Package 'MEGENA' reference manual

Title:	Multiscale Clustering of Geometrical Network
Description:	Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.
Authors:	Won-Min Song, Bin Zhang
Maintainer:	Won-Min Song <[email protected]>
License:	GPL (>= 3)
Version:	1.6
Built:	2025-03-04 04:49:22 UTC
Source:	https://github.com/songw01/megena

co-expression network analysis

Description

construction of gene-gene interaction network and dissection into multi-scale functional modules, and network key drivers.

Details

Package:	MEGENA
Type:	Package
Version:	1.3
Date:	2015-12-18
License:	GPL (>= 2)

Multiscale Embedded Gene Co-expression Network Analysis (MEGENA)

Author(s)

Won-Min Song

Maintainer: Won-Min Song <[email protected]>

References

Song W-M, Zhang B (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. doi: 10.1371/journal.pcbi.1004574

PFN calculation

Description

main function to calculate PFN a ranked list of edge pairs

Usage

calculate.PFN(edgelist,max.skipEdges = NULL,maxENum = NULL,doPar = FALSE,
	num.cores = NULL, keep.track = TRUE, save.vertex.names=TRUE,
	vertex.names.file='vertex.names.RData')
resume.calculate.PFN(edgelist, net, max.skipEdges = NULL, doPar = FALSE,
	num.cores = NULL, keep.track = TRUE, vertex.names.file='vertex.names.RData')
calculate.PFN(edgelist,max.skipEdges = NULL,maxENum = NULL,doPar = FALSE,
	num.cores = NULL, keep.track = TRUE, save.vertex.names=TRUE,
	vertex.names.file='vertex.names.RData')
resume.calculate.PFN(edgelist, net, max.skipEdges = NULL, doPar = FALSE,
	num.cores = NULL, keep.track = TRUE, vertex.names.file='vertex.names.RData')

Arguments

`edgelist`	three column edgelist: first two columns are topological edges, and the third column is the weight. Must be a data.frame object.
`max.skipEdges`	Maximum number of edges to be searched by planarity test without any inclusion to PFN. If set NULL, it will be automatically set to number of cores x 1000. It acts as a threhold to quicken PFN construction termination during PCP.
`maxENum`	maximum number of edges to include in final PFN. Default value is NULL, which invokes maximal number of edges allowed in planar network.
`doPar`	TRUE/FALSE logical variable to choose parallelization.
`num.cores`	number of cores to use in parallelization.
`keep.track`	If TRUE, pfg_el.RData will be created in working folder. This file can be used later for restart in case PFN construction did not finish successfully. Default is TRUE.
`net`	a data.frame with intermediate network edges from a PFN job. The edges should have been converted to numeric index of vector `vertex.names` given by `vertex.names.file`.
`save.vertex.names`	whether to save vertex names that matches the gene indices in intermediate output.
`vertex.names.file`	file to save vertex names if `save.vertex.names` is `TRUE`.

Details

If doPar = TRUE, then num.cores are registered for PCP.

Value

output is three column edgelist data.frame, third column being the weight.

Author(s)

Won-Min Song

Examples

# test simplest case of planar network (a 3-clique).
a <- c(1,1,2);b <- c(2,3,3);w <- runif(3,0,1);
el <- cbind(a,b,w);el <- as.data.frame(el[order(el[,3],decreasing = TRUE),])
calculate.PFN(edgelist = el,max.skipEdges = Inf,doPar = FALSE,num.cores = NULL)
# test simplest case of planar network (a 3-clique).
a <- c(1,1,2);b <- c(2,3,3);w <- runif(3,0,1);
el <- cbind(a,b,w);el <- as.data.frame(el[order(el[,3],decreasing = TRUE),])
calculate.PFN(edgelist = el,max.skipEdges = Inf,doPar = FALSE,num.cores = NULL)

correlation calculation

Description

correlation analysis with FDR calculation

Usage

calculate.rho.signed(datExpr,n.perm = 10,FDR.cutoff = 0.05,estimator = "pearson",
                               use.obs = "na.or.complete",
                               direction = "absolute",
                               rho.thresh = NULL,sort.el = TRUE)
calculate.rho.signed(datExpr,n.perm = 10,FDR.cutoff = 0.05,estimator = "pearson",
                               use.obs = "na.or.complete",
                               direction = "absolute",
                               rho.thresh = NULL,sort.el = TRUE)

Arguments

`datExpr`	gene expression data matrix
`n.perm`	Number of permutations to perform. If `doPerm = NULL`, calculates BH FDR p-values instead of permutation based FDR.
`FDR.cutoff`	FDR threshold to output final results of significant correlations.
`estimator`	Type of correlation coefficient to calculate, and gets passed to 'method' in 'cor()' function. Either "pearson" or "spearman".
`direction`	If direction = "absolute", absolute valued correlations are considered. If direction = "positive" or "negative", signed correlation is considered, and significance is calculated for positive/negative values.
`rho.thresh`	vector of correlation cutoff threshold to calculate significance. If NULL, threshold values will be generated by increment of 0.01 between -1 and 1 (if direction = "positive" or "negative"), or 0 and 1 (if direction = "absolute").
`sort.el`	logical value to determine whether correlation list should be sorted
`use.obs`	an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".

Details

direction = "absolute" can serve as two-tailed significance test, where as direction = "positive"/"negative" is one-tailed test.

Value

output is a list. output$signif.ijw is three column edgelist data.frame, third column being the weight. output$FDR is the permutation FDR table across the threshold values in rho.thresh.

