{ "_id": "6a1df70b1d7bb097a0a5bf3c", "Package": "SPONGE", "Type": "Package", "Title": "Sparse Partial Correlations On Gene Expression", "Version": "1.35.3", "Encoding": "UTF-8", "Authors@R": "c(person(given = \"Markus\",\nfamily = \"List\",\nrole = c(\"aut\", \"cre\"),\nemail = \"markus.list@tum.de\",\ncomment = c(ORCID = \"0000-0002-0941-4168\")),\nperson(given = \"Markus\",\nfamily = \"Hoffmann\",\nrole = c(\"aut\"),\nemail = \"markus.hoffmann@nih.gov\",\ncomment = c(ORCID = \"0000-0002-1920-288X\")),\nperson(given = \"Lena\",\nfamily = \"Strasser\",\nrole = c(\"aut\"),\nemail = \"lena.j.strasser@tum.de\",\ncomment = c(ORCID = \"0009-0007-7881-6818\")),\nperson(given = \"Fabio\",\nfamily = \"Boniolo\",\nrole = c(\"aut\")),\nperson(given = \"Azim Dehghani\",\nfamily = \"Amirabad\",\nrole = c(\"aut\")),\nperson(given = \"Dennis\",\nfamily = \"Kostka\",\nrole = c(\"aut\")),\nperson(given = \"Marcel H.\",\nfamily = \"Schulz\",\nrole = c(\"aut\")))", "Description": "This package provides methods to efficiently detect\ncompetitive endogeneous RNA interactions between two genes.\nSuch interactions are mediated by one or several miRNAs such\nthat both gene and miRNA expression data for a larger number of\nsamples is needed as input. The SPONGE package now also\nincludes spongEffects: ceRNA modules offer patient-specific\ninsights into the miRNA regulatory landscape.", "License": "GPL (>=3)", "LazyData": "TRUE", "LazyDataCompression": "xz", "VignetteBuilder": "knitr", "biocViews": "GeneExpression, Transcription, GeneRegulation,\nNetworkInference, Transcriptomics, SystemsBiology, Regression,\nRandomForest, MachineLearning", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libpng-dev\nlibxml2-dev libssl-dev perl zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-06-01 13:44:15 UTC", "RemoteUrl": "https://github.com/bioc/SPONGE", "RemoteRef": "HEAD", "RemoteSha": "48ad8f6a345400acedfab41bdf0bc4b33f4fc2ee", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 21:07:27 UTC", "User": "root" }, "Author": "Markus List [aut, cre] (ORCID: ),\nMarkus Hoffmann [aut] (ORCID: ),\nLena Strasser [aut] (ORCID: ),\nFabio Boniolo [aut],\nAzim Dehghani Amirabad [aut],\nDennis Kostka [aut],\nMarcel H. 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spongEffects", "object": "train_network_centralities", "class": [ "data.table", "data.frame" ], "fields": [ "gene", "degree", "eigenvector", "betweenness", "page_rank" ], "rows": 259, "table": true, "tojson": true } ], "_help": [ { "page": "build_classifier_central_genes", "title": "build classifiers for central genes", "topics": [ "build_classifier_central_genes" ] }, { "page": "calibrate_model", "title": "tests and trains a model for a disease using a training and test data set (e.g., TCGA-BRCA and METABRIC)", "topics": [ "calibrate_model" ] }, { "page": "ceRNA_interactions", "title": "ceRNA interactions", "topics": [ "ceRNA_interactions" ] }, { "page": "check_and_convert_expression_data", "title": "Checks if expression data is in matrix or ExpressionSet format and converts the latter to a standard matrix. Alternatively, a big.matrix descriptor object can be supplied to make use of shared memory between parallelized workers through the bigmemory package.", "topics": [ "check_and_convert_expression_data" ] }, { "page": "define_modules", "title": "Functions to define Sponge modules, created as all the first neighbors of the most central genes", "topics": [ "define_modules" ] }, { "page": "enrichment_modules", "title": "Calculate enrichment scores", "topics": [ "enrichment_modules" ] }, { "page": "ensembl.df", "title": "example potential central nodes", "topics": [ "ensembl.df" ] }, { "page": "filter_ceRNA_network", "title": "prepare ceRNA network and network centralities from SPONGE / SPONGEdb for spongEffects", "topics": [ "filter_ceRNA_network" ] }, { "page": "fn_combined_centrality", "title": "Function to calculate centrality scores Calculation of combined centrality scores as proposed by Del Rio et al. (2009)", "topics": [ "fn_combined_centrality" ] }, { "page": "fn_discretize_spongeffects", "title": "discretize #' (functions taken from: Jerby-Arnon et al. 2018)", "topics": [ "fn_discretize_spongeffects" ] }, { "page": "fn_elasticnet", "title": "Computes an elastic net model", "topics": [ "fn_elasticnet" ] }, { "page": "fn_exact_match_summary", "title": "Calibrate classification method", "topics": [ "fn_exact_match_summary" ] }, { "page": "fn_filter_network", "title": "Preprocessing ceRNA network", "topics": [ "fn_filter_network" ] }, { "page": "fn_gene_miRNA_F_test", "title": "Perform F test for gene-miRNA elastic net model", "topics": [ "fn_gene_miRNA_F_test" ] }, { "page": "fn_get_model_coef", "title": "Extract the model coefficients from an elastic net model", "topics": [ "fn_get_model_coef" ] }, { "page": "fn_get_rss", "title": "Compute the residual sum of squares error for an elastic net model", "topics": [ "fn_get_rss" ] }, { "page": "fn_get_semi_random_OE", "title": "Function to calculate semi random enrichment scores of modules OE (functions taken from: Jerby-Arnon et al. 2018)", "topics": [ "fn_get_semi_random_OE" ] }, { "page": "fn_get_shared_miRNAs", "title": "Identify miRNAs for which both genes have miRNA binding sites aka miRNA response elements in the competing endogeneous RNA hypothesis", "topics": [ "fn_get_shared_miRNAs" ] }, { "page": "fn_OE_module", "title": "Function to calculate enrichment scores of modules OE (functions taken from: Jerby-Arnon et al. 2018)", "topics": [ "fn_OE_module" ] }, { "page": "fn_RF_classifier", "title": "RF classification model", "topics": [ "fn_RF_classifier" ] }, { "page": "fn_weighted_degree", "title": "Function to calculate centrality scores Calculation of weighted degree scores based on Opsahl et al. (2010) Hyperparameter to tune: Alpha = 0 -> degree centrality as defined in Freeman, 1978 (number of edges).", "topics": [ "fn_weighted_degree" ] }, { "page": "gene_expr", "title": "Gene expression test data set", "topics": [ "gene_expr" ] }, { "page": "genes_pairwise_combinations", "title": "Compute all pairwise interactions for a number of genes as indices", "topics": [ "genes_pairwise_combinations" ] }, { "page": "get_central_modules", "title": "prepare ceRNA network and network centralities from SPONGE / SPONGEdb", "topics": [ "get_central_modules" ] }, { "page": "mir_expr", "title": "miRNA expression test data set", "topics": [ "mir_expr" ] }, { "page": "mir_interactions", "title": "miRNA / gene interactions", "topics": [ "mir_interactions" ] }, { "page": "mircode_ensg", "title": "mircode predicted miRNA gene interactions", "topics": [ "mircode_ensg" ] }, { "page": "mircode_symbol", "title": "mircode predicted miRNA gene interactions", "topics": [ "mircode_symbol" ] }, { "page": "plot_accuracy_sensitivity_specificity", "title": "list of plots for (1) accuracy and (2) sensitivity + specificity (see Boniolo and Hoffmann 2022 et al. Fig. 3a and Fig. 3b)", "topics": [ "plot_accuracy_sensitivity_specificity" ] }, { "page": "plot_confusion_matrices", "title": "plots the confusion matrix from spongEffects train_and_test() (see Boniolo and Hoffmann 2022 et al. Fig. 3a and Fig. 3b)", "topics": [ "plot_confusion_matrices" ] }, { "page": "plot_density_scores", "title": "plots the density of the model