_hals" ] }, { "page": "params_norm_doublets_defaults", "title": "Helper function to generate normalisation defaults for doublet detection.", "topics": [ "params_norm_doublets_defaults" ] }, { "page": "params_pca_defaults", "title": "Helper function to generate default parameters for PCA", "topics": [ "params_pca_defaults" ] }, { "page": "params_ppca", "title": "Wrapper function for probabilistic PCA parameters", "topics": [ "params_ppca" ] }, { "page": "params_sc_apr", "title": "Wrapper function for analytic Pearson residual parameters", "topics": [ "params_sc_apr" ] }, { "page": "params_sc_aucell", "title": "Wrapper function for parameters for AUCell", "topics": [ "params_sc_aucell" ] }, { "page": "params_sc_bbknn", "title": "Wrapper function for the BBKNN parameters", "topics": [ "params_sc_bbknn" ] }, { "page": "params_sc_bonsai", "title": "Wrapper function for Bonsai parameters", "topics": [ "params_sc_bonsai" ] }, { "page": "params_sc_branch_selection", "title": "Wrapper function for the branch cell selection parameters", "topics": [ "params_sc_branch_selection" ] }, { "page": "params_sc_bt_metacells", "title": "Wrapper function for parameters for bootstrapped meta cell generation", "topics": [ "params_sc_bt_metacells" ] }, { "page": "params_sc_cellphonedb", "title": "Parameters for the CellPhoneDB analysis", "topics": [ "params_sc_cellphonedb" ] }, { "page": "params_sc_cellsweep", "title": "Default parameters for CellSweep denoising", "topics": [ "params_sc_cellsweep" ] }, { "page": "params_sc_dsb", "title": "Default parameters for DSB ADT normalisation", "topics": [ "params_sc_dsb" ] }, { "page": "params_sc_empty_droplets", "title": "Parameters for identifying empty droplets", "topics": [ "params_sc_empty_droplets" ] }, { "page": "params_sc_fast_cluster", "title": "Fast single cell clustering parameters", "topics": [ "params_sc_fast_cluster" ] }, { "page": "params_sc_fastmnn", "title": "Wrapper function for the fastMNN parameters", "topics": [ "params_sc_fastmnn" ] }, { "page": "params_sc_gene_trends", "title": "Wrapper function for gene trend parameters", "topics": [ "params_sc_gene_trends" ] }, { "page": "params_sc_harmony", "title": "Default parameters for Harmony batch correction", "topics": [ "params_sc_harmony" ] }, { "page": "params_sc_harmony_v2", "title": "Default parameters for Harmony v2 batch correction", "topics": [ "params_sc_harmony_v2" ] }, { "page": "params_sc_hotspot", "title": "Wrapper function for parameters for HotSpot", "topics": [ "params_sc_hotspot" ] }, { "page": "params_sc_hvg", "title": "Wrapper function for HVG detection parameters.", "topics": [ "params_sc_hvg" ] }, { "page": "params_sc_knn", "title": "Parameters for single cell kNN searches", "topics": [ "params_sc_knn" ] }, { "page": "params_sc_magic", "title": "Wrapper function for MAGIC imputation parameters", "topics": [ "params_sc_magic" ] }, { "page": "params_sc_miloR", "title": "Wrapper function for parameters for MiloR", "topics": [ "params_sc_miloR" ] }, { "page": "params_sc_min_quality", "title": "Wrapper function to generate QC metric params for single cell", "topics": [ "params_sc_min_quality" ] }, { "page": "params_sc_mtx_io", "title": "Wrapper function to provide data for mtx-based loading", "topics": [ "params_sc_mtx_io" ] }, { "page": "params_sc_neighbours", "title": "Wrapper function for parameters for neighbour identification in single cell", "topics": [ "params_sc_neighbours" ] }, { "page": "params_sc_palantir", "title": "Wrapper function for Palantir parameters", "topics": [ "params_sc_palantir" ] }, { "page": "params_sc_pca", "title": "Wrapper for PCA specifically designed for single cells", "topics": [ "params_sc_pca" ] }, { "page": "params_sc_sctransform", "title": "Wrapper function for scTransform (v2) parameters", "topics": [ "params_sc_sctransform" ] }, { "page": "params_sc_seacells", "title": "Wrapper function for the SEACells parameters", "topics": [ "params_sc_seacells" ] }, { "page": "params_sc_seurat_cca", "title": "Wrapper function for the Seurat CCA parameters", "topics": [ "params_sc_seurat_cca" ] }, { "page": "params_sc_seurat_rpca", "title": "Wrapper function for the Seurat rPCA parameters", "topics": [ "params_sc_seurat_rpca" ] }, { "page": "params_sc_supercell", "title": "Wrapper function for parameters for SuperCell generation", "topics": [ "params_sc_supercell" ] }, { "page": "params_sc_synthetic_cellsweep", "title": "Default parameters for generation of synthetic CellSweep data", "topics": [ "params_sc_synthetic_cellsweep" ] }, { "page": "params_sc_synthetic_data", "title": "Default parameters for generation of synthetic single cell data (RNA)", "topics": [ "params_sc_synthetic_data" ] }, { "page": "params_sc_synthetic_data_adt", "title": "Default parameters for generation of synthetic single cell data (ADT)", "topics": [ "params_sc_synthetic_data_adt" ] }, { "page": "params_sc_synthetic_dialogue", "title": "Default parameters for generation of synthetic DIALOGUE