Random_spongEffects     build random classifiers
build_classifier_central_genes
                        build classifiers for central genes
calibrate_model         tests and trains a model for a disease using a
                        training and test data set (e.g., TCGA-BRCA and
                        METABRIC)
ceRNA_interactions      ceRNA interactions
check_and_convert_expression_data
                        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.
define_modules          Functions to define Sponge modules, created as
                        all the first neighbors of the most central
                        genes
enrichment_modules      Calculate enrichment scores
ensembl.df              example potential central nodes
filter_ceRNA_network    prepare ceRNA network and network centralities
                        from SPONGE / SPONGEdb for spongEffects
fn_OE_module            Function to calculate enrichment scores of
                        modules OE (functions taken from: Jerby-Arnon
                        et al. 2018)
fn_RF_classifier        RF classification model
fn_combined_centrality
                        Function to calculate centrality scores
                        Calculation of combined centrality scores as
                        proposed by Del Rio et al. (2009)
fn_discretize_spongeffects
                        discretize #' (functions taken from:
                        Jerby-Arnon et al. 2018)
fn_elasticnet           Computes an elastic net model
fn_exact_match_summary
                        Calibrate classification method
fn_filter_network       Preprocessing ceRNA network
fn_gene_miRNA_F_test    Perform F test for gene-miRNA elastic net model
fn_get_model_coef       Extract the model coefficients from an elastic
                        net model
fn_get_rss              Compute the residual sum of squares error for
                        an elastic net model
fn_get_semi_random_OE   Function to calculate semi random enrichment
                        scores of modules OE (functions taken from:
                        Jerby-Arnon et al. 2018)
fn_get_shared_miRNAs    Identify miRNAs for which both genes have miRNA
                        binding sites aka miRNA response elements in
                        the competing endogeneous RNA hypothesis
fn_weighted_degree      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).
gene_expr               Gene expression test data set
genes_pairwise_combinations
                        Compute all pairwise interactions for a number
                        of genes as indices
get_central_modules     prepare ceRNA network and network centralities
                        from SPONGE / SPONGEdb
mir_expr                miRNA expression test data set
mir_interactions        miRNA / gene interactions
mircode_ensg            mircode predicted miRNA gene interactions
mircode_symbol          mircode predicted miRNA gene interactions
plot_accuracy_sensitivity_specificity
                        list of plots for (1) accuracy and (2)
                        sensitivity + specificity (see Boniolo and
                        Hoffmann 2022 et al. Fig. 3a and Fig. 3b)
plot_confusion_matrices
                        plots the confusion matrix from spongEffects
                        train_and_test() (see Boniolo and Hoffmann 2022
                        et al. Fig. 3a and Fig. 3b)
plot_density_scores     plots the density of the model scores for
                        subtypes (see Boniolo and Hoffmann 2022 et al.
                        Fig. 2)
plot_heatmaps           plots the heatmaps from training_and_test_model
                        (see Boniolo and Hoffmann 2022 et al. Fig. 6)
plot_involved_miRNAs_to_modules
                        plots the heatmap of miRNAs invovled in the
                        interactions of the modules (see Boniolo and
                        Hoffmann 2022 et al. Fig. 7a)
plot_top_modules        plots the top x gini index modules (see Boniolo
                        and Hoffmann 2022 et al. Figure 5)
precomputed_cov_matrices
                        covariance matrices under the null hypothesis
                        that sensitivity correlation is zero
precomputed_null_model
                        A null model for testing purposes
prepare_metabric_for_spongEffects
                        prepare METABRIC formats for spongEffects
prepare_tcga_for_spongEffects
                        prepare TCGA formats for spongEffects
sample_zero_mscor_cov   Sampling zero multiple miRNA sensitivity
                        covariance matrices
sample_zero_mscor_data
                        Sample mscor coefficients from pre-computed
                        covariance matrices
sponge                  Compute competing endogeneous RNA interactions
                        using Sparse Partial correlations ON Gene
                        Expression (SPONGE)
sponge_build_null_model
                        Build null model for p-value computation
sponge_compute_p_values
                        Compute p-values for SPONGE interactions
sponge_edge_centralities
                        Computes edge centralities
sponge_gene_miRNA_interaction_filter
                        Determine miRNA-gene interactions to be
                        considered in SPONGE
sponge_network          Prepare a sponge network for plotting
sponge_node_centralities
                        Computes various node centralities
sponge_plot_network     Plot a sponge network
sponge_plot_network_centralities
                        plot node network centralities
sponge_plot_simulation_results
                        Plot simulation results for different null
                        models
sponge_run_benchmark    run sponge benchmark where various settings,
                        i.e. with or without regression, single or
                        pooled miRNAs, are compared.
sponge_subsampling      Sponge subsampling
targetscan_ensg         targetscan predicted miRNA gene interactions
targetscan_symbol       targetscan predicted miRNA gene interactions
test_cancer_gene_expr   example test expression data for spongEffects
test_cancer_metadata    example test sample meta data for spongEffects
test_cancer_mir_expr    example test miRNA data for spongEffects
train_cancer_gene_expr
                        example training expression data for
                        spongEffects
train_cancer_metadata   example training sample meta data for
                        spongEffects
train_cancer_mir_expr   example training miRNA data for spongEffects
train_ceRNA_interactions
                        example train ceRNA interactions for
                        spongEffects
train_network_centralities
                        example train network centralities for
                        spongEffects
