Package: GRaNIE 1.17.0

Christian Arnold

GRaNIE: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data

Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.

Authors:Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb]

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manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
GRaNIE/json (API)

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

Pkgdown/docs site:https://grp-zaugg.embl-community.io

On BioConductor:GRaNIE-1.17.0(bioc 3.24)GRaNIE-1.16.0(bioc 3.23)

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

softwaregeneexpressiongeneregulationnetworkinferencegenesetenrichmentbiomedicalinformaticsgeneticstranscriptomicsatacseqrnaseqgraphandnetworkregressiontranscriptionchipseq

4.71 score 17 scripts 326 downloads 43 exports 142 dependencies

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Exports:add_featureVariationadd_TF_gene_correlationaddConnections_peak_geneaddConnections_TF_peakaddDataaddSNPDataaddTFBSAR_classification_wrapperbuild_eGRN_graphcalculateCommunitiesEnrichmentcalculateCommunitiesStatscalculateGeneralEnrichmentcalculateTFEnrichmentchangeOutputDirectorydeleteIntermediateDatafilterConnectionsForPlottingfilterDatafilterGRNAndConnectGenesgenerateStatsSummarygetCountsgetGRNConnectionsgetGRNSummarygetParametersgetTopNodesinitializeGRNloadExampleObjectnGenesnPeaksnTFsoverlapPeaksAndTFBSperformAllNetworkAnalysesplot_stats_connectionSummaryplotCommunitiesEnrichmentplotCommunitiesStatsplotCorrelationsplotDiagnosticPlots_peakGeneplotDiagnosticPlots_TFPeaksplotDiagnosticPlots_TFPeaks_GCplotGeneralEnrichmentplotGeneralGraphStatsplotPCA_allplotTFEnrichmentvisualizeGRN

Dependencies:abindAnnotationDbiAnnotationFilterAnnotationHubaskpassbackportsBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocIOBiocManagerBiocParallelBiocVersionbiomaRtBiostringsbitbit64bitopsblobcachemcheckmatecigarillocirclizeclicliprclueclustercodetoolscolorspaceComplexHeatmapcpp11crayoncurldata.tableDBIdbplyrDelayedArrayDESeq2digestdoParalleldplyrensembldbfarverfastmapfilelockforcatsforeachformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomicAlignmentsGenomicFeaturesGenomicRangesGetoptLongggplot2GlobalOptionsglueGO.dbgraphgridExtragtablehmshttrhttr2igraphIRangesisobanditeratorsjsonliteKEGGRESTlabelinglambda.rlatticelazyevallifecyclelimmalocfitmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpatchworkpillarpkgconfigplyrpngprettyunitsprogressProtGenericspurrrR6rappdirsRColorBrewerRcppRcppArmadilloRCurlreadrreshape2restfulrRhtslibrjsonrlangRsamtoolsRSQLitertracklayerS4ArraysS4VectorsS7scalesSeqinfoshapesnowSparseArraySparseMstatmodstringistringrSummarizedExperimentsystibbletidyrtidyselecttopGOtzdbUCSC.utilsutf8vctrsviridisviridisLitevroomwithrXMLxml2XVectoryaml