Author(s)

Won-Min Song

Examples


data(Sample_Expression)
rho.out = calculate.rho.signed(datExpr,n.perm = 10,FDR.cutoff = 0.05,estimator = "pearson",
                               use.obs = "na.or.complete",
                               direction = "absolute",
                               rho.thresh = NULL,sort.el = TRUE)

data(Sample_Expression)
rho.out = calculate.rho.signed(datExpr,n.perm = 10,FDR.cutoff = 0.05,estimator = "pearson",
                               use.obs = "na.or.complete",
                               direction = "absolute",
                               rho.thresh = NULL,sort.el = TRUE)

Parallelized PFN computation

Description

PFN construction by parallelized edge screening.

Usage

compute.PFN.par(sortedEdge,Ng,maxENum,Njob,Ncore,max.skipEdges = NULL,
keep.track = TRUE,initial.links = NULL)
compute.PFN.par(sortedEdge,Ng,maxENum,Njob,Ncore,max.skipEdges = NULL,
keep.track = TRUE,initial.links = NULL)

Arguments

`sortedEdge`	3-column matrix for the input edgelist (e.g. - correlation pair list). Must be sorted by third column, which is usually weight vector.
`Ng`	integer. number of genes included in sortedEdge.
`maxENum`	Maximum number of edges to include in final PFN. The theoretical maximal number enforced by Euler's formula is 3(Ng-2).
`max.skipEdges`	Maximum number of edges to be counted before any valid edge to be included in PFN. This works as a termination condition to avoid exhaustive planarity testing over all edges provided in sortedEdge.
`Njob`	Number of edges to be passed to each core for parallelized edge screening.
`Ncore`	Number of cores to utilize.
`keep.track`	TRUE/FALSE logical. Indicate if the record of PFN construction is saved in temporary file "pfg_el.RData". Default is TRUE.
`initial.links`	If provided, PFN construction will restart by regarding these initial.links as already-built PFN.

Details

This is parallelized implementation of PFN construction, where it is possible to re-capture PFN construction by providing already computed edgelist into initial.links. Although provivded, this function itself may require careful caution and users are encouraged to use more user-friendly "calculate.PFN()" instead.

Value

A 3-column matrices, where first two columns are integer indices for vertices, and third is the weight vector.

Author(s)

Won-Min Song

Toy example data

Description

A portion of TCGA breast cancer data to test run MEGENA.

Usage

Sample_Expression.RData
Sample_Expression.RData

Format

Contains a matrix object, "datExpr". Use data(Sample_Expression) to load.

Details

a gene expression matrix.

References

1. Song, W.M. and B. Zhang, Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol, 2015. 11(11): p. e1004574.

MEGENA clustering + MHA

Description

multiscale clustering analysis (MCA) and multiscale hub analysis (MHA) pipeline

Usage

do.MEGENA(g,
do.hubAnalysis = TRUE,
mod.pval = 0.05,hub.pval = 0.05,remove.unsig = TRUE,
min.size = 10,max.size = 2500,
doPar = FALSE,num.cores = 4,n.perm = 100,singleton.size = 3,
save.output = FALSE)
do.MEGENA(g,
do.hubAnalysis = TRUE,
mod.pval = 0.05,hub.pval = 0.05,remove.unsig = TRUE,
min.size = 10,max.size = 2500,
doPar = FALSE,num.cores = 4,n.perm = 100,singleton.size = 3,
save.output = FALSE)

Arguments

`g`	igraph object of PFN.
`do.hubAnalysis`	TRUE/FALSE indicating to perform multiscale hub analysis (MHA) in downstream. Default is TRUE.
`mod.pval`	cluster significance p-value threshold w.r.t random planar networks
`hub.pval`	hub significance p-value threshold w.r.t random planar networks
`remove.unsig`	TRUE/FALSE indicating to remove insignificant clusters in MHA.
`min.size`	minimum cluster size
`max.size`	maximum cluster size
`doPar`	TRUE/FALSE indicating parallelization usage
`num.cores`	number of cores to use in parallelization.
`n.perm`	number of permutations to calculate hub significance p-values/cluster significance p-values.
`singleton.size`	Minimum module size to regard as non-singleton module. Default is 3.
`save.output`	TRUE/FALSE to save outputs from each step of analysis

Details

Performs MCA and MHA by taking PFN as input. Returns a list object containing clustering outputs, hub analysis outputs, and node summary table.

Value

A series of output files are written in wkdir. Major outputs are,

`module.output`	outputs from MCA
`hub.output`	outputs from MHA
`node.summary`	node table summarizing clustering results.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)

## End(Not run)
## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)

## End(Not run)

Draw sunburst plot showing MEGENA module hierarchy.

Description

Sunburst plot and colored heatmaps

Usage

  draw_sunburst_wt_fill(module.df,
                        parent.col = "module.parent",id.col = "id",
                        min.angle = 5,
                        feat.col,
                        fill.type = "continuous",log.transform = TRUE,
                        fill.scale = NULL,
						border.col = "black",
						border.width = 0.25,
                        theme.adjust = NULL
                        )
draw_sunburst_wt_fill(module.df,
                        parent.col = "module.parent",id.col = "id",
                        min.angle = 5,
                        feat.col,
                        fill.type = "continuous",log.transform = TRUE,
                        fill.scale = NULL,
						border.col = "black",
						border.width = 0.25,
                        theme.adjust = NULL
                        )

Arguments

`module.df`	A data.frame table summarizing module information. Must contain module parent and child relation for hierarchy visualization.
`parent.col`	Character object, name for the parent module column in module.df.
`id.col`	Character object for the module id column in module df.
`min.angle`	Minimum angle that rectangles in the sunburst are labeled with respective module id.
`feat.col`	Chracter object, for the feature column in module.df to color the heatmaps.
`fill.type`	continuous/discrete, is the variable numeric (continuous) or factor (discrete)?
`log.transform`	TRUE/FALSE. do log10 transform for p-