Package: nichenetr 2.2.2

nichenetr: Modeling Intercellular Communication by Linking Ligands to Target Genes with NicheNet

NicheNet is a computational framework for modeling intercellular communication by linking extracellular ligands to intracellular gene regulatory changes. It integrates prior knowledge of ligand-receptor interactions, signaling pathways, and transcription factor regulation to predict which ligands from sender cells affect gene expression in receiver cells. This enables mechanistic hypothesis generation about cell-cell communication events observed in transcriptomic data.

Authors:Zaoqu Liu [aut, cre], Robin Browaeys [aut], Wouter Saelens [aut], Yvan Saeys [aut]

nichenetr_2.2.2.tar.gz
nichenetr_2.2.2.zip(r-4.7)nichenetr_2.2.2.zip(r-4.6)
nichenetr_2.2.2.tgz(r-4.6-x86_64)nichenetr_2.2.2.tgz(r-4.6-arm64)
nichenetr_2.2.2.tar.gz(r-4.7-x86_64)nichenetr_2.2.2.tar.gz(r-4.7-arm64)nichenetr_2.2.2.tar.gz(r-4.6-arm64)nichenetr_2.2.2.tar.gz(r-4.6-x86_64)
nichenetr_2.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nichenetr/json (API)

# Install 'nichenetr' in R:
install.packages('nichenetr', repos = c('https://zaoqu-liu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zaoqu-liu/nichenetr/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

4.04 score 1 packages 724 scripts 148 exports 274 dependencies

Last updated from:54c2cb1cc3 (on master). Checks:9 FAIL, 1 ERROR, 2 FAILURE, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64FAIL3911
linux-devel-x86_64FAIL3860
source / vignettesERROR2662
linux-release-arm64FAIL3833
linux-release-x86_64FAIL3859
macos-release-arm64FAIL3768
macos-release-x86_64FAIL3915
macos-oldrel-arm64FAIL85
macos-oldrel-x86_64FAIL303
windows-develFAILURE3824
windows-releaseFAILURE3784
windows-oldrelFAIL113
wasm-releaseOK286

Exports:add_hyperparameters_parameter_settingsadd_ligand_popularity_measures_to_perfsadd_new_datasourcealias_to_symbol_seuratapply_hub_correctionsassess_influence_sourceassess_rf_class_probabilitiesassign_ligands_to_celltypebootstrap_ligand_activity_analysiscalculate_decalculate_fraction_top_predictedcalculate_fraction_top_predicted_fishercalculate_niche_decalculate_niche_de_targetscalculate_p_value_bootstrapcalculate_spatial_DEclassification_evaluation_continuous_pred_wrapperclear_database_cachecombine_sender_receiver_deconstruct_ligand_target_matrixconstruct_ligand_tf_matrixconstruct_modelconstruct_random_modelconstruct_tf_target_matrixconstruct_weighted_networksconvert_alias_to_symbolsconvert_cluster_to_settingsconvert_expression_settings_evaluationconvert_expression_settings_evaluation_regressionconvert_gene_list_settings_evaluationconvert_human_to_mouse_symbolsconvert_mouse_to_human_symbolsconvert_settings_ligand_predictionconvert_settings_tf_predictionconvert_settings_topn_ligand_predictionconvert_single_cell_expression_to_settingscorrect_topology_pprdiagrammer_format_signaling_graphestimate_source_weights_characterizationevaluate_importances_ligand_predictionevaluate_ligand_prediction_per_binevaluate_modelevaluate_model_applicationevaluate_model_application_multi_ligandevaluate_model_cvevaluate_multi_ligand_target_predictionevaluate_multi_ligand_target_prediction_regressionevaluate_random_modelevaluate_single_importances_ligand_predictionevaluate_target_predictionevaluate_target_prediction_interpreteevaluate_target_prediction_per_binevaluate_target_prediction_regressionextract_ligands_from_settingsextract_top_fraction_ligandsextract_top_fraction_targetsextract_top_n_ligandsextract_top_n_targetsgenerate_info_tablesgenerate_prioritization_tablesget_active_ligand_receptor_networkget_active_ligand_target_dfget_active_ligand_target_matrixget_active_regulatory_networkget_active_signaling_networkget_database_cache_statsget_expressed_genesget_exprs_avgget_lfc_celltypeget_ligand_activities_targetsget_ligand_signaling_pathget_ligand_signaling_path_with_receptorget_ligand_slope_ligand_prediction_popularityget_ligand_target_links_oiget_multi_ligand_importancesget_multi_ligand_importances_regressionget_multi_ligand_rf_importancesget_multi_ligand_rf_importances_regressionget_ncitations_genesget_non_spatial_deget_optimized_parameters_nsga2rget_prioritization_tablesget_single_ligand_importancesget_single_ligand_importances_regressionget_slope_ligand_popularityget_slope_target_gene_popularityget_slope_target_gene_popularity_ligand_predictionget_target_genes_ligand_oiget_top_predicted_genesget_weighted_ligand_receptor_linksget_weighted_ligand_target_linksinfer_supporting_datasourcesligand_activity_performance_top_i_removedmake_circos_lrmake_circos_plotmake_discrete_ligand_target_matrixmake_heatmap_bidir_lt_ggplotmake_heatmap_ggplotmake_ligand_activity_target_exprs_plotmake_ligand_receptor_lfc_plotmake_ligand_receptor_lfc_spatial_plotmake_line_plotmake_mushroom_plotmake_threecolor_heatmap_ggplotmlrmbo_optimizationmodel_based_ligand_activity_predictionmodel_evaluation_hyperparameter_optimization_mlrmbomodel_evaluation_optimization_applicationmodel_evaluation_optimization_mlrmbomodel_evaluation_optimization_nsga2rmutate_condnichenet_seuratobj_aggregatenichenet_seuratobj_aggregate_cluster_denichenet_seuratobj_cluster_denormalize_single_cell_ligand_activitiespredict_ligand_activitiespredict_single_cell_ligand_activitiesprepare_circos_visualizationprepare_ligand_receptor_visualizationprepare_ligand_target_visualizationprepare_settings_leave_one_in_characterizationprepare_settings_leave_one_out_characterizationprepare_settings_one_vs_one_characterizationprocess_characterization_ligand_predictionprocess_characterization_ligand_prediction_single_measuresprocess_characterization_popularity_slopes_ligand_predictionprocess_characterization_popularity_slopes_target_predictionprocess_characterization_target_predictionprocess_characterization_target_prediction_averageprocess_mlrmbo_nichenet_optimizationprocess_niche_deprocess_receiver_target_deprocess_spatial_deprocess_table_to_icrandomize_complete_network_source_specificrandomize_datasource_networkrandomize_networkrun_nsga2R_clusterscale_quantilescale_quantile_adaptedscaling_modified_zscorescaling_zscoresingle_ligand_activity_score_classificationsingle_ligand_activity_score_regressionvisualize_parameter_valuesvisualize_parameter_values_across_foldswrapper_average_performanceswrapper_evaluate_single_importances_ligand_prediction

Dependencies:abindaskpassbackportsbase64encBBmiscBHBiocGenericsbitbit64bitopsblobbootbroombslibcachemcallrcarcarDatacaretcaToolscellrangercheckmatecirclizeclassclicliprclockclueclustercodetoolscolorspacecommonmarkComplexHeatmapconflictedcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIdbplyrdeldirDerivdiagramDiagrammeRDiceKrigingdigestdoBydoParalleldotCall64dplyrdqrngdtplyre1071emoaevaluatefarverfastDummiesfastmapfastmatchfdrtoolfitdistrplusFNNfontawesomefontBitstreamVerafontLiberationfontquiverforcatsforeachforecastforeignFormulafracdifffsfuturefuture.applygarglegdtoolsgenericsGetoptLongggforceggiraphggnewscaleggplot2ggpubrggrepelggridgesggsciggsignifGlobalOptionsglobalsgluegoftestgoogledrivegooglesheets4gowergplotsgridExtragtablegtoolshardhathavenherehighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttricaidsigraphipredIRangesirlbaisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallhslifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminiUIminqamlrmlrMBOModelMetricsmodelrnlmenloptrnnetnumDerivopensslotelparallellyparallelMapParamHelperspatchworkpbapplypbkrtestpillarpkgconfigplotlyplyrpngpolyclippolynomprettyunitspROCprocessxprodlimprogressprogressrpromisesproxypspurrrquantregR6raggrandomForestRANNrappdirsrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLRdpackreadrreadxlrecipesreformulasrematchrematch2reprexreshape2reticulaterjsonrlangrmarkdownROCRrpartrprojrootRSpectrarstatixrstudioapiRtsnervestS4VectorsS7sassscalesscattermoresctransformselectrSeuratSeuratObjectshadowtextshapeshinysitmosmoofsourcetoolsspspamSparseMsparsevctrsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsSQUAREMstringistringrsurvivalsyssystemfontstensortextshapingtibbletidyrtidyselecttidyversetimechangetimeDatetinytextweenrtzdburcautf8uuiduwotvctrsviridisLitevisNetworkvroomwithrxfunXMLxml2xtableyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Add hyperparameters to existing parameter settingsadd_hyperparameters_parameter_settings
