# Sparse Partial Correlations On Gene Expression

## Documentation for package ‘SPONGE’ version 1.28.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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_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_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_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).
genes_pairwise_combinations | Compute all pairwise interactions for a number of genes as indices
gene_expr | Gene expression test data set
get_central_modules | prepare ceRNA network and network centralities from SPONGE / SPONGEdb
mircode_ensg | mircode predicted miRNA gene interactions
mircode_symbol | mircode predicted miRNA gene interactions
mir_expr | miRNA expression test data set
mir_interactions | 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
Random_spongEffects | build random classifiers
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