Package Details
Motivation and Necessity | Installation | GRaNIE package and required dependency packages | Additional packages | Detailed information about the scope of the optional packages | Example Workflow | Example GRN object | Input | Open chromatin and RNA-seq data | TF and TFBS data | HOCOMOCO-derived TFBS and download links | Other TFBS sources | Importing TF and TFBS from the JASPAR databases directly in R | Sample metadata (optional but highly recommended) | Hi-C data (optional) | Capture Hi-C data / known promoter-enhancer links (optional) | SNP data (optional) | TF activity data (optional, coming soon) | Methodological Details and Basic Mode of Action | Data normalization | Normalization methods common for peaks and RNA data | Normalization methods specific for peaks data | Raw vs pre.normalized data | TF-peak connections | TF-peak connection types | GC correction | TF Activity connections | Calculating TF Activity | Importing TF Activity | Adding TF Activity TF-peak connections | Peak-gene associations | Building eGRNs: Linking TF-peak and peak-gene links and filtering | Background eGRN | Methods | Object and output details related to the background links | Constructing the eGRN graph | Enrichment analyses | Network visualization and visualization filtering | General comments | Changing the visualization parameters and layout | Filtering the network for visualization purposes | Feature variation quantification | Output | GRN object and results stored within | Object details | Results in the object | Object summary | Original and normalized data, annotations and provided metadata | Object data | Plots | PCA plots and results | TF-peak results and diagnostic plots | Overall connection summary | TF-specific plots | Connection summary | Correlation plots | Extra plots for the GC correction | Overall summary | Activator-repressor classification results and diagnostic plots | Peak-gene results and diagnostic plots | Correlation raw p-value distribution | Correlation coefficient distribution | Stratification with metadata and additional features | Filtered TF-peak-gene connections (eGRN table) | TF-gene connections | Connection summary plots | eGRN graph | Community information | Enrichment results and plots | General enrichment | Community enrichment | TF enrichment | Feature variation quantification | SNP data | Guidelines, Recommendations, Limitations, Scope | Package scope | Number of samples | Peaks | RNA-Seq | Transcription factor binding sites (TFBS) | Choice of correlation methods for TF-peak, peak-gene, TF-gene connections and outlier robustness | Peak-gene p-values accuracy and violations | eGRNs from single-cell data | Recapitulating object history, function parameters etc | Memory footprint and execution time, feasibility with large datasets | CPU time | Memory footprint | References

Last update: 2024-09-25
Started: 2022-09-14

GRaNIE single-cell eGRN inference
Motivation and Summary | General notes and sources | Prerequisites: a multimodal Seurat object | Our default preprocessing | RNA | ATAC | Integrating the modalities | Clustering and pseudobulk creation | Methods | Choosing the right number of clusters and working strategies for running GRaNIE | Filtering | Preparing the input data for GRaNIE | Metadata | TF database | Running GRaNIE | Scripts | Data processing and GRaNIE preparation | Current limitations | Example data | Further notes and FAQs | Session Info

Last update: 2024-09-02
Started: 2023-01-26

GRaNIE Workflow Example
Motivation and Summary | Example data | Example Workflow | Install suggested, additional packages for full functionality | Note on version compatibility and errors in the vignette | General notes | Reading the data required for the GRaNIE package | Initialize a GRaNIE object | Add data | Object history | Quality control 1: PCA plots | Add TFs and TFBS and overlap with peak | Filter data (optional) | Add TF-enhancer connections | Quality control 2: Diagnostic plots for TF-enhancer connections | Run the AR classification and QC (optional) | Save GRaNIE object to disk (optional) | Add enhancer-gene connections | Quality control 3: Diagnostic plots for enhancer-gene connections | Combine TF-enhancer and enhancer-gene connections and filter | Add TF-gene correlations (optional) | Retrieve filtered connections | Generate a connection summary for filtered connections | Construct the eGRN graph | Visualize the eGRN | Network and enrichment analyses for filtered connections | General network statistics | General network enrichment | Community network statistics and enrichment | TF enrichment analyses | Wrapping up | How to continue? | Session Info

