Package: Mergeomics 1.41.0

Zeyneb Kurt

Mergeomics: Integrative network analysis of omics data

The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).

Authors:Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang

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manual.pdf |manual.html✨
card.svg |card.png
Mergeomics/json (API)
NEWS

# Install 'Mergeomics' in R:
install.packages('Mergeomics', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • job.kda - Key Driver Analyzing results

On BioConductor:Mergeomics-1.41.0(bioc 3.24)Mergeomics-1.40.0(bioc 3.23)

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

software

4.48 score 15 scripts 428 downloads 13 mentions 79 exports 0 dependencies

Last updated from:9113a2ad4b. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

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bioc-checksERROR169
linux-devel-x86_64NOTE413
source / vignettesOK418
linux-release-x86_64NOTE412
macos-release-arm64NOTE259
macos-oldrel-arm64NOTE239
windows-develNOTE510
windows-releaseNOTE481
windows-oldrelNOTE455
wasm-releaseOK86

Exports:kda.analyzekda.analyze.execkda.analyze.simulatekda.analyze.testkda.configurekda.finishkda.finish.estimatekda.finish.savekda.finish.summarizekda.finish.trimkda.preparekda.prepare.overlapkda.prepare.screenkda.startkda.start.edgeskda.start.identifykda.start.moduleskda2cytoscapekda2cytoscape.colorizekda2cytoscape.colormapkda2cytoscape.driverskda2cytoscape.edgeskda2cytoscape.execkda2cytoscape.identifykda2himmelikda2himmeli.colorizekda2himmeli.colormapkda2himmeli.driverskda2himmeli.edgeskda2himmeli.execkda2himmeli.identifyMSEA.KDA.onestepssea.analyzessea.analyze.observessea.analyze.randgenesssea.analyze.randlocissea.analyze.simulatessea.analyze.statisticssea.controlssea.finishssea.finish.detailsssea.finish.fdrssea.finish.genesssea.metassea.preparessea.prepare.countsssea.prepare.structuressea.startssea.start.configuressea.start.identifyssea.start.relabelssea2kdassea2kda.analyzessea2kda.importtool.aggregatetool.clustertool.cluster.statictool.coalescetool.coalesce.exectool.coalesce.findtool.coalesce.mergetool.fdrtool.fdr.bhtool.fdr.empiricaltool.graphtool.graph.degreetool.graph.listtool.metaptool.normalizetool.normalize.qualitytool.overlaptool.readtool.savetool.subgraphtool.subgraph.findtool.subgraph.searchtool.subgraph.statstool.translatetool.unify

Dependencies:

Mergeomics

Rendered fromMergeomics.Rnwusingutils::Sweaveon May 30 2026.

Last update: 2018-08-30
Started: 2016-02-12

Readme and manuals

Help Manual

Help pageTopics
Integrative network analysis of omics dataMergeomics-package Mergeomics
Key Driver Analyzing resultsjob.kda
Weighted key driver analysis (wKDA) main functionkda.analyze
Auxiliary function for weight key driver analysis (wKDA)kda.analyze.exec
Weighted key driver analysis (wKDA) simulationkda.analyze.simulate
Calculate enrichment score for wKDAkda.analyze.test
Set parameters for weighted key driver analysis (wKDA)kda.configure
Organize and save resultskda.finish
Estimate measures for accomplished wKDA resultskda.finish.estimate
Save full wKDA resultskda.finish.save
Summarize the wKDA resultskda.finish.summarize
Trim numbers before savekda.finish.trim
Prepare graph topology for weighted key driver analysiskda.prepare
Extract overlapping co-hubskda.prepare.overlap
Prepare hubs and hubnetskda.prepare.screen
Import data for weighted key driver analysiskda.start
Import nodes and edges of graph topologykda.start.edges
Convert identities to indices for wKDAkda.start.identify
Import module descriptionskda.start.modules
Generate input files for Cytoscapekda2cytoscape
Trace module memberships of geneskda2cytoscape.colorize
Assign one color to each unique modulekda2cytoscape.colormap
Select top key drivers for each modulekda2cytoscape.drivers
Find edges of a given node with a specified depthkda2cytoscape.edges
Evaluate each module separately for visualizationkda2cytoscape.exec
Match identities with respect to given variable namekda2cytoscape.identify
Generate input files for Himmelikda2himmeli
Trace module memberships of geneskda2himmeli.colorize
Assign one color to each unique modulekda2himmeli.colormap
Select top key drivers for each modulekda2himmeli.drivers
Find edges of a given node with a specified depthkda2himmeli.edges
Evaluate each module separately for visualizationkda2himmeli.exec
Match identities with respect to given variable namekda2himmeli.identify
Run MSEA and/or KDA in one stepMSEA.KDA.onestep
Marker set enrichment analysis (MSEA)ssea.analyze
Collect enrichment score statistics for MSEAssea.analyze.observe
Estimate enrichment from randomized genesssea.analyze.randgenes
Estimate enrichment from randomized markerssea.analyze.randloci
Simulate scores for MSEAssea.analyze.simulate
MSEA statistics for enrichment scoressea.analyze.statistic
Add internal positive control modules for MSEAssea.control
Organize and save MSEA resultsssea.finish
Organize and save module, gene, top locus, Ps of MSEA resultsssea.finish.details
Organize and save FDR results of the MSEAssea.finish.fdr
Organize and save gene-realted MSEA resultsssea.finish.genes
Merge multiple MSEA results into meta MSEAssea.meta
Prepare an indexed database for MSEAssea.prepare
Calculate hit counts up to a given quantilessea.prepare.counts
Construct hierarchical representation of componentsssea.prepare.structure
Create a job for MSEAssea.start
Check parameters before MSEAssea.start.configure
Convert identities to indices for MSEAssea.start.identify
Update gene symbols after merging overlapped markersssea.start.relabel
Generate inputs for wKDAssea2kda
Apply second MSEA after merging the modulesssea2kda.analyze
Import genes and top markers from original filesssea2kda.import
Aggregate the entriestool.aggregate
Hierarchical clustering of nodestool.cluster
Static hierarchical clusteringtool.cluster.static
Calculate overlaps between groups (main function)tool.coalesce
Find, merge, and trim overlapping clusterstool.coalesce.exec
Find overlapping clusterstool.coalesce.find
Merge overlapping clusterstool.coalesce.merge
Estimate False Discovery Rates (FDR)tool.fdr
Benjamini and Hochberg False Discovery Ratetool.fdr.bh
Estimate Empirical False Discovery Ratestool.fdr.empirical
Convert an edge list to a graph representationtool.graph
Find degrees of the nodestool.graph.degree
Return edge list for each nodetool.graph.list
Estimate meta P-valuestool.metap
Estimate statistical scores based on Gauss distributiontool.normalize
Check normalization qualitytool.normalize.quality
Calculate overlaps between groups of specified itemstool.overlap
Read a data frame from a filetool.read
Save a data frame in tab-delimited filetool.save
Determine network neighbors for a set of nodestool.subgraph
Find edges to adjacent nodestool.subgraph.find
Search neighborhoods for given nodestool.subgraph.search
Calculate node degrees and strengthstool.subgraph.stats
Translate gene symbolstool.translate
Convert a distribution to uniform rankstool.unify