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Mergeomics (version 1.0.0)

Integrative network analysis of omics data

Description

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).

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Version

Version

1.0.0

License

GPL (>= 2)

Maintainer

Zeyneb Kurt

Last Published

February 15th, 2017

Functions in Mergeomics (1.0.0)

Weighted key driver analysis (wKDA) main function

kda.finish.estimate

Estimate measures for accomplished wKDA results

Organize and save results

kda.prepare.overlap

Extract overlapping co-hubs

kda.prepare.screen

Prepare hubs and hubnets

kda2cytoscape.drivers

Select top key drivers for each module

kda2cytoscape.edges

Find edges of a given node with a specified depth

kda2himmeli.identify

Match identities with respect to given variable name

Generate input files for Himmeli

ssea.finish.fdr

Organize and save FDR results of the MSEA

kda2cytoscape.colorize

Trace module memberships of genes

kda.analyze.simulate

Weighted key driver analysis (wKDA) simulation

kda2cytoscape.colormap

Assign one color to each unique module

kda2himmeli.edges

Find edges of a given node with a specified depth

kda2himmeli.exec

Evaluate each module separately for visualization

ssea.prepare.structure

Construct hierarchical representation of components

ssea.start.configure

Check parameters before MSEA

ssea.start.relabel

Update gene symbols after merging overlapped markers

Convert an edge list to a graph representation

tool.graph.degree

Find degrees of the nodes

ssea2kda.analyze

Apply second MSEA after merging the modules

ssea.finish.genes

Organize and save gene-realted MSEA results

tool.cluster.static

Static hierarchical clustering

tool.coalesce.exec

Find, merge, and trim overlapping clusters

tool.graph.list

Return edge list for each node

Estimate meta P-values

Save a data frame in tab-delimited file

tool.subgraph.find

Find edges to adjacent nodes

kda.finish.trim

Trim numbers before save

Prepare graph topology for weighted key driver analysis

Generate input files for Cytoscape

kda2himmeli.colorize

Trace module memberships of genes

ssea.analyze.randgenes

Estimate enrichment from randomized genes

Organize and save MSEA results

ssea.analyze.randloci

Estimate enrichment from randomized marker

Merge multiple MSEA results into meta MSEA

ssea2kda.import

Import genes and top markers from original files

tool.coalesce.merge

Merge overlapping clusters

Generate inputs for wKDA

Benjamini and Hochberg False Discovery Rate

tool.subgraph.stats

Calculate node degrees and strengths

Translate gene symbols

kda.analyze.test

Calculate enrichment score for wKDA

Set parameters for weighted key driver analysis (wKDA)

kda.start.edges

Import nodes and edges of graph topology

kda.start.identify

Convert identities to indices for wKDA

kda2cytoscape.exec

Evaluate each module separately for visualization

kda2cytoscape.identify

Match identities with respect to given variable name

Marker set enrichment analysis (MSEA)

ssea.analyze.observe

Collect enrichment score statistics for MSEA

tool.normalize.quality

Check normalization quality

Estimate statistical scores based on Gauss distribution

Convert a distribution to uniform ranks

kda.finish.save

Save full wKDA results

kda.finish.summarize

Summarize the wKDA results

kda2himmeli.drivers

Select top key drivers for each module

kda2himmeli.colormap

Assign one color to each unique module

ssea.analyze.simulate

Simulate scores for MSEA

ssea.analyze.statistic

MSEA statistics for enrichment score

ssea.prepare.counts

Calculate hit counts up to a given quantile

tool.coalesce.find

Find overlapping clusters

Prepare an indexed database for MSEA

Calculate overlaps between groups (main function)

Estimate False Discovery Rates (FDR)

tool.fdr.empirical

Estimate Empirical False Discovery Rates

tool.subgraph.search

Search neighborhoods for given nodes

Determine network neighbors for a set of nodes

kda.analyze.exec

Auxiliary function for weight key driver analysis (wKDA)

Key Driver Analyzing results

Import data for weighted key driver analysis

Add internal positive control modules for MSEA

kda.start.modules

Import module descriptions

ssea.finish.details

Organize and save module, gene, top locus, Ps of MSEA results

ssea.start.identify

Convert identities to indices for MSEA

Create a job for MSEA

Aggregate the entries

Calculate overlaps between groups of specified items

Hierarchical clustering of nodes

Read a data frame from a file