Package: SPONGE 1.27.0

Markus List

SPONGE: Sparse Partial Correlations On Gene Expression

This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.

Authors:Markus List [aut, cre], Markus Hoffmann [aut]

SPONGE_1.27.0.tar.gz
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SPONGE.pdf |SPONGE.html✨
SPONGE/json (API)
NEWS

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

Peer review:

Datasets:

On BioConductor:SPONGE-1.27.0(bioc 3.20)SPONGE-1.26.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

29 exports 1.16 score 217 dependencies 1 dependents 11 mentions

Last updated 2 months agofrom:485926e009

Exports:build_classifier_central_genescalibrate_modeldefine_modulesenrichment_modulesfilter_ceRNA_networkget_central_modulesplot_accuracy_sensitivity_specificityplot_confusion_matricesplot_density_scoresplot_heatmapsplot_involved_miRNAs_to_modulesplot_top_modulesprepare_metabric_for_spongEffectsprepare_tcga_for_spongEffectsRandom_spongEffectssample_zero_mscor_covsample_zero_mscor_dataspongesponge_build_null_modelsponge_compute_p_valuessponge_edge_centralitiessponge_gene_miRNA_interaction_filtersponge_networksponge_node_centralitiessponge_plot_networksponge_plot_network_centralitiessponge_plot_simulation_resultssponge_run_benchmarksponge_subsampling

Dependencies:abindAnnotationDbiaskpassbackportsbase64encbayestestRBiobaseBiocFileCacheBiocGenericsbiomaRtBiostringsbitbit64blobbootbroombslibcachemcallrcarcarDatacaretcellrangercheckmatecirclizeclassclicliprclockclueclustercodetoolscolorspaceComplexHeatmapconflictedcorrplotcowplotcpp11crayoncurlcvmsdata.tabledatawizardDBIdbplyrDerivdiagramdigestdoBydoParalleldoRNGdplyrdtplyre1071ellipsisevaluateexpmfansifarverfastmapfilelockfontawesomeforcatsforeachfsfuturefuture.applygarglegenericsGenomeInfoDbGenomeInfoDbDataGetoptLongggplot2ggpubrggrepelggridgesggsciggsignifglmnetGlobalOptionsglobalsgluegoogledrivegooglesheets4gowergRbasegridExtragroupdata2gtablehardhathavenhighrhmshtmltoolshttrhttr2idsigraphinsightipredIRangesisobanditeratorsjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglatticelavalifecyclelistenvlme4logginglubridatemagrittrMASSMatrixMatrixModelsmatrixStatsmemoiseMetBrewermgcvmicrobenchmarkmimeminqamiRBaseConverterModelMetricsmodelrMuMInmunsellnlmenloptrnnetnumbersnumDerivopensslparallellyparameterspbkrtestpillarpkgconfigplogrplyrpngpolynomppcorprettyunitspROCprocessxprodlimprogressprogressrproxypspurrrquantregR6raggrandomForestrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadrreadxlrearrrrecipesrematchrematch2reprexreshape2rjsonrlangrmarkdownrngtoolsrpartRSQLiterstatixrstudioapirvestS4VectorssassscalesselectrshapeSparseMSQUAREMstringistringrsurvivalsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetimeDatetinytextnettzdbUCSC.utilsutf8uuidvctrsviridisLitevroomwithrxfunxml2XVectoryamlzlibbioc

Sparse Partial correlation ON Gene Expression with SPONGE

Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H. Schulz

Rendered fromSPONGE.Rmdusingknitr::rmarkdownon Jun 27 2024.

Last update: 2022-04-19
Started: 2017-06-08

spongeEffects.Rmd

Hoffmann M - Boniolo F

Rendered fromspongEffects.Rmdusingknitr::rmarkdownon Jun 27 2024.