data", "topics": [ "params_sc_synthetic_dialogue" ] }, { "page": "params_sc_vision", "title": "Wrapper function for parameters for VISION with auto-correlation", "topics": [ "params_sc_vision" ] }, { "page": "params_sc_wnn", "title": "Wrapper function for WNN parameters", "topics": [ "params_sc_wnn" ] }, { "page": "params_scdblfinder", "title": "Wrapper function for scDblFinder doublet detection parameters", "topics": [ "params_scdblfinder" ] }, { "page": "params_scenic", "title": "Constructor for SCENIC parameters", "topics": [ "params_scenic" ] }, { "page": "params_scenic_binarise", "title": "Wrapper function for parameters for the SCENIC binarisation", "topics": [ "params_scenic_binarise" ] }, { "page": "params_scenic_extra_trees_defaults", "title": "Default parameters for the SCENIC ExtraTrees regression learner", "topics": [ "params_scenic_extra_trees_defaults" ] }, { "page": "params_scenic_gradient_boosting_defaults", "title": "Default parameters for the SCENIC GradientBoosting (GRNBoost2) regression learner", "topics": [ "params_scenic_gradient_boosting_defaults" ] }, { "page": "params_scenic_random_forest_defaults", "title": "Default parameters for the SCENIC RandomForest regression learner", "topics": [ "params_scenic_random_forest_defaults" ] }, { "page": "params_scrublet", "title": "Wrapper function for Scrublet doublet detection parameters", "topics": [ "params_scrublet" ] }, { "page": "params_sctype_cells", "title": "Parameters for the per-cell ScType assignment", "topics": [ "params_sctype_cells" ] }, { "page": "params_snf", "title": "Wrapper function to generate SNF parameters", "topics": [ "params_snf" ] }, { "page": "params_ssgsea", "title": "Wrapper function to generate ssGSEA parameters", "topics": [ "params_ssgsea" ] }, { "page": "params_symphony_map", "title": "Default parameters for Symphony query mapping", "topics": [ "params_symphony_map" ] }, { "page": "params_synthetic_bulk_rnaseq", "title": "Wrapper function to generate synthetic bulk RNAseq parameters", "topics": [ "params_synthetic_bulk_rnaseq" ] }, { "page": "per_cell_qc_outlier", "title": "Use MAD outlier detection on per-cell QC metrics", "topics": [ "per_cell_qc_outlier" ] }, { "page": "permute_seed_nodes", "title": "Generate permuation scores for the diffusion", "topics": [ "permute_seed_nodes" ] }, { "page": "phate_sc", "title": "Run PHATE on a SingleCells/MetaCells object", "topics": [ "phate_sc" ] }, { "page": "plot_boxplot_normalization", "title": "Helper plot function for boxplot of normalised data", "topics": [ "plot_boxplot_normalization" ] }, { "page": "plot_epsilon_res", "title": "Plot the epsilon vs. power law goodness of fit result", "topics": [ "plot_epsilon_res" ] }, { "page": "plot_hvgs", "title": "Plot the highly variable genes", "topics": [ "plot_hvgs" ] }, { "page": "plot_ica_ncomp_params", "title": "Plot various parameters with no comp", "topics": [ "plot_ica_ncomp_params" ] }, { "page": "plot_ica_stability_individual", "title": "Plot the stability of the ICA components", "topics": [ "plot_ica_stability_individual" ] }, { "page": "plot_optimal_cuts", "title": "Plot the k cuts vs median R2", "topics": [ "plot_optimal_cuts" ] }, { "page": "plot_pca", "title": "Helper plot function for pca with contrasts", "topics": [ "plot_pca" ] }, { "page": "plot_pca_res", "title": "Plot the PCA data", "topics": [ "plot_pca_res" ] }, { "page": "plot_preprocessing_genes", "title": "Helper plot function of distribution of genes by samples", "topics": [ "plot_preprocessing_genes" ] }, { "page": "plot_preprocessing_outliers", "title": "Helper plot function for identification of outliers", "topics": [ "plot_preprocessing_outliers" ] }, { "page": "plot_rbf_impact", "title": "Helper function to plot distance to affinity relationship", "topics": [ "plot_rbf_impact" ] }, { "page": "plot_resolution_res", "title": "Plot the resolution results.", "topics": [ "plot_resolution_res" ] }, { "page": "plot_voom_normalization", "title": "Helper plot function for Voom normalisation", "topics": [ "plot_voom_normalization" ] }, { "page": "plot.LdaKSweepResult", "title": "Plot the LDA topic count sweep", "topics": [ "plot.LdaKSweepResult" ] }, { "page": "plot.NmfKSweepResult", "title": "Plot a consensus NMF k sweep", "topics": [ "plot.NmfKSweepResult" ] }, { "page": "pre_process_sim_onto", "title": "Pre-process data for subsequent ontology similarity", "topics": [ "pre_process_sim_onto" ] }, { "page": "prepare_cell_markers", "title": "Helper function to prepare cell markers", "topics": [ "prepare_cell_markers" ] }, { "page": "preprocess_bulk_coexp", "title": "Process the raw data", "topics": [ "preprocess_bulk_coexp" ] }, { "page": "preprocess_bulk_dge", "title": "QC on the bulk dge data (DEPRECATED!)", "topics": [ "preprocess_bulk_dge" ] }, { "page": "prescan_h5ad_files", "title": "Pre-scan multiple h5ad files for multi-sample loading", "topics": [ "prescan_h5ad_files" ] }, { "page": "prescan_mtx_dirs", "title": "Prescan multiple