Merge target gene prediction performances with popularity measures of ligandsadd_ligand_popularity_measures_to_perfs
Add a new data source to the modeladd_new_datasource
Convert aliases to official gene symbols in a Seurat Objectalias_to_symbol_seurat
Annotation table of all data sources used in the NicheNet modelannotation_data_sources
Apply hub corrections to the weighted integrated ligand-signaling and gene regulatory networkapply_hub_corrections
Assess the influence of an individual data source on ligand-target probability scoresassess_influence_source
Assess probability that a target gene belongs to the geneset based on a multi-ligand random forest modelassess_rf_class_probabilities
Assign ligands to cell typesassign_ligands_to_celltype
Run ligand activity analysis with bootstrapbootstrap_ligand_activity_analysis
Calculate differential expression of one cell type versus all other cell typescalculate_de
Determine the fraction of genes belonging to the geneset or background and to the top-predicted genes.calculate_fraction_top_predicted
Perform a Fisher's exact test to determine whether genes belonging to the gene set of interest are more likely to be part of the top-predicted targets.calculate_fraction_top_predicted_fisher
Calculate differential expression of cell types in one niche versus all other niches of interest.calculate_niche_de
Calculate differential expression of receiver cell type in one niche versus all other niches of interest: focus on finding DE genescalculate_niche_de_targets
Calculate ligand p-values from the bootstrapped ligand activity analysiscalculate_p_value_bootstrap
Calculate differential expression between spatially different subpopulations of the same cell typecalculate_spatial_DE
Assess how well classification predictions accord to the expected responseclassification_evaluation_continuous_pred_wrapper
Clear Database Cacheclear_database_cache
Combine the differential expression information of ligands in the sender celltypes with the differential expression information of their cognate receptors in the receiver cell typescombine_sender_receiver_de
Construct a ligand-target probability matrix for ligands of interest.construct_ligand_target_matrix
Construct a ligand-tf signaling probability matrix for ligands of interest.construct_ligand_tf_matrix
Construct a ligand-target model given input parameters.construct_model
Construct a randomised ligand-target model given input parameters.construct_random_model
Construct a tf-target matrix.construct_tf_target_matrix
Construct weighted layer-specific networksconstruct_weighted_networks
Convert aliases to official gene symbolsconvert_alias_to_symbols
Convert cluster assignment to settings format suitable for target gene prediction.convert_cluster_to_settings
Convert expression settings to correct settings format for evaluation of target gene prediction.convert_expression_settings_evaluation
Convert expression settings to correct settings format for evaluation of target gene log fold change prediction (regression).convert_expression_settings_evaluation_regression
Convert gene list to correct settings format for evaluation of target gene prediction.convert_gene_list_settings_evaluation
Convert human gene symbols to their mouse one-to-one orthologs.convert_human_to_mouse_symbols
Convert mouse gene symbols to their human one-to-one orthologs.convert_mouse_to_human_symbols
Convert settings to correct settings format for ligand prediction.convert_settings_ligand_prediction
Convert settings to correct settings format for TF prediction.convert_settings_tf_prediction
Converts expression settings to format in which the total number of potential ligands is reduced up to n top-predicted active ligands.convert_settings_topn_ligand_prediction
Prepare single-cell expression data to perform ligand activity analysisconvert_single_cell_expression_to_settings
Adapt a ligand-target probability matrix construced via PPR by correcting for network topolgoy.correct_topology_ppr
