Package: BioNERO 1.13.0

Fabricio Almeida-Silva

BioNERO: Biological Network Reconstruction Omnibus

BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.

Authors:Fabricio Almeida-Silva [cre, aut], Thiago Venancio [aut]

BioNERO_1.13.0.tar.gz
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BioNERO.pdf |BioNERO.html✨
BioNERO/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/almeidasilvaf/bionero/issues

Datasets:
  • filt.se - Filtered maize gene expression data from Shin et al., 2021.
  • og.zma.osa - Orthogroups between maize and rice
  • osa.se - Rice gene expression data from Shin et al., 2021.
  • zma.interpro - Maize Interpro annotation
  • zma.se - Maize gene expression data from Shin et al., 2021.
  • zma.tfs - Maize transcription factors

On BioConductor:BioNERO-1.13.0(bioc 3.20)BioNERO-1.12.0(bioc 3.19)

bioconductor-package

53 exports 1.16 score 155 dependencies 1 dependents

Last updated 2 months agofrom:27adc4d46d

Exports:check_SFTconsensus_modulesconsensus_SFT_fitconsensus_trait_corcor2adjcormat_to_edgelistdetect_communitiesdfs2oneenrichment_analysisexp_genes2orthogroupsexp_preprocessexp2corexp2gcnexp2gcn_blockwiseexp2grnfilter_by_variancegene_significanceget_edge_listget_HKget_hubs_gcnget_hubs_grnget_hubs_ppiget_neighborsgrn_average_rankgrn_combinedgrn_filtergrn_inferis_singletonmodPres_netrepmodPres_WGCNAmodule_enrichmentmodule_preservationmodule_stabilitymodule_trait_cornet_statsparse_orthofinderPC_correctionplot_dendro_and_colorsplot_eigengene_networkplot_expression_profileplot_gcnplot_gene_significanceplot_grnplot_heatmapplot_module_trait_corplot_ngenes_per_moduleplot_PCAplot_ppiq_normalizeremove_nonexpreplace_naSFT_fitZKfiltering

Dependencies:abindannotateAnnotationDbiaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbslibcachemcheckmatecirclizecliclueclustercodacodetoolscolorspaceComplexHeatmapcpp11crayoncurldata.tableDBIDelayedArraydigestdoParalleldplyrdynamicTreeCutedgeRevaluatefansifarverfastclusterfastmapfontawesomeforeachforeignformatRFormulafsfutile.loggerfutile.optionsgenefiltergenericsGENIE3GenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggdendroggnetworkggplot2ggrepelGlobalOptionsglueGO.dbgridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetshttrigraphimputeinfotheointergraphIRangesisobanditeratorsjquerylibjsonliteKEGGRESTknitrlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminetmunsellNetRepnetworknlmennetopensslpatchworkpillarpkgconfigplogrplyrpngpreprocessCoreR6rappdirsRColorBrewerRcppRcppArmadilloreshape2RhpcBLASctlrjsonrlangrmarkdownrpartRSQLiterstudioapiS4ArraysS4VectorssassscalesshapesnasnowSparseArraystatmodstatnet.commonstringistringrSummarizedExperimentsurvivalsvasystibbletidyselecttinytexUCSC.utilsutf8vctrsviridisviridisLiteWGCNAwithrxfunXMLxtableXVectoryamlzlibbioc

Gene coexpression network inference

Fabricio Almeida-Silva, Thiago Motta Venancio

Rendered fromvignette_01_GCN_inference.Rmdusingknitr::rmarkdownon Jun 25 2024.

Last update: 2023-08-21
Started: 2021-01-30

Gene regulatory network inference

Fabricio Almeida-Silva, Thiago Motta Venancio

Rendered fromvignette_02_GRN_inference.Rmdusingknitr::rmarkdownon Jun 25 2024.