mtx directories for a multi-load", "topics": [ "prescan_mtx_dirs" ] }, { "page": "prescan_tenx_h5_files", "title": "Pre-scan multiple 10x CellRanger h5 files for multi-sample loading", "topics": [ "prescan_tenx_h5_files" ] }, { "page": "prioritise_interactions", "title": "Prioritise sender-ligand-receiver-receptor interactions", "topics": [ "prioritise_interactions" ] }, { "page": "process_go_data", "title": "Process Gene Ontology data into the right format", "topics": [ "process_go_data" ] }, { "page": "pseudobulk_dge_sc", "title": "Run the edgeR quasi-likelihood workflow on pseudo-bulked single cells", "topics": [ "pseudobulk_dge_sc" ] }, { "page": "qc_bulk_dge", "title": "QC on the bulk dge data", "topics": [ "qc_bulk_dge" ] }, { "page": "RbhGraph", "title": "Reciprocal best hit graph", "topics": [ "RbhGraph" ] }, { "page": "read_h5ad_metadata", "title": "Read obs and var tables and metadata from an h5ad file", "topics": [ "read_h5ad_metadata" ] }, { "page": "read_h5ad_x_summary", "title": "Read summary statistics from the X slot of an h5ad file", "topics": [ "read_h5ad_x_summary" ] }, { "page": "read_motif_annotation_file", "title": "Read in the motif annotation file", "topics": [ "read_motif_annotation_file" ] }, { "page": "read_motif_ranking", "title": "Read in the motif rankings and transform them into a matrix", "topics": [ "read_motif_ranking" ] }, { "page": "read_multi_tenx_h5_adt", "title": "Read in 10x h5 ADT data from multiple files", "topics": [ "read_multi_tenx_h5_adt" ] }, { "page": "read_tenx_h5_adt", "title": "Read in 10x h5 ADT data", "topics": [ "read_tenx_h5_adt" ] }, { "page": "read_tenx_h5_metadata", "title": "Read barcode and feature tables and metadata from a 10x h5 file", "topics": [ "read_tenx_h5_metadata" ] }, { "page": "relayout_bonsai", "title": "Recompute the layout of a Bonsai tree", "topics": [ "relayout_bonsai" ] }, { "page": "remove_adt_isotypes", "title": "Return the ADT feature names removing the isotypes", "topics": [ "remove_adt_isotypes" ] }, { "page": "remove_magic", "title": "Remove the MAGIC imputed layer", "topics": [ "remove_magic", "remove_magic.ScCache", "remove_magic.SingleCells", "remove_magic.SingleCellsSubset" ] }, { "page": "remove_residual_fit", "title": "Remove the fitted residual model", "topics": [ "remove_residual_fit", "remove_residual_fit.MetaCells", "remove_residual_fit.ScCache", "remove_residual_fit.SingleCells", "remove_residual_fit.SingleCellsSubset" ] }, { "page": "remove_samples", "title": "Remove samples from object", "topics": [ "remove_samples" ] }, { "page": "rescue_cells", "title": "Rescue MAD-flagged cells that fall within safe bounds", "topics": [ "rescue_cells" ] }, { "page": "reset_cells_to_keep", "title": "Reset the cells to keep", "topics": [ "reset_cells_to_keep", "reset_cells_to_keep.MetaCells", "reset_cells_to_keep.SingleCells", "reset_cells_to_keep.SingleCellsMultiModal", "reset_cells_to_keep.SingleCellsSubset" ] }, { "page": "robust_scale", "title": "Robust scaler.", "topics": [ "robust_scale" ] }, { "page": "rs_2d_loess", "title": "Rust implementation of a Loess function", "topics": [ "rs_2d_loess" ] }, { "page": "rs_batch_silhouette_width", "title": "Calculate batch silhouette width from an embedding", "topics": [ "rs_batch_silhouette_width" ] }, { "page": "rs_blitzgsea_calibrate", "title": "Calibrate the blitzGSEA gamma null for a signature", "topics": [ "rs_blitzgsea_calibrate" ] }, { "page": "rs_blitzgsea_score", "title": "Score gene sets against a calibrated blitzGSEA null", "topics": [ "rs_blitzgsea_score" ] }, { "page": "rs_calc_norm_factors", "title": "Calculate normalisation factors", "topics": [ "rs_calc_norm_factors" ] }, { "page": "rs_cell_type_asw", "title": "Calculate cell type silhouette width from an embedding", "topics": [ "rs_cell_type_asw" ] }, { "page": "rs_cistarget", "title": "Run CisTarget motif enrichment analysis", "topics": [ "rs_cistarget" ] }, { "page": "rs_compare_knn", "title": "Helper to compare kNN graphs", "topics": [ "rs_compare_knn" ] }, { "page": "rs_constrained_page_rank", "title": "Calculate a constrained page-rank score", "topics": [ "rs_constrained_page_rank" ] }, { "page": "rs_constrained_page_rank_list", "title": "Calculate a constrained page-rank score over a list.", "topics": [ "rs_constrained_page_rank_list" ] }, { "page": "rs_contrastive_pca", "title": "Calculate the contrastive PCA", "topics": [ "rs_contrastive_pca" ] }, { "page": "rs_cor", "title": "Calculate the column wise correlations.", "topics": [ "rs_cor" ] }, { "page": "rs_cor_upper_triangle", "title": "Calculate the column wise correlations and returns the upper triangle", "topics": [ "rs_cor_upper_triangle" ] }, { "page": "rs_cor2", "title": "Calculate the column wise correlations.", "topics": [ "rs_cor2" ] }, { "page": "rs_cos", "title": "Calculate the column wise cosine similarities", "topics": [ "rs_cos" ] }, { "page": "rs_count_zeroes", "title": "Helper to get zero stats from a given matrix", "topics": [ "rs_count_zeroes" ] }, { "page": "rs_cov2cor", "title": "Calculates the correlation matrix from the co-variance matrix", "topics": [ "rs_cov2cor" ] }, { "page": "rs_covariance", "title": "Calculate the column-wise co-variance.", "topics": [ "rs_covariance" ] }, { "page": "rs_cpm", "title": "Counts per million", "topics": [ "rs_cpm" ] }, { "page": "rs_dense_to_upper_triangle", "title": "Generate a vector-based representation of the upper triangle of a matrix", "topics": [ "rs_dense_to_upper_triangle" ] }, { "page": "rs_differential_cor", "title": "Calculate the column wise differential correlation between two sets of data.", "topics": [ "rs_differential_cor" ] }, { "page": "rs_dist", "title": "Calculate the pairwise column distance in a matrix", "topics": [ "rs_dist" ] }, { "page": "rs_edger_ql", "title": "Run the edgeR quasi-likelihood chain on a count matrix", "topics": [ "rs_edger_ql" ] }, { "page": "rs_fast_auc", "title": "Fast AUC calculation", "topics": [ "rs_fast_auc" ] }, { "page": "rs_fast_ica", "title": "Run the Rust implementation of fast ICA.", "topics": [ "rs_fast_ica" ] }, { "page": "rs_fdr_adjustment", "title": "Calculate a BH-based FDR", "topics": [ "rs_fdr_adjustment" ] }, { "page": "rs_filter_by_expr", "title": "Filter lowly expressed genes", "topics": [ "rs_filter_by_expr" ] }, { "page": "rs_gene_trends", "title": "Fit Palantir gene trends over pseudotime", "topics": [ "rs_gene_trends" ] }, { "page": "rs_generate_ligand_target_influence", "title": "Generate the ligand to target influence matrices", "topics": [ "rs_generate_ligand_target_influence" ] }, { "page": "rs_geom_elim_fgsea_simple", "title": "Run fgsea simple method for gene ontology with elimination method", "topics": [ "rs_geom_elim_fgsea_simple" ] }, { "page": "rs_gower_dist", "title": "Calculates the Gower distance for a given matrix", "topics": [ "rs_gower_dist" ] }, { "page": "rs_graph_connectivity", "title": "Calculate graph connectivity per cell type", "topics": [ "rs_graph_connectivity" ] }, { "page": "rs_gse_geom_elim", "title": "Run hypergeometric enrichment over the gene ontology", "topics": [ "rs_gse_geom_elim" ] }, { "page": "rs_gse_geom_elim_list", "title": "Run hypergeometric enrichment a list of target genes over the gene ontology", "topics": [ "rs_gse_geom_elim_list" ] }, { "page": "rs_gsva", "title": "Rust version of the GSVA algorithm", "topics": [ "rs_gsva" ] }, { "page": "rs_h5ad_data", "title": "Load in h5ad data via Rust", "topics": [ "rs_h5ad_data" ] }, { "page": "rs_hamming_dist", "title": "Calculates the Hamming distance between categorical columns", "topics": [ "rs_hamming_dist" ] }, { "page": "rs_harmony", "title": "Harmony batch correction in Rust", "topics": [ "rs_harmony" ] }, { "page": "rs_harmony_v2", "title": "Harmony batch correction in Rust (version 2)", "topics": [ "rs_harmony_v2" ] }, { "page": "rs_hedges_g", "title": "Calculate the Hedge's G effect", "topics": [ "rs_hedges_g" ] }, { "page": "rs_hypergeom_test", "title": "Run a single hypergeometric test.", "topics": [ "rs_hypergeom_test" ] }, { "page": "rs_hypergeom_test_list", "title": "Run a hypergeometric test over a list of target genes", "topics": [ "rs_hypergeom_test_list" ] }, { "page": "rs_ica_iters", "title": "Run ICA over a given no_comp with random initialisations of w_init", "topics": [ "rs_ica_iters" ] }, { "page": "rs_ica_iters_cv", "title": "Run ICA with cross-validation and random initialisation", "topics": [ "rs_ica_iters_cv" ] }, { "page": "rs_jaccard_row_integers", "title": "Calculate rapidly Jaccard similarities between rows", "topics": [ "rs_jaccard_row_integers" ] }, { "page": "rs_kbet", "title": "Calculate kBET type scores", "topics": [ "rs_kbet" ] }, { "page": "rs_knn_label_propagation", "title": "kNN label propagation", "topics": [ "rs_knn_label_propagation" ] }, { "page": "rs_knn_mat_to_edge_list", "title": "Flatten kNN matrix to edge list", "topics": [ "rs_knn_mat_to_edge_list" ] }, { "page": "rs_leiden_clustering", "title": "Rust implementation of Leiden and Louvain clustering", "topics": [ "rs_leiden_clustering" ] }, { "page": "rs_ligand_activity_scores", "title": "Calculate the NicheNet ligand activity scores", "topics": [ "rs_ligand_activity_scores" ] }, { "page": "rs_limma_voom", "title": "Run the limma linear model chain on a count matrix", "topics": [ "rs_limma_voom" ] }, { "page": "rs_lisi", "title": "Calculate LISI scores on any label", "topics": [ "rs_lisi" ] }, { "page": "rs_mad_outlier", "title": "Calculate MAD outlier detection in Rust.", "topics": [ "rs_mad_outlier" ] }, { "page": "rs_mc_aucell", "title": "Calculate AUCell in Rust (for meta cells)", "topics": [ "rs_mc_aucell" ] }, { "page": "rs_mc_dialogue", "title": "Run DIALOGUE over meta cells", "topics": [ "rs_mc_dialogue" ] }, { "page": "rs_mc_hotspot_autocor", "title": "Calculate gene spatial auto-correlations (for meta cells)", "topics": [ "rs_mc_hotspot_autocor" ] }, { "page": "rs_mc_hotspot_gene_cor", "title": "Calculate