Last update: 2022-04-26
Started: 2022-02-10

Readme and manuals

Help Manual

Help pageTopics
build classifiers for central genesbuild_classifier_central_genes
tests and trains a model for a disease using a training and test data set (e.g., TCGA-BRCA and METABRIC)calibrate_model
ceRNA interactionsceRNA_interactions
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.check_and_convert_expression_data
Functions to define Sponge modules, created as all the first neighbors of the most central genesdefine_modules
Calculate enrichment scoresenrichment_modules
example potential central nodesensembl.df
prepare ceRNA network and network centralities from SPONGE / SPONGEdb for spongEffectsfilter_ceRNA_network
Function to calculate centrality scores Calculation of combined centrality scores as proposed by Del Rio et al. (2009)fn_combined_centrality
discretize #' (functions taken from: Jerby-Arnon et al. 2018)fn_discretize_spongeffects
Computes an elastic net modelfn_elasticnet
Calibrate classification methodfn_exact_match_summary
Preprocessing ceRNA networkfn_filter_network
Perform F test for gene-miRNA elastic net modelfn_gene_miRNA_F_test
Extract the model coefficients from an elastic net modelfn_get_model_coef
Compute the residual sum of squares error for an elastic net modelfn_get_rss
Function to calculate semi random enrichment scores of modules OE (functions taken from: Jerby-Arnon et al. 2018)fn_get_semi_random_OE
Identify miRNAs for which both genes have miRNA binding sites aka miRNA response elements in the competing endogeneous RNA hypothesisfn_get_shared_miRNAs
Function to calculate enrichment scores of modules OE (functions taken from: Jerby-Arnon et al. 2018)fn_OE_module
RF classification modelfn_RF_classifier
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).fn_weighted_degree
Gene expression test data setgene_expr
Compute all pairwise interactions for a number of genes as indicesgenes_pairwise_combinations
prepare ceRNA network and network centralities from SPONGE / SPONGEdbget_central_modules
miRNA expression test data setmir_expr
miRNA / gene interactionsmir_interactions
mircode predicted miRNA gene interactionsmircode_ensg
mircode predicted miRNA gene interactionsmircode_symbol
list of plots for (1) accuracy and (2) sensitivity + specificity (see Boniolo and Hoffmann 2022 et al. Fig. 3a and Fig. 3b)plot_accuracy_sensitivity_specificity
plots the confusion matrix from spongEffects train_and_test() (see Boniolo and Hoffmann 2022 et al. Fig. 3a and Fig. 3b)plot_confusion_matrices
plots the density of the model scores for subtypes (see Boniolo and Hoffmann 2022 et al. Fig. 2)plot_density_scores
plots the heatmaps from training_and_test_model (see Boniolo and Hoffmann 2022 et al. Fig. 6)plot_heatmaps
plots the heatmap of miRNAs invovled in the interactions of the modules (see Boniolo and Hoffmann 2022 et al. Fig. 7a)plot_involved_miRNAs_to_modules
plots the top x gini index modules (see Boniolo and Hoffmann 2022 et al. Figure 5)plot_top_modules
covariance matrices under the null hypothesis that sensitivity correlation is zeroprecomputed_cov_matrices
A null model for testing purposesprecomputed_null_model
prepare METABRIC formats for spongEffectsprepare_metabric_for_spongEffects
prepare TCGA formats for spongEffectsprepare_tcga_for_spongEffects
build random classifiersRandom_spongEffects
Sampling zero multiple miRNA sensitivity covariance matricessample_zero_mscor_cov
Sample mscor coefficients from pre-computed covariance matricessample_zero_mscor_data
Compute competing endogeneous RNA interactions using Sparse Partial correlations ON Gene Expression (SPONGE)sponge
Build null model for p-value computationsponge_build_null_model
Compute p-values for SPONGE interactionssponge_compute_p_values
Computes edge centralitiessponge_edge_centralities
Determine miRNA-gene interactions to be considered in SPONGEsponge_gene_miRNA_interaction_filter
Prepare a sponge network for plottingsponge_network
Computes various node centralitiessponge_node_centralities
Plot a sponge networksponge_plot_network
plot node network centralitiessponge_plot_network_centralities
Plot simulation results for different null modelssponge_plot_simulation_results
run sponge benchmark where various settings, i.e. with or without regression, single or pooled miRNAs, are compared.sponge_run_benchmark
Sponge subsamplingsponge_subsampling
targetscan predicted miRNA gene interactionstargetscan_ensg
targetscan predicted miRNA gene interactionstargetscan_symbol
example test expression data for spongEffectstest_cancer_gene_expr
example test sample meta data for spongEffectstest_cancer_metadata
example test miRNA data for spongEffectstest_cancer_mir_expr
example training expression data for spongEffectstrain_cancer_gene_expr
example training sample meta data for spongEffectstrain_cancer_metadata
example training miRNA data for spongEffectstrain_cancer_mir_expr
example train ceRNA interactions for spongEffectstrain_ceRNA_interactions
example train network centralities for spongEffectstrain_network_centralities