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"evaluate_model_application", "title": "Construct and evaluate a ligand-target model given input parameters (for application purposes).", "topics": [ "evaluate_model_application" ] }, { "page": "evaluate_model_application_multi_ligand", "title": "Construct and evaluate a ligand-target model given input parameters (for application purposes + multi-ligand predictive model).", "topics": [ "evaluate_model_application_multi_ligand" ] }, { "page": "evaluate_model_cv", "title": "Construct and evaluate a ligand-target model given input parameters with the purpose of evaluating cross-validation models.", "topics": [ "evaluate_model_cv" ] }, { "page": "evaluate_multi_ligand_target_prediction", "title": "Evaluation of target gene prediction for multiple ligands.", "topics": [ "evaluate_multi_ligand_target_prediction" ] }, { "page": "evaluate_multi_ligand_target_prediction_regression", "title": "Evaluation of target gene value prediction for multiple ligands (regression).", "topics": [ "evaluate_multi_ligand_target_prediction_regression" ] }, { "page": "evaluate_random_model", "title": "Construct and evaluate a randomised ligand-target model given input parameters.", "topics": [ "evaluate_random_model" ] }, { "page": "evaluate_single_importances_ligand_prediction", "title": "Evaluation of ligand activity prediction performance of single ligand importance scores: aggregate all datasets.", "topics": [ "evaluate_single_importances_ligand_prediction" ] }, { "page": "evaluate_target_prediction", "title": "Evaluation of target gene prediction.", "topics": [ "evaluate_target_prediction" ] }, { "page": "evaluate_target_prediction_interprete", "title": "Evaluation of target gene prediction.", "topics": [ "evaluate_target_prediction_interprete" ] }, { "page": "evaluate_target_prediction_per_bin", "title": "Evaluate target gene predictions for different bins/groups of targets genes", "topics": [ "evaluate_target_prediction_per_bin" ] }, { "page": "evaluate_target_prediction_regression", "title": "Evaluation of target gene value prediction (regression).", "topics": [ "evaluate_target_prediction_regression" ] }, { "page": "expression_settings_validation", "title": "Expression datasets for validation", "topics": [ "expression_settings_validation" ] }, { "page": "extract_ligands_from_settings", "title": "Extract ligands of interest from settings", "topics": [ "extract_ligands_from_settings" ] }, { "page": "extract_top_fraction_ligands", "title": "Get the predicted top n percentage ligands of a target of interest", "topics": [ "extract_top_fraction_ligands" ] }, { "page": "extract_top_fraction_targets", "title": "Get the predicted top n percentage target genes of a ligand of interest", "topics": [ "extract_top_fraction_targets" ] }, { "page": "extract_top_n_ligands", "title": "Get the predicted top n ligands of a target gene of interest", "topics": [ "extract_top_n_ligands" ] }, { "page": "extract_top_n_targets", "title": "Get the predicted top n target genes of a ligand of interest", "topics": [ "extract_top_n_targets" ] }, { "page": "geneinfo_2022", "title": "Gene annotation information: version 2 - january 2022", "topics": [ "geneinfo_2022" ] }, { "page": "geneinfo_alias_human", "title": "Gene annotation information: version 2 - january 2022 - suited for alias conversion", "topics": [ "geneinfo_alias_human" ] }, { "page": "geneinfo_alias_mouse", "title": "Gene annotation information: version 2 - january 2022 - suited for alias conversion", "topics": [ "geneinfo_alias_mouse" ] }, { "page": "geneinfo_human", "title": "Gene annotation information", "topics": [ "geneinfo_human" ] }, { "page": "generate_info_tables", "title": "Generate tables used for 'generate_prioritization_tables'", "topics": [ "generate_info_tables" ] }, { "page": "generate_prioritization_tables", "title": "Perform a prioritization of cell-cell interactions (similar to MultiNicheNet).", "topics": [ "generate_prioritization_tables" ] }, { "page": "get_active_ligand_receptor_network", "title": "Get active ligand-receptor network for cellular interaction between a sender and receiver cell.", "topics": [ "get_active_ligand_receptor_network" ] }, { "page": "get_active_ligand_target_df", "title": "Get active ligand-target