gene to gene spatial correlations (for meta cells)", "topics": [ "rs_mc_hotspot_gene_cor" ] }, { "page": "rs_mc_hvg", "title": "Meta cells highly variable genes", "topics": [ "rs_mc_hvg" ] }, { "page": "rs_mc_pca", "title": "PCA on MetaCells (sparse data)", "topics": [ "rs_mc_pca" ] }, { "page": "rs_mc_scenic", "title": "SCENIC on MetaCells", "topics": [ "rs_mc_scenic" ] }, { "page": "rs_mc_vision", "title": "Calculate VISION pathway scores in Rust (for meta cells)", "topics": [ "rs_mc_vision" ] }, { "page": "rs_mc_vision_with_autocorrelation", "title": "Calculate VISION pathway scores with auto-correlation (for meta cells)", "topics": [ "rs_mc_vision_with_autocorrelation" ] }, { "page": "rs_mitch_calc", "title": "Calculate mitch enrichment leveraging Rust under the hood", "topics": [ "rs_mitch_calc" ] }, { "page": "rs_mutual_info", "title": "Calculates the mutual information matrix", "topics": [ "rs_mutual_info" ] }, { "page": "rs_nebula_mc", "title": "Fit the NEBULA negative binomial gamma mixed model over meta cells", "topics": [ "rs_nebula_mc" ] }, { "page": "rs_onto_semantic_sim", "title": "Calculate the semantic similarity in an ontology", "topics": [ "rs_onto_semantic_sim" ] }, { "page": "rs_onto_semantic_sim_mat", "title": "Calculate the semantic similarity in an ontology", "topics": [ "rs_onto_semantic_sim_mat" ] }, { "page": "rs_onto_sim_wang", "title": "Calculate the Wang similarity for specific terms", "topics": [ "rs_onto_sim_wang" ] }, { "page": "rs_onto_sim_wang_mat", "title": "Calculate the Wang similarity matrix for an ontology", "topics": [ "rs_onto_sim_wang_mat" ] }, { "page": "rs_ot_harmonic_sum", "title": "Calculate the OT harmonic sum", "topics": [ "rs_ot_harmonic_sum" ] }, { "page": "rs_page_rank", "title": "Rust version of calculating the personalised page rank", "topics": [ "rs_page_rank" ] }, { "page": "rs_page_rank_parallel", "title": "Calculate massively parallelised personalised page rank scores", "topics": [ "rs_page_rank_parallel" ] }, { "page": "rs_pcr", "title": "Principal component regression on batch", "topics": [ "rs_pcr" ] }, { "page": "rs_phyper", "title": "Calculate the hypergeometric test in Rust", "topics": [ "rs_phyper" ] }, { "page": "rs_pointwise_mutual_info", "title": "Calculates the point wise mutual information", "topics": [ "rs_pointwise_mutual_info" ] }, { "page": "rs_prcomp", "title": "Rust implementation of prcomp", "topics": [ "rs_prcomp" ] }, { "page": "rs_prepare_whitening", "title": "Prepare the data for whitening", "topics": [ "rs_prepare_whitening" ] }, { "page": "rs_random_svd", "title": "Run randomised SVD over a matrix", "topics": [ "rs_random_svd" ] }, { "page": "rs_range_norm", "title": "Apply a range normalisation on a vector.", "topics": [ "rs_range_norm" ] }, { "page": "rs_rank_matrix_col", "title": "Gene rank matrix", "topics": [ "rs_rank_matrix_col" ] }, { "page": "rs_rank_matrix_col_stable", "title": "Stable-gene rank matrix", "topics": [ "rs_rank_matrix_col_stable" ] }, { "page": "rs_rbf_function", "title": "Apply a Radial Basis Function", "topics": [ "rs_rbf_function" ] }, { "page": "rs_rbf_function_mat", "title": "Apply a Radial Basis Function (to a matrix)", "topics": [ "rs_rbf_function_mat" ] }, { "page": "rs_rbh_cor", "title": "Generate reciprocal best hits based on correlations", "topics": [ "rs_rbh_cor" ] }, { "page": "rs_rbh_sets", "title": "Generate reciprocal best hits based on set similarities", "topics": [ "rs_rbh_sets" ] }, { "page": "rs_remove_batch_effect", "title": "Remove batch effects from a log-expression matrix", "topics": [ "rs_remove_batch_effect" ] }, { "page": "rs_sc_knn", "title": "Generates the kNN graph", "topics": [ "rs_sc_knn" ] }, { "page": "rs_sc_knn_w_dist", "title": "Generates the kNN graph with additional distances", "topics": [ "rs_sc_knn_w_dist" ] }, { "page": "rs_sc_snn", "title": "Generates the sNN graph for igraph", "topics": [ "rs_sc_snn" ] }, { "page": "rs_set_similarity", "title": "Set similarities", "topics": [ "rs_set_similarity" ] }, { "page": "rs_set_similarity_list", "title": "Set similarities over one list", "topics": [ "rs_set_similarity_list" ] }, { "page": "rs_set_similarity_list2", "title": "Set similarities over two list", "topics": [ "rs_set_similarity_list2" ] }, { "page": "rs_singscore_multi", "title": "Rust version of singscore for many gene sets", "topics": [ "rs_singscore_multi" ] }, { "page": "rs_singscore_permutation_test", "title": "Rust version of the singscore permutation test", "topics": [ "rs_singscore_permutation_test" ] }, { "page": "rs_singscore_single", "title": "Rust version of singscore for a single gene set", "topics": [ "rs_singscore_single" ] }, { "page": "rs_snf", "title": "Similarity network fusion", "topics": [ "rs_snf" ] }, { "page": "rs_sparse_dict_dgrdl", "title": "Generate a sparse dictionary with DGRDL", "topics": [ "rs_sparse_dict_dgrdl" ] }, { "page": "rs_sparse_dict_dgrdl_grid_search", "title": "Generate a sparse dictionary with DGRDL", "topics": [ "rs_sparse_dict_dgrdl_grid_search" ] }, { "page": "rs_spatial_fdr", "title": "Weighted Benjamini-Hochberg over overlapping neighbourhoods", "topics": [ "rs_spatial_fdr" ] }, { "page": "rs_spectral_clustering", "title": "Rust implementation of spectral clustering", "topics": [ "rs_spectral_clustering" ] }, { "page": "rs_spectral_clustering_sim", "title": "Rust implementation of spectral clustering", "topics": [ "rs_spectral_clustering_sim" ] }, { "page": "rs_ssgsea", "title": "Rust version of the ssGSEA algorithm", "topics": [ "rs_ssgsea" ] }, { "page": "rs_tied_diffusion_parallel", "title": "Calculate massively parallelised tied diffusion scores", "topics": [ "rs_tied_diffusion_parallel" ] }, { "page": "rs_tom", "title": "Calculates the TOM over an affinity matrix", "topics": [ "rs_tom" ] }, { "page": "rs_upper_triangle_to_dense", "title": "Reconstruct a matrix from a flattened upper triangle vector", "topics": [ "rs_upper_triangle_to_dense" ] }, { "page": "rs_upper_triangle_to_sparse", "title": "Generate sparse data from an upper triangle", "topics": [ "rs_upper_triangle_to_sparse" ] }, { "page": "rs_voom_normalise", "title": "Voom-transform a count matrix", "topics": [ "rs_voom_normalise" ] }, { "page": "rs_wnn", "title": "Run the weighted nearest neighbour algorithm", "topics": [ "rs_wnn" ] }, { "page": "run_bpca", "title": "Bayesian PCA on a matrix with missing values", "topics": [ "run_bpca" ] }, { "page": "run_cell_qc", "title": "Run outlier detection on per-cell QC metrics", "topics": [ "run_cell_qc" ] }, { "page": "run_cell_qc_fixed", "title": "Fixed-threshold cell QC", "topics": [ "run_cell_qc_fixed" ] }, { "page": "run_cistarget", "title": "Main function to run CisTarget", "topics": [ "run_cistarget" ] }, { "page": "run_edger_ql", "title": "Run the edgeR quasi-likelihood workflow", "topics": [ "run_edger_ql" ] }, { "page": "run_gene_trends_sc", "title": "Fit gene trends over Palantir pseudotime", "topics": [ "run_gene_trends_sc" ] }, { "page": "run_lda", "title": "Fit a latent Dirichlet allocation model", "topics": [ "run_lda" ] }, { "page": "run_limma_voom", "title": "Wrapper for a Limma Voom analysis", "topics": [ "run_limma_voom" ] }, { "page": "run_magic_sc", "title": "Impute a subset of genes with MAGIC", "topics": [ "run_magic_sc" ] }, { "page": "run_paga_sc", "title": "Run PAGA graph abstraction", "topics": [ "run_paga_sc" ] }, { "page": "run_palantir_sc", "title": "Run Palantir trajectory inference", "topics": [ "run_palantir_sc" ] }, { "page": "run_ppca", "title": "Probabilistic PCA on a matrix with missing values", "topics": [ "run_ppca" ] }, { "page": "run_snf", "title": "Run the SNF algorithm", "topics": [ "run_snf" ] }, { "page": "save_h5ad", "title": "Save a 'SingleCells' object to h5ad", "topics": [ "save_h5ad" ] }, { "page": "save_sc_exp_to_disk", "title": "Save memory-bound data to disk", "topics": [ "save_sc_exp_to_disk" ] }, { "page": "sc_knn_to_nearest_neighbours", "title": "Convert SingleCellNearestNeighbour to manifoldsR NearestNeighbours", "topics": [ "sc_knn_to_nearest_neighbours" ] }, { "page": "sc_pipeline", "title": "Construct an empty single cell pipeline", "topics": [ "sc_pipeline" ] }, { "page": "scdblfinder_sc", "title": "Run scDblFinder doublet detection on a SingleCells object", "topics": [ "scdblfinder_sc" ] }, { "page": "scenic_gene_filter_sc", "title": "Filter genes for SCENIC GRN inference", "topics": [ "scenic_gene_filter_sc" ] }, { "page": "scenic_grn_sc", "title": "Run SCENIC GRN inference", "topics": [ "scenic_grn_sc" ] }, { "page": "score_clusters", "title": "Score clusters based on ScType", "topics": [ "score_clusters", "score_clusters.ScTypeResults" ] }, { "page": "scrublet_sc", "title": "Doublet detection with Scrublet", "topics": [ "scrublet_sc" ] }, { "page": "sct_corrected_counts_sc", "title": "Write scTransform-corrected counts to a new store", "topics": [ "sct_corrected_counts_sc" ] }, { "page": "set_bonsai_embedding", "title": "Store the leaf coordinates of a Bonsai tree as an embedding", "topics": [ "set_bonsai_embedding" ] }, { "page": "set_magic", "title": "Set/add the MAGIC imputed layer", "topics": [ "set_magic", "set_magic.ScCache", "set_magic.SingleCells", "set_magic.SingleCellsSubset" ] }, { "page": "set_residual_fit", "title": "Set/add the fitted residual model", "topics": [ "set_residual_fit", "set_residual_fit.MetaCells", "set_residual_fit.ScCache", "set_residual_fit.SingleCells", "set_residual_fit.SingleCellsSubset" ] }, { "page": "set_sc_new_obs_col", "title": "Add a new column to the obs table", "topics": [ "set_sc_new_obs_col" ] }, { "page": "set_sc_new_obs_col_multiple", "title": "Add multiple new columns to the obs table", "topics": [ "set_sc_new_obs_col_multiple" ] }, { "page": "set_sc_new_var_cols", "title": "Add a new column to the var table", "topics": [ "set_sc_new_var_cols" ] }, { "page": "setnames_sc", "title": "Rename columns in the obs or var table", "topics": [ "setnames_sc" ] }, { "page": "seurat_cca_sc", "title": "Run