Last update: 2023-08-21
Started: 2021-02-25

Network comparison: consensus modules and module preservation

Fabricio Almeida-Silva, Thiago Motta Venancio

Rendered fromvignette_03_network_comparison.Rmdusingknitr::rmarkdownon Jun 25 2024.

Last update: 2023-08-21
Started: 2021-03-01

Readme and manuals

Help Manual

Help pageTopics
Check scale-free topology fit for a given networkcheck_SFT
Identify consensus modules across independent data setsconsensus_modules
Pick power to fit networks to scale-free topologyconsensus_SFT_fit
Correlate set-specific modules and consensus modules to sample informationconsensus_trait_cor
Calculate an adjacency matrix from a correlation matrixcor2adj
Transform a correlation matrix to an edge listcormat_to_edgelist
Detect communities in a networkdetect_communities
Combine multiple expression tables (.tsv) into a single data framedfs2one
Perform overrepresentation analysis for a set of genesenrichment_analysis
Collapse gene-level expression data to orthogroup levelexp_genes2orthogroups
Preprocess expression data for network reconstructionexp_preprocess
Calculate pairwise correlations between genes in a matrixexp2cor
Infer gene coexpression network from gene expressionexp2gcn
Infer gene coexpression network from gene expression in a blockwise mannerexp2gcn_blockwise
Infer gene regulatory network from expression dataexp2grn
Filtered maize gene expression data from Shin et al., 2021.filt.se
Keep only genes with the highest variancesfilter_by_variance
Calculate gene significance for a given group of genesgene_significance
Get edge list from an adjacency matrix for a group of genesget_edge_list
Get housekeeping genes from global expression profileget_HK
Get GCN hubsget_hubs_gcn
Get hubs for gene regulatory networkget_hubs_grn get_hubs_ppi
Get 1st-order neighbors of a given gene or group of genesget_neighbors
Rank edge weights for GRNs and calculate average across different methodsgrn_average_rank
Infer gene regulatory network with multiple algorithms and combine results in a listgrn_combined
Filter a gene regulatory network based on optimal scale-free topology fitgrn_filter
Infer gene regulatory network with one of three algorithmsgrn_infer
Logical expression to check if gene or gene set is singleton or notis_singleton
Calculate module preservation between two expression data sets using NetRep's algorithmmodPres_netrep
Calculate module preservation between two expression data sets using WGCNA's algorithmmodPres_WGCNA
Perform enrichment analysis for coexpression network modulesmodule_enrichment
Calculate network preservation between two expression data setsmodule_preservation
Perform module stability analysismodule_stability
Correlate module eigengenes to traitmodule_trait_cor
Calculate network statisticsnet_stats
Orthogroups between maize and riceog.zma.osa
Rice gene expression data from Shin et al., 2021.osa.se
Parse orthogroups identified by OrthoFinderparse_orthofinder
Apply Principal Component (PC)-based correction for confounding artifactsPC_correction
Plot dendrogram of genes and modulesplot_dendro_and_colors
Plot eigengene networkplot_eigengene_network
Plot expression profile of given genes across samplesplot_expression_profile
Plot gene coexpression network from edge listplot_gcn
Plot a heatmap of gene significanceplot_gene_significance
Plot gene regulatory network from edge listplot_grn
Plot heatmap of hierarchically clustered sample correlations or gene expressionplot_heatmap
Plot a heatmap of module-trait correlationsplot_module_trait_cor
Plot number of genes per moduleplot_ngenes_per_module
Plot Principal Component Analysis (PCA) of samplesplot_PCA
Plot protein-protein interaction network from edge listplot_ppi
Quantile normalize the expression dataq_normalize
Remove genes that are not expressed based on a user-defined thresholdremove_nonexp
Remove missing values in a gene expression data framereplace_na
Pick power to fit network to a scale-free topologySFT_fit
Filter outlying samples based on the standardized connectivity (Zk) methodZKfiltering
Maize Interpro annotationzma.interpro
Maize gene expression data from Shin et al., 2021.zma.se
Maize transcription factorszma.tfs