network in data frame format.", "topics": [ "get_active_ligand_target_df" ] }, { "page": "get_active_ligand_target_matrix", "title": "Get active ligand-target matrix.", "topics": [ "get_active_ligand_target_matrix" ] }, { "page": "get_active_regulatory_network", "title": "Get active gene regulatory network in a receiver cell.", "topics": [ "get_active_regulatory_network" ] }, { "page": "get_active_signaling_network", "title": "Get active signaling network in a receiver cell.", "topics": [ "get_active_signaling_network" ] }, { "page": "get_database_cache_stats", "title": "Get Cache Statistics", "topics": [ "get_database_cache_stats" ] }, { "page": "get_expressed_genes", "title": "Determine expressed genes of a cell type from an input object", "topics": [ "get_expressed_genes", "get_expressed_genes.default", "get_expressed_genes.Seurat" ] }, { "page": "get_exprs_avg", "title": "Calculate average of gene expression per cell type.", "topics": [ "get_exprs_avg" ] }, { "page": "get_lfc_celltype", "title": "Get log fold change values of genes in cell type of interest", "topics": [ "get_lfc_celltype" ] }, { "page": "get_ligand_activities_targets", "title": "Calculate the ligand activities and infer ligand-target links based on a list of niche-specific genes per receiver cell type", "topics": [ "get_ligand_activities_targets" ] }, { "page": "get_ligand_signaling_path", "title": "Get ligand-target signaling paths between ligand(s) and target gene(s) of interest", "topics": [ "get_ligand_signaling_path" ] }, { "page": "get_ligand_signaling_path_with_receptor", "title": "Get ligand-target signaling paths between ligand(s), receptors, and target gene(s) of interest", "topics": [ "get_ligand_signaling_path_with_receptor" ] }, { "page": "get_ligand_slope_ligand_prediction_popularity", "title": "Regression analysis between popularity of left-out ligands for ligand activity prediction performance", "topics": [ "get_ligand_slope_ligand_prediction_popularity" ] }, { "page": "get_ligand_target_links_oi", "title": "Get ligand-target links of interest", "topics": [ "get_ligand_target_links_oi" ] }, { "page": "get_multi_ligand_importances", "title": "Get ligand importances from a multi-ligand classfication model.", "topics": [ "get_multi_ligand_importances" ] }, { "page": "get_multi_ligand_importances_regression", "title": "Get ligand importances from a multi-ligand regression model.", "topics": [ "get_multi_ligand_importances_regression" ] }, { "page": "get_multi_ligand_rf_importances", "title": "Get ligand importances from a multi-ligand trained random forest model.", "topics": [ "get_multi_ligand_rf_importances" ] }, { "page": "get_multi_ligand_rf_importances_regression", "title": "Get ligand importances from a multi-ligand trained random forest regression model.", "topics": [ "get_multi_ligand_rf_importances_regression" ] }, { "page": "get_ncitations_genes", "title": "Get the number of times of gene is mentioned in the pubmed literature", "topics": [ "get_ncitations_genes" ] }, { "page": "get_non_spatial_de", "title": "Makes a table similar to the output of `calculate_spatial_DE` and `process_spatial_de`, but now in case you don't have spatial information for the sender and/or receiver celltype. This is needed for comparability reasons.", "topics": [ "get_non_spatial_de" ] }, { "page": "get_optimized_parameters_nsga2r", "title": "Get optimized parameters from the output of 'run_nsga2R_cluster'.", "topics": [ "get_optimized_parameters_nsga2r" ] }, { "page": "get_prioritization_tables", "title": "Use the information from the niche- and spatial differential expression analysis of ligand-senders and receptor-receivers pairs, in addition to the ligand activity prediction and ligand-target inferernce, in order to make a final ligand-receptor and ligand-target prioritization table.", "topics": [ "get_prioritization_tables" ] }, { "page": "get_single_ligand_importances", "title": "Get ligand importances based on target gene prediction performance of single ligands.", "topics": [ "get_single_ligand_importances" ] }, { "page": "get_single_ligand_importances_regression", "title": "Get ligand importances based on target gene value prediction performance of single ligands (regression).", "topics": [ "get_single_ligand_importances_regression" ] }, { "page": "get_slope_ligand_popularity", "title": "Regression analysis between ligand popularity and target gene predictive performance", "topics": [ "get_slope_ligand_popularity" ] }, { "page": "get_slope_target_gene_popularity", "title": "Regression analysis between target gene popularity and target gene predictive performance", "topics": [ "get_slope_target_gene_popularity" ] }, { "page": "get_slope_target_gene_popularity_ligand_prediction", "title": "Regression analysis between target gene popularity and ligand activity predictive performance", "topics": [ "get_slope_target_gene_popularity_ligand_prediction" ] }, { "page": "get_target_genes_ligand_oi", "title": "Get a set of predicted target genes of a ligand of interest", "topics": [ "get_target_genes_ligand_oi" ] }, { "page": "get_top_predicted_genes", "title": "Find which genes were among the top-predicted targets genes in a specific cross-validation round and see whether these genes belong to the gene set of interest as well.", "topics": [ "get_top_predicted_genes" ] }, { "page": "get_weighted_ligand_receptor_links", "title": "Get the weighted ligand-receptor links between a possible ligand and its receptors", "topics": [ "get_weighted_ligand_receptor_links" ] }, { "page": "get_weighted_ligand_target_links", "title": "Infer weighted active ligand-target links between a possible ligand and target genes of interest", "topics": [ "get_weighted_ligand_target_links" ] }, { "page": "hyperparameter_list", "title": "Optimized hyperparameter values", "topics": [ "hyperparameter_list" ] }, { "page": "infer_supporting_datasources", "title": "Get the data sources that support the specific interactions in the extracted ligand-target signaling subnetwork", "topics": [ "infer_supporting_datasources" ] }, { "page": "ligand_activity_performance_top_i_removed", "title": "Calculate ligand activity performance without considering evaluation datasets belonging to the top i most frequently cited ligands", "topics": [ "ligand_activity_performance_top_i_removed" ] }, { "page": "make_circos_lr", "title": "make_circos_lr", "topics": [ "make_circos_lr" ] }, { "page": "make_circos_plot", "title": "Draw a circos plot", "topics": [ "make_circos_plot" ] }, { "page": "make_discrete_ligand_target_matrix", "title": "Convert probabilistic ligand-target matrix to a discrete one.", "topics": [ "make_discrete_ligand_target_matrix" ] }, { "page": "make_heatmap_bidir_lt_ggplot", "title": "Make a ggplot heatmap object from an input ligand-target matrix.", "topics": [ "make_heatmap_bidir_lt_ggplot" ] }, { "page": "make_heatmap_ggplot", "title": "Make a ggplot heatmap object from an input matrix (2-color).", "topics": [ "make_heatmap_ggplot" ] }, { "page": "make_ligand_activity_target_exprs_plot", "title": "make_ligand_activity_target_exprs_plot", "topics": [ "make_ligand_activity_target_exprs_plot" ] }, { "page": "make_ligand_receptor_lfc_plot", "title": "make_ligand_receptor_lfc_plot", "topics": [ "make_ligand_receptor_lfc_plot" ] }, { "page": "make_ligand_receptor_lfc_spatial_plot", "title": "make_ligand_receptor_lfc_spatial_plot", "topics": [ "make_ligand_receptor_lfc_spatial_plot" ] }, { "page": "make_line_plot", "title": "Make a line plot", "topics": [ "make_line_plot" ] }, { "page": "make_mushroom_plot", "title": "Make a \"mushroom plot\" of ligand-receptor interactions", "topics": [ "make_mushroom_plot" ] }, { "page": "make_threecolor_heatmap_ggplot", "title": "Make a ggplot heatmap object from an input matrix (3-color).", "topics": [ "make_threecolor_heatmap_ggplot" ] }, { "page": "mlrmbo_optimization", "title": "Optimization of objective functions via model-based optimization (mlrMBO).", "topics": [ "mlrmbo_optimization" ] }, { "page": "model_based_ligand_activity_prediction", "title": "Prediction of ligand activity prediction by a model trained on ligand importance scores.", "topics": [ "model_based_ligand_activity_prediction" ] }, { "page": "model_evaluation_hyperparameter_optimization_mlrmbo", "title": "Construct and evaluate a ligand-target model given input parameters with the purpose of hyperparameter optimization using mlrMBO.", "topics": [ "model_evaluation_hyperparameter_optimization_mlrmbo" ] }, { "page": "model_evaluation_optimization_application", "title": "Construct and