Seurat CCA integration", "topics": [ "seurat_cca_sc" ] }, { "page": "seurat_rpca_sc", "title": "Run Seurat rPCA integration", "topics": [ "seurat_rpca_sc" ] }, { "page": "SimilarityNetworkFusion", "title": "Similarity network fusion", "topics": [ "SimilarityNetworkFusion" ] }, { "page": "simplify_hypergeom_res", "title": "Simplify gene set results via ontologies", "topics": [ "simplify_hypergeom_res" ] }, { "page": "simulate_dropouts", "title": "Simulate sequencing-depth dropouts on synthetic bulk data", "topics": [ "simulate_dropouts" ] }, { "page": "SingleCellCountData", "title": "Single cell count data handler", "topics": [ "$.SingleCellCountData", "SingleCellCountData" ] }, { "page": "SingleCells", "title": "bixverse SingleCells class", "topics": [ "SingleCells" ] }, { "page": "SingleCellsMultiModal", "title": "bixverse SingleCells (multi modal) class", "topics": [ "SingleCellsMultiModal" ] }, { "page": "SingleCellsSubset", "title": "bixverse single cell subset class", "topics": [ "SingleCellsSubset" ] }, { "page": "stabilised_nmf_bulk", "title": "Run stabilised (multi-restart) NMF on a BulkCoExp", "topics": [ "stabilised_nmf_bulk" ] }, { "page": "stabilised_nmf_sc", "title": "Run stabilised (multi-run) NMF on single cell or meta cell data", "topics": [ "stabilised_nmf_sc" ] }, { "page": "step_bbknn_sc", "title": "Pipeline step: BBKNN batch correction", "topics": [ "step_bbknn_sc" ] }, { "page": "step_clusters_sc", "title": "Pipeline step: graph-based clustering", "topics": [ "step_clusters_sc" ] }, { "page": "step_fast_mnn_sc", "title": "Pipeline step: fastMNN batch correction", "topics": [ "step_fast_mnn_sc" ] }, { "page": "step_harmony_sc", "title": "Pipeline step: Harmony batch correction", "topics": [ "step_harmony_sc" ] }, { "page": "step_harmony_v2_sc", "title": "Pipeline step: Harmony v2 batch correction", "topics": [ "step_harmony_v2_sc" ] }, { "page": "step_hvg_sc", "title": "Pipeline step: identify highly variable genes", "topics": [ "step_hvg_sc" ] }, { "page": "step_metacells_sc", "title": "Pipeline step: generate meta cells", "topics": [ "step_metacells_sc" ] }, { "page": "step_neighbours_sc", "title": "Pipeline step: nearest neighbours", "topics": [ "step_neighbours_sc" ] }, { "page": "step_pca_sc", "title": "Pipeline step: PCA", "topics": [ "step_pca_sc" ] }, { "page": "stream_h5ad", "title": "Stream in h5ad to 'SingleCells' (alias)", "topics": [ "stream_h5ad" ] }, { "page": "SymphonyReference", "title": "bixverse SymphonyReference class", "topics": [ "SymphonyReference" ] }, { "page": "synthetic_bulk_cor_matrix", "title": "Generates synthetic bulk RNAseq data", "topics": [ "synthetic_bulk_cor_matrix" ] }, { "page": "synthetic_c_pca_data", "title": "Generates synthetic data for contrastive PCA exploration.", "topics": [ "synthetic_c_pca_data" ] }, { "page": "synthetic_signal_matrix", "title": "Generates a simple synthetic, pseudo gene expression matrix", "topics": [ "synthetic_signal_matrix" ] }, { "page": "test_nhoods", "title": "Test neighbourhoods for differential abundance", "topics": [ "test_nhoods", "test_nhoods.miloR" ] }, { "page": "tf_to_genes_correlations", "title": "Generate TF to gene correlations", "topics": [ "tf_to_genes_correlations", "tf_to_genes_correlations.ScenicGrn" ] }, { "page": "tf_to_genes_motif_enrichment", "title": "Run the SCENIC motif enrichment", "topics": [ "tf_to_genes_motif_enrichment", "tf_to_genes_motif_enrichment.ScenicGrn" ] }, { "page": "tied_diffusion", "title": "Diffuse seed genes in a tied manner over a network", "topics": [ "tied_diffusion" ] }, { "page": "to_snake_case", "title": "Helper function to transform strings to snake_case", "topics": [ "to_snake_case" ] }, { "page": "top_genes_perc_sc", "title": "Calculate the proportions of reads for the Top N genes", "topics": [ "top_genes_perc_sc" ] }, { "page": "transfer_labels_symphony", "title": "Transfer labels from a Symphony reference to a query via kNN majority vote", "topics": [ "transfer_labels_symphony" ] }, { "page": "tsne_sc", "title": "Run t-SNE on a SingleCells/MetaCells object", "topics": [ "tsne_sc" ] }, { "page": "umap_sc", "title": "Run UMAP on a SingleCells/MetaCells object", "topics": [ "umap_sc" ] }, { "page": "update_metadata_values", "title": "Replace values in a metadata column", "topics": [ "update_metadata_values" ] }, { "page": "upper_triangle_to_sparse", "title": "Transform an upper triangle-stored matrix to a sparse one", "topics": [ "upper_triangle_to_sparse" ] }, { "page": "upper_triangular_sym_mat", "title": "Class for symmetric correlation matrices", "topics": [ "upper_triangular_sym_mat" ] }, { "page": "validate_pipeline", "title": "Check that a pipeline can run on a given class", "topics": [ "validate_pipeline" ] }, { "page": "vision_sc", "title": "Calculate VISION scores", "topics": [ "vision_sc" ] }, { "page": "vision_w_autocor_sc", "title": "Calculate VISION scores (with auto-correlation scores)", "topics": [ "vision_w_autocor_sc" ] }, { "page": "write_cellranger_output", "title": "Helper function to write data to a cell ranger like output", "topics": [ "write_cellranger_output" ] }, { "page": "write_h5ad_sc", "title": "Helper function to write data to h5ad format", "topics": [ "write_h5ad_sc" ] }, { "page": "write_h5ad_sc_dense", "title": "Helper function to write data to a dense h5ad file", "topics": [ "write_h5ad_sc_dense" ] } ], "_pkglogo": "https://github.com/GregorLueg/bixverse/raw/main/man/figures/logo.png", "_readme": "https://github.com/GregorLueg/bixverse/raw/main/README.md", "_rundeps": [ "arrow", "assertthat", "backports", "biocmake", "bit", "bit64", "brio", "callr", "checkmate", "cli", "cpp11", "data.table", "DBI", "desc", "dir.expiry", "dplyr", "duckdb", "farver", "fastcluster", "filelock", "generics", "ggplot2", "glue", "gtable", "igraph", "isoband", "jsonlite", "labeling", "lattice", "lifecycle", "magrittr", "manifoldsR", "Matrix", "matrixStats", "mirai", "nanonext", "otel", "patchwork", "pillar", "pkgbuild", "pkgconfig", "processx", "ps", "purrr", "R.methodsS3", "R.oo", "R.utils", "R6", "RColorBrewer", "rextendr", "rhdf5", "rhdf5filters", "Rhdf5lib", "rlang", "rprojroot", "S7", "scales", "stringi", "stringr", "tibble", "tidyselect", "utf8", "vctrs", "viridisLite", "withr", "zeallot" ], "_score": 6.650412440668238, "_indexed": true, "_nocasepkg": "bixverse", "_universes": [ "gregorlueg" ], "_previous": "0.6.3", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.7.0", "date": "2026-10-14T23:11:36.000Z", "distro": "resolute", "arch": "aarch64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/4368d5c5a97a5d69ccba50ba07729eb5f8420b6489e9584137cb6627f9ed5428", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.7.0", "os": "linux", "version": "0.7.0", "date": "2026-10-14T23:11:45.000Z", "distro": "resolute", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/922382d26b851a07f7080ded5f52054c55d52691fee1134829a0806588c30984", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.6.1", "os": "linux", "version": "0.7.0", "date": "2026-10-14T23:11:19.000Z", "distro": "resolute", "arch": "aarch64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/f2888d19b7430c616734aa301d41e7d140bb39c1e2c86e7482567ad5d8158bb3", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.6.1", "os": "linux", "version": "0.7.0", "date": "2026-10-14T23:11:27.000Z", "distro": "resolute", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/369fcc8980936ac07298da9d333515e68a81a232522eb9148568223606869f45", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.5.3", "os": "mac", "version": "0.7.0", "date": "2026-10-14T23:09:55.000Z", "arch": "aarch64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/da4adc39dccc0fc3783952d9c38454ad760a3a2fffa5353527630157f6fc11bf", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.5.3", "os": "mac", "version": "0.7.0", "date": "2026-10-14T23:11:55.000Z", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/8f46c699e32002a946aa1bf97b1f9e38123bbd3a299caf0ec144121d02003937", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.6.1", "os": "mac", "version": "0.7.0", "date": "2026-10-14T23:09:51.000Z", "arch": "aarch64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/0a063dcd51a4d51f93460da972d4766a9ada992a4933fdd9c245382372027ee6", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.6.1", "os": "mac", "version": "0.7.0", "date": "2026-10-14T23:13:08.000Z", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/68fc08712e132f81b761acee9ea4391f06af2be8ad1d97ffeaf8df6c4b4483f3", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.7.0", "os": "win", "version": "0.7.0", "date": "2026-10-14T23:11:43.000Z", "arch": "aarch64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/1b6ae9c2c6ce240014f6be3928e63c727c663ac5c2ef5a2d6b07193b357c9b63", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.7.0", "os": "win", "version": "0.7.0", "date": "2026-10-14T23:11:00.000Z", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/00745f49b1065279092e2d4a60bc25b6211df974c562a966fda17325ebe25cd5", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.5.3", "os": "win", "version": "0.7.0", "date": "2026-10-14T23:09:44.000Z", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/14f45fd71375e06cf52f86148c11c8711b5032ea82906b338c35222a3ffa0e5e", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.6.1", "os": "win", "version": "0.7.0", "date": "2026-10-14T23:13:22.000Z", "arch": "aarch64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/dd06b97807160d7623b0491c7a7823bd61a6fedf128b9c647577c98f5e3f2d27", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" }, { "r": "4.6.1", "os": "win", "version": "0.7.0", "date": "2026-10-14T23:10:12.000Z", "arch": "x86_64", "commit": "d543471b7464b6dddc9a3e660abfca3252166593", "fileid": "https://r2.ropensci.org/f2b1b77ae3a231586e112d7d229ca68ea10d2159ec5e46d9639880028ad6c084", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/gregorlueg/actions/runs/38650407145" } ] }