Prepare extracted ligand-target signaling network for visualization with DiagrammeR.diagrammer_format_signaling_graph
Estimate data source weights of data sources of interest based on leave-one-in and leave-one-out characterization performances.estimate_source_weights_characterization
Evaluation of ligand activity prediction based on ligand importance scores.evaluate_importances_ligand_prediction
Evaluate ligand activity predictions for different bins/groups of targets genesevaluate_ligand_prediction_per_bin
Construct and evaluate a ligand-target model given input parameters.evaluate_model
Construct and evaluate a ligand-target model given input parameters (for application purposes).evaluate_model_application
Construct and evaluate a ligand-target model given input parameters (for application purposes + multi-ligand predictive model).evaluate_model_application_multi_ligand
Construct and evaluate a ligand-target model given input parameters with the purpose of evaluating cross-validation models.evaluate_model_cv
Evaluation of target gene prediction for multiple ligands.evaluate_multi_ligand_target_prediction
Evaluation of target gene value prediction for multiple ligands (regression).evaluate_multi_ligand_target_prediction_regression
Construct and evaluate a randomised ligand-target model given input parameters.evaluate_random_model
Evaluation of ligand activity prediction performance of single ligand importance scores: aggregate all datasets.evaluate_single_importances_ligand_prediction
Evaluation of target gene prediction.evaluate_target_prediction
Evaluation of target gene prediction.evaluate_target_prediction_interprete
Evaluate target gene predictions for different bins/groups of targets genesevaluate_target_prediction_per_bin
Evaluation of target gene value prediction (regression).evaluate_target_prediction_regression
Expression datasets for validationexpression_settings_validation
Extract ligands of interest from settingsextract_ligands_from_settings
Get the predicted top n percentage ligands of a target of interestextract_top_fraction_ligands
Get the predicted top n percentage target genes of a ligand of interestextract_top_fraction_targets
Get the predicted top n ligands of a target gene of interestextract_top_n_ligands
Get the predicted top n target genes of a ligand of interestextract_top_n_targets
Gene annotation information: version 2 - january 2022geneinfo_2022
Gene annotation information: version 2 - january 2022 - suited for alias conversiongeneinfo_alias_human
Gene annotation information: version 2 - january 2022 - suited for alias conversiongeneinfo_alias_mouse
Gene annotation informationgeneinfo_human
Generate tables used for 'generate_prioritization_tables'generate_info_tables
Perform a prioritization of cell-cell interactions (similar to MultiNicheNet).generate_prioritization_tables
Get active ligand-receptor network for cellular interaction between a sender and receiver cell.get_active_ligand_receptor_network
Get active ligand-target network in data frame format.get_active_ligand_target_df
Get active ligand-target matrix.get_active_ligand_target_matrix
Get active gene regulatory network in a receiver cell.get_active_regulatory_network
Get active signaling network in a receiver cell.get_active_signaling_network
Get Cache Statisticsget_database_cache_stats
Determine expressed genes of a cell type from an input objectget_expressed_genes get_expressed_genes.default get_expressed_genes.Seurat
Calculate average of gene expression per cell type.get_exprs_avg
Get log fold change values of genes in cell type of interestget_lfc_celltype
Calculate the ligand activities and infer ligand-target links based on a list of niche-specific genes per receiver cell typeget_ligand_activities_targets
Get ligand-target signaling paths between ligand(s) and target gene(s) of interestget_ligand_signaling_path
Get ligand-target signaling paths between ligand(s), receptors, and target gene(s) of interestget_ligand_signaling_path_with_receptor
Regression analysis between popularity of left-out ligands for ligand activity prediction performanceget_ligand_slope_ligand_prediction_popularity