evaluate a ligand-target model given input parameters with the purpose of parameter optimization for multi-ligand application.", "topics": [ "model_evaluation_optimization_application" ] }, { "page": "model_evaluation_optimization_mlrmbo", "title": "Construct and evaluate a ligand-target model given input parameters with the purpose of parameter optimization with mlrMBO.", "topics": [ "model_evaluation_optimization_mlrmbo" ] }, { "page": "model_evaluation_optimization_nsga2r", "title": "Construct and evaluate a ligand-target model with the purpose of parameter optimization with NSGA-II.", "topics": [ "model_evaluation_optimization_nsga2r" ] }, { "page": "mutate_cond", "title": "Change values in a tibble if some condition is fulfilled.", "topics": [ "mutate_cond" ] }, { "page": "ncitations", "title": "Number of citations for genes", "topics": [ "ncitations" ] }, { "page": "nichenet_seuratobj_aggregate", "title": "Perform NicheNet analysis on Seurat object: explain DE between conditions", "topics": [ "nichenet_seuratobj_aggregate" ] }, { "page": "nichenet_seuratobj_aggregate_cluster_de", "title": "Perform NicheNet analysis on Seurat object: explain DE between two cell clusters from separate conditions", "topics": [ "nichenet_seuratobj_aggregate_cluster_de" ] }, { "page": "nichenet_seuratobj_cluster_de", "title": "Perform NicheNet analysis on Seurat object: explain DE between two cell clusters", "topics": [ "nichenet_seuratobj_cluster_de" ] }, { "page": "normalize_single_cell_ligand_activities", "title": "Normalize single-cell ligand activities", "topics": [ "normalize_single_cell_ligand_activities" ] }, { "page": "optimized_source_weights_df", "title": "Optimized data source weights", "topics": [ "optimized_source_weights_df" ] }, { "page": "predict_ligand_activities", "title": "Predict activities of ligands in regulating expression of a gene set of interest", "topics": [ "predict_ligand_activities" ] }, { "page": "predict_single_cell_ligand_activities", "title": "Single-cell ligand activity prediction", "topics": [ "predict_single_cell_ligand_activities" ] }, { "page": "prepare_circos_visualization", "title": "Prepare circos visualization", "topics": [ "prepare_circos_visualization" ] }, { "page": "prepare_ligand_receptor_visualization", "title": "Prepare ligand-receptor visualization", "topics": [ "prepare_ligand_receptor_visualization" ] }, { "page": "prepare_ligand_target_visualization", "title": "Prepare heatmap visualization of the ligand-target links starting from a ligand-target tibble.", "topics": [ "prepare_ligand_target_visualization" ] }, { "page": "prepare_settings_leave_one_in_characterization", "title": "Prepare settings for leave-one-in characterization", "topics": [ "prepare_settings_leave_one_in_characterization" ] }, { "page": "prepare_settings_leave_one_out_characterization", "title": "Prepare settings for leave-one-out characterization", "topics": [ "prepare_settings_leave_one_out_characterization" ] }, { "page": "prepare_settings_one_vs_one_characterization", "title": "Prepare settings for one-vs-one characterization", "topics": [ "prepare_settings_one_vs_one_characterization" ] }, { "page": "process_characterization_ligand_prediction", "title": "Process the output of model evaluation for data source characterization purposes on the ligand prediction performance", "topics": [ "process_characterization_ligand_prediction" ] }, { "page": "process_characterization_ligand_prediction_single_measures", "title": "Process the output of model evaluation for data source characterization purposes on the ligand prediction performance (for every importance score individually)", "topics": [ "process_characterization_ligand_prediction_single_measures" ] }, { "page": "process_characterization_popularity_slopes_ligand_prediction", "title": "Process the output of model evaluation for data source characterization purposes on the popularity bias assessment of ligand activity performance", "topics": [ "process_characterization_popularity_slopes_ligand_prediction" ] }, { "page": "process_characterization_popularity_slopes_target_prediction", "title": "Process the output of model evaluation for data source characterization purposes on the popularity bias assessment of target prediction performance", "topics": [ "process_characterization_popularity_slopes_target_prediction" ] }, { "page": "process_characterization_target_prediction", "title": "Process the output of model evaluation for data source characterization purposes on the target prediction performance", "topics": [ "process_characterization_target_prediction" ] }, { "page": "process_characterization_target_prediction_average", "title": "Process the output of model evaluation for data source characterization purposes on the target prediction performance (average)", "topics": [ "process_characterization_target_prediction_average" ] }, { "page": "process_mlrmbo_nichenet_optimization", "title": "Process the output of mlrmbo multi-objective optimization to extract optimal parameter values.", "topics": [ "process_mlrmbo_nichenet_optimization" ] }, { "page": "process_niche_de", "title": "Process the DE output of `calculate_niche_de`", "topics": [ "process_niche_de" ] }, { "page": "process_receiver_target_de", "title": "Processing differential expression output of the receiver cell types", "topics": [ "process_receiver_target_de" ] }, { "page": "process_spatial_de", "title": "Process the spatialDE output", "topics": [ "process_spatial_de" ] }, { "page": "process_table_to_ic", "title": "Process DE or expression information into intercellular communication focused information.", "topics": [ "process_table_to_ic" ] }, { "page": "randomize_complete_network_source_specific", "title": "Randomize an integrated network by shuffling its source networks", "topics": [ "randomize_complete_network_source_specific" ] }, { "page": "randomize_datasource_network", "title": "Randomize a network of a particular data source.", "topics": [ "randomize_datasource_network" ] }, { "page": "randomize_network", "title": "Randomize a network", "topics": [ "randomize_network" ] }, { "page": "run_nsga2R_cluster", "title": "Run NSGA-II for parameter optimization.", "topics": [ "run_nsga2R_cluster" ] }, { "page": "scale_quantile", "title": "Cut off outer quantiles and rescale to a [0, 1] range", "topics": [ "scale_quantile" ] }, { "page": "scale_quantile_adapted", "title": "Normalize values in a vector by quantile scaling and add a pseudovalue of 0.001", "topics": [ "scale_quantile_adapted" ] }, { "page": "scaling_modified_zscore", "title": "Normalize values in a vector by the modified z-score method.", "topics": [ "scaling_modified_zscore" ] }, { "page": "scaling_zscore", "title": "Normalize values in a vector by the z-score method", "topics": [ "scaling_zscore" ] }, { "page": "single_ligand_activity_score_classification", "title": "Assess how well cells' ligand activities predict a binary property of interest of cells.", "topics": [ "single_ligand_activity_score_classification" ] }, { "page": "single_ligand_activity_score_regression", "title": "Perform a correlation and regression analysis between cells' ligand activities and property scores of interest", "topics": [ "single_ligand_activity_score_regression" ] }, { "page": "source_weights_df", "title": "Data source weights", "topics": [ "source_weights_df" ] }, { "page": "visualize_parameter_values", "title": "Visualize parameter values from the output of 'run_nsga2R_cluster'.", "topics": [ "visualize_parameter_values" ] }, { "page": "visualize_parameter_values_across_folds", "title": "Visualize parameter values from the output of 'run_nsga2R_cluster' across cross-validation folds.", "topics": [ "visualize_parameter_values_across_folds" ] }, { "page": "wrapper_average_performances", "title": "Calculate average performance of datasets of a specific ligand.", "topics": [ "wrapper_average_performances" ] }, { "page": "wrapper_evaluate_single_importances_ligand_prediction", "title": "Evaluation of ligand activity prediction performance of single ligand importance scores: each dataset individually.", "topics": [ "wrapper_evaluate_single_importances_ligand_prediction" ] } ], "_readme": "https://github.com/Zaoqu-Liu/nichenetr/raw/master/README.md", "_rundeps": [ "abind", "askpass", "backports", "base64enc", "BBmisc", "BH", "BiocGenerics", "bit", "bit64", "bitops", "blob", "boot", "broom", "bslib", "cachem", "callr", "car", "carData", "caret", "caTools", "cellranger", "checkmate", "circlize", "class", "cli", 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