scores for subtypes (see Boniolo and Hoffmann 2022 et al. Fig. 2)", "topics": [ "plot_density_scores" ] }, { "page": "plot_heatmaps", "title": "plots the heatmaps from training_and_test_model (see Boniolo and Hoffmann 2022 et al. Fig. 6)", "topics": [ "plot_heatmaps" ] }, { "page": "plot_involved_miRNAs_to_modules", "title": "plots the heatmap of miRNAs invovled in the interactions of the modules (see Boniolo and Hoffmann 2022 et al. Fig. 7a)", "topics": [ "plot_involved_miRNAs_to_modules" ] }, { "page": "plot_top_modules", "title": "plots the top x gini index modules (see Boniolo and Hoffmann 2022 et al. Figure 5)", "topics": [ "plot_top_modules" ] }, { "page": "precomputed_cov_matrices", "title": "covariance matrices under the null hypothesis that sensitivity correlation is zero", "topics": [ "precomputed_cov_matrices" ] }, { "page": "precomputed_null_model", "title": "A null model for testing purposes", "topics": [ "precomputed_null_model" ] }, { "page": "prepare_metabric_for_spongEffects", "title": "prepare METABRIC formats for spongEffects", "topics": [ "prepare_metabric_for_spongEffects" ] }, { "page": "prepare_tcga_for_spongEffects", "title": "prepare TCGA formats for spongEffects", "topics": [ "prepare_tcga_for_spongEffects" ] }, { "page": "Random_spongEffects", "title": "Define random modules", "topics": [ "Random_spongEffects" ] }, { "page": "sample_zero_mscor_cov", "title": "Sampling zero multiple miRNA sensitivity covariance matrices", "topics": [ "sample_zero_mscor_cov" ] }, { "page": "sample_zero_mscor_data", "title": "Sample mscor coefficients from pre-computed covariance matrices", "topics": [ "sample_zero_mscor_data" ] }, { "page": "sponge", "title": "Compute competing endogeneous RNA interactions using Sparse Partial correlations ON Gene Expression (SPONGE)", "topics": [ "sponge" ] }, { "page": "sponge_build_null_model", "title": "Build null model for p-value computation", "topics": [ "sponge_build_null_model" ] }, { "page": "sponge_compute_p_values", "title": "Compute p-values for SPONGE interactions", "topics": [ "sponge_compute_p_values" ] }, { "page": "sponge_edge_centralities", "title": "Computes edge centralities", "topics": [ "sponge_edge_centralities" ] }, { "page": "sponge_gene_miRNA_interaction_filter", "title": "Determine miRNA-gene interactions to be considered in SPONGE", "topics": [ "sponge_gene_miRNA_interaction_filter" ] }, { "page": "sponge_network", "title": "Prepare a sponge network for plotting", "topics": [ "sponge_network" ] }, { "page": "sponge_node_centralities", "title": "Computes various node centralities", "topics": [ "sponge_node_centralities" ] }, { "page": "sponge_plot_network", "title": "Plot a sponge network", "topics": [ "sponge_plot_network" ] }, { "page": "sponge_plot_network_centralities", "title": "plot node network centralities", "topics": [ "sponge_plot_network_centralities" ] }, { "page": "sponge_plot_simulation_results", "title": "Plot simulation results for different null models", "topics": [ "sponge_plot_simulation_results" ] }, { "page": "sponge_run_benchmark", "title": "run sponge benchmark where various settings, i.e. with or without regression, single or pooled miRNAs, are compared.", "topics": [ "sponge_run_benchmark" ] }, { "page": "sponge_subsampling", "title": "Sponge subsampling", "topics": [ "sponge_subsampling" ] }, { "page": "targetscan_ensg", "title": "targetscan predicted miRNA gene interactions", "topics": [ "targetscan_ensg" ] }, { "page": "targetscan_symbol", "title": "targetscan predicted miRNA gene interactions", "topics": [ "targetscan_symbol" ] }, { "page": 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