Get ligand-target links of interestget_ligand_target_links_oi
Get ligand importances from a multi-ligand classfication model.get_multi_ligand_importances
Get ligand importances from a multi-ligand regression model.get_multi_ligand_importances_regression
Get ligand importances from a multi-ligand trained random forest model.get_multi_ligand_rf_importances
Get ligand importances from a multi-ligand trained random forest regression model.get_multi_ligand_rf_importances_regression
Get the number of times of gene is mentioned in the pubmed literatureget_ncitations_genes
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.get_non_spatial_de
Get optimized parameters from the output of 'run_nsga2R_cluster'.get_optimized_parameters_nsga2r
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.get_prioritization_tables
Get ligand importances based on target gene prediction performance of single ligands.get_single_ligand_importances
Get ligand importances based on target gene value prediction performance of single ligands (regression).get_single_ligand_importances_regression
Regression analysis between ligand popularity and target gene predictive performanceget_slope_ligand_popularity
Regression analysis between target gene popularity and target gene predictive performanceget_slope_target_gene_popularity
Regression analysis between target gene popularity and ligand activity predictive performanceget_slope_target_gene_popularity_ligand_prediction
Get a set of predicted target genes of a ligand of interestget_target_genes_ligand_oi
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.get_top_predicted_genes
Get the weighted ligand-receptor links between a possible ligand and its receptorsget_weighted_ligand_receptor_links
Infer weighted active ligand-target links between a possible ligand and target genes of interestget_weighted_ligand_target_links
Optimized hyperparameter valueshyperparameter_list
Get the data sources that support the specific interactions in the extracted ligand-target signaling subnetworkinfer_supporting_datasources
Calculate ligand activity performance without considering evaluation datasets belonging to the top i most frequently cited ligandsligand_activity_performance_top_i_removed
make_circos_lrmake_circos_lr
Draw a circos plotmake_circos_plot
Convert probabilistic ligand-target matrix to a discrete one.make_discrete_ligand_target_matrix
Make a ggplot heatmap object from an input ligand-target matrix.make_heatmap_bidir_lt_ggplot
Make a ggplot heatmap object from an input matrix (2-color).make_heatmap_ggplot
make_ligand_activity_target_exprs_plotmake_ligand_activity_target_exprs_plot
make_ligand_receptor_lfc_plotmake_ligand_receptor_lfc_plot
make_ligand_receptor_lfc_spatial_plotmake_ligand_receptor_lfc_spatial_plot
Make a line plotmake_line_plot
Make a "mushroom plot" of ligand-receptor interactionsmake_mushroom_plot
Make a ggplot heatmap object from an input matrix (3-color).make_threecolor_heatmap_ggplot
Optimization of objective functions via model-based optimization (mlrMBO).mlrmbo_optimization
Prediction of ligand activity prediction by a model trained on ligand importance scores.model_based_ligand_activity_prediction
Construct and evaluate a ligand-target model given input parameters with the purpose of hyperparameter optimization using mlrMBO.model_evaluation_hyperparameter_optimization_mlrmbo
Construct and evaluate a ligand-target model given input parameters with the purpose of parameter optimization for multi-ligand application.model_evaluation_optimization_application
Construct and evaluate a ligand-target model given input parameters with the purpose of parameter optimization with mlrMBO.model_evaluation_optimization_mlrmbo
Construct and evaluate a ligand-target model with the purpose of parameter optimization with NSGA-II.model_evaluation_optimization_nsga2r
Change values in a tibble if some condition is fulfilled.mutate_cond
Number of citations for genesncitations
Perform NicheNet analysis on Seurat object: explain DE between conditionsnichenet_seuratobj_aggregate
