R: Integrative network analysis of omics data

# Integrative network analysis of omics data

## Documentation for package ‘Mergeomics’ version 1.40.0

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

## Help Pages

Mergeomics-package | Integrative network analysis of omics data
job.kda | Key Driver Analyzing results
kda.analyze | Weighted key driver analysis (wKDA) main function
kda.analyze.exec | Auxiliary function for weight key driver analysis (wKDA)
kda.analyze.simulate | Weighted key driver analysis (wKDA) simulation
kda.analyze.test | Calculate enrichment score for wKDA
kda.configure | Set parameters for weighted key driver analysis (wKDA)
kda.finish | Organize and save results
kda.finish.estimate | Estimate measures for accomplished wKDA results
kda.finish.save | Save full wKDA results
kda.finish.summarize | Summarize the wKDA results
kda.finish.trim | Trim numbers before save
kda.prepare | Prepare graph topology for weighted key driver analysis
kda.prepare.overlap | Extract overlapping co-hubs
kda.prepare.screen | Prepare hubs and hubnets
kda.start | Import data for weighted key driver analysis
kda.start.edges | Import nodes and edges of graph topology
kda.start.identify | Convert identities to indices for wKDA
kda.start.modules | Import module descriptions
kda2cytoscape | Generate input files for Cytoscape
kda2cytoscape.colorize | Trace module memberships of genes
kda2cytoscape.colormap | Assign one color to each unique module
kda2cytoscape.drivers | Select top key drivers for each module
kda2cytoscape.edges | Find edges of a given node with a specified depth
kda2cytoscape.exec | Evaluate each module separately for visualization
kda2cytoscape.identify | Match identities with respect to given variable name
kda2himmeli | Generate input files for Himmeli
kda2himmeli.colorize | Trace module memberships of genes
kda2himmeli.colormap | Assign one color to each unique module
kda2himmeli.drivers | Select top key drivers for each module
kda2himmeli.edges | Find edges of a given node with a specified depth
kda2himmeli.exec | Evaluate each module separately for visualization
kda2himmeli.identify | Match identities with respect to given variable name
Mergeomics | Integrative network analysis of omics data
MSEA.KDA.onestep | Run MSEA and/or KDA in one step
ssea.analyze | Marker set enrichment analysis (MSEA)
ssea.analyze.observe | Collect enrichment score statistics for MSEA
ssea.analyze.randgenes | Estimate enrichment from randomized genes
ssea.analyze.randloci | Estimate enrichment from randomized marker
ssea.analyze.simulate | Simulate scores for MSEA
ssea.analyze.statistic | MSEA statistics for enrichment score
ssea.control | Add internal positive control modules for MSEA
ssea.finish | Organize and save MSEA results
ssea.finish.details | Organize and save module, gene, top locus, Ps of MSEA results
ssea.finish.fdr | Organize and save FDR results of the MSEA
ssea.finish.genes | Organize and save gene-realted MSEA results
ssea.meta | Merge multiple MSEA results into meta MSEA
ssea.prepare | Prepare an indexed database for MSEA
ssea.prepare.counts | Calculate hit counts up to a given quantile
ssea.prepare.structure | Construct hierarchical representation of components
ssea.start | Create a job for MSEA
ssea.start.configure | Check parameters before MSEA
ssea.start.identify | Convert identities to indices for MSEA
ssea.start.relabel | Update gene symbols after merging overlapped markers
ssea2kda | Generate inputs for wKDA
ssea2kda.analyze | Apply second MSEA after merging the modules
ssea2kda.import | Import genes and top markers from original files
tool.aggregate | Aggregate the entries
tool.cluster | Hierarchical clustering of nodes
tool.cluster.static | Static hierarchical clustering
tool.coalesce | Calculate overlaps between groups (main function)
tool.coalesce.exec | Find, merge, and trim overlapping clusters
tool.coalesce.find | Find overlapping clusters
tool.coalesce.merge | Merge overlapping clusters
tool.fdr | Estimate False Discovery Rates (FDR)
tool.fdr.bh | Benjamini and Hochberg False Discovery Rate
tool.fdr.empirical | Estimate Empirical False Discovery Rates
tool.graph | Convert an edge list to a graph representation
tool.graph.degree | Find degrees of the nodes
tool.graph.list | Return edge list for each node
tool.metap | Estimate meta P-values
tool.normalize | Estimate statistical scores based on Gauss distribution
tool.normalize.quality | Check normalization quality
tool.overlap | Calculate overlaps between groups of specified items
tool.read | Read a data frame from a file
tool.save | Save a data frame in tab-delimited file
tool.subgraph | Determine network neighbors for a set of nodes
tool.subgraph.find | Find edges to adjacent nodes
tool.subgraph.search | Search neighborhoods for given nodes
tool.subgraph.stats | Calculate node degrees and strengths
tool.translate | Translate gene symbols
tool.unify | Convert a distribution to uniform ranks