Last update: 2023-10-26
Started: 2022-04-25

Readme and manuals

Help Manual

Help pageTopics
Quantify and interpret multiple sources of biological and technical variation for features (TFs, peaks, and genes) in a 'GRN' objectadd_featureVariation
Add TF-gene correlations to a 'GRN' object.add_TF_gene_correlation
Add peak-gene connections to a 'GRN' objectaddConnections_peak_gene
Add TF-peak connections to a 'GRN' objectaddConnections_TF_peak
Add data to a 'GRN' object.addData
Add TF activity data to GRN object using a simplified procedure for estimating it. EXPERIMENTAL.addData_TFActivity
Add SNP data to a 'GRN' object and associate SNPs to peaks.addSNPData
Add TFBS to a 'GRN' object.addTFBS
Run the activator-repressor classification for the TFs for a 'GRN' objectAR_classification_wrapper
Builds a graph out of a set of connectionsbuild_eGRN_graph
Run an enrichment analysis for the genes in each community in the filtered 'GRN' objectcalculateCommunitiesEnrichment
Generate graph communities and their summarizing statisticscalculateCommunitiesStats
Run an enrichment analysis for the genes in the whole network in the filtered 'GRN' objectcalculateGeneralEnrichment
Run an enrichment analysis for the set of genes connected to a particular TF or sets of TFs in the filtered 'GRN' objectcalculateTFEnrichment
Change the output directory of a GRN objectchangeOutputDirectory
Optional convenience function to delete intermediate data from the function 'AR_classification_wrapper' and summary statistics that may occupy a lot of spacedeleteIntermediateData
Filter connections for subsequent visualization with `visualizeGRN()` from the filtered eGRNfilterConnectionsForPlotting
Filter RNA-seq and/or peak data from a 'GRN' objectfilterData
Filter TF-peaks and peak-gene connections and combine them to TF-peak-gene connections to construct an eGRN.filterGRNAndConnectGenes
Generate a summary for the number of connections for different filtering criteria for a 'GRN' object.generateStatsSummary
Get counts for the various data defined in a 'GRN' objectgetCounts
Extract connections or links from a 'GRN' object as a data frame.getGRNConnections
Summarize a 'GRN' object to a named list for comparison with other 'GRN' objects.getGRNSummary
Retrieve parameters for previously used function calls and general parameters for a 'GRN' object.getParameters
Retrieve the top nodes (TFs or genes) with respect to either degree or Eigenvector centrality in the filtered 'GRN' object.getTopNodes
*GRaNIE* (*G*ene *R*egul*a*tory *N*etwork *I*nference including *E*nhancers): Reconstruction and evaluation of data-driven, cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNAseq data (general package information)GRaNIE-package GRaNIE
Create, represent, investigate, quantify and visualize enhancer-mediated gene regulatory networks (*eGRNs*)GRN-class
Import externally derived TF Activity data. EXPERIMENTAL.importTFData
Create and initialize an empty 'GRN' object.initializeGRN
Load example GRN datasetloadExampleObject
Get the number of genes for a 'GRN' object.genes nGenes
Get the number of peaks for a 'GRN' object.nPeaks peaks
Get the number of TFs for a 'GRN' object.nTFs TFs
Overlap peaks and TFBS for a 'GRN' objectoverlapPeaksAndTFBS
Perform all network-related statistical and descriptive analyses, including community and enrichment analyses. See the functions it executes in the @seealso section below.performAllNetworkAnalyses
Plot various network connectivity summaries for a 'GRN' objectplot_stats_connectionSummary
Plot community-based enrichment results for a filtered 'GRN' objectplotCommunitiesEnrichment
Plot general structure & connectivity statistics for each community in a filtered 'GRN'plotCommunitiesStats
Plot scatter plots of the underlying count data for either TF-peak, peak-gene or TF-gene pairs for a 'GRN' objectplotCorrelations
Plot diagnostic plots for peak-gene connections for a 'GRN' objectplotDiagnosticPlots_peakGene
Plot diagnostic plots for TF-peak connections for a 'GRN' objectplotDiagnosticPlots_TFPeaks
Plot GC-specific diagnostic plots for TF-peak connections for a 'GRN' objectplotDiagnosticPlots_TFPeaks_GC
Plot the general enrichement resultsplotGeneralEnrichment
Plot general structure and connectivity statistics for a filtered 'GRN' objectplotGeneralGraphStats
Produce a PCA plot of the data from a 'GRN' objectplotPCA_all
Plot TF-based GO enrichment resultsplotTFEnrichment
Visualize a filtered eGRN in a flexible manner.visualizeGRN