Perform NicheNet analysis on Seurat object: explain DE between two cell clusters from separate conditionsnichenet_seuratobj_aggregate_cluster_de
Perform NicheNet analysis on Seurat object: explain DE between two cell clustersnichenet_seuratobj_cluster_de
Normalize single-cell ligand activitiesnormalize_single_cell_ligand_activities
Optimized data source weightsoptimized_source_weights_df
Predict activities of ligands in regulating expression of a gene set of interestpredict_ligand_activities
Single-cell ligand activity predictionpredict_single_cell_ligand_activities
Prepare circos visualizationprepare_circos_visualization
Prepare ligand-receptor visualizationprepare_ligand_receptor_visualization
Prepare heatmap visualization of the ligand-target links starting from a ligand-target tibble.prepare_ligand_target_visualization
Prepare settings for leave-one-in characterizationprepare_settings_leave_one_in_characterization
Prepare settings for leave-one-out characterizationprepare_settings_leave_one_out_characterization
Prepare settings for one-vs-one characterizationprepare_settings_one_vs_one_characterization
Process the output of model evaluation for data source characterization purposes on the ligand prediction performanceprocess_characterization_ligand_prediction
Process the output of model evaluation for data source characterization purposes on the ligand prediction performance (for every importance score individually)process_characterization_ligand_prediction_single_measures
Process the output of model evaluation for data source characterization purposes on the popularity bias assessment of ligand activity performanceprocess_characterization_popularity_slopes_ligand_prediction
Process the output of model evaluation for data source characterization purposes on the popularity bias assessment of target prediction performanceprocess_characterization_popularity_slopes_target_prediction
Process the output of model evaluation for data source characterization purposes on the target prediction performanceprocess_characterization_target_prediction
Process the output of model evaluation for data source characterization purposes on the target prediction performance (average)process_characterization_target_prediction_average
Process the output of mlrmbo multi-objective optimization to extract optimal parameter values.process_mlrmbo_nichenet_optimization
Process the DE output of `calculate_niche_de`process_niche_de
Processing differential expression output of the receiver cell typesprocess_receiver_target_de
Process the spatialDE outputprocess_spatial_de
Process DE or expression information into intercellular communication focused information.process_table_to_ic
Randomize an integrated network by shuffling its source networksrandomize_complete_network_source_specific
Randomize a network of a particular data source.randomize_datasource_network
Randomize a networkrandomize_network
Run NSGA-II for parameter optimization.run_nsga2R_cluster
Cut off outer quantiles and rescale to a [0, 1] rangescale_quantile
Normalize values in a vector by quantile scaling and add a pseudovalue of 0.001scale_quantile_adapted
Normalize values in a vector by the modified z-score method.scaling_modified_zscore
Normalize values in a vector by the z-score methodscaling_zscore
Assess how well cells' ligand activities predict a binary property of interest of cells.single_ligand_activity_score_classification
Perform a correlation and regression analysis between cells' ligand activities and property scores of interestsingle_ligand_activity_score_regression
Data source weightssource_weights_df
Visualize parameter values from the output of 'run_nsga2R_cluster'.visualize_parameter_values
Visualize parameter values from the output of 'run_nsga2R_cluster' across cross-validation folds.visualize_parameter_values_across_folds
Calculate average performance of datasets of a specific ligand.wrapper_average_performances
Evaluation of ligand activity prediction performance of single ligand importance scores: each dataset individually.wrapper_evaluate_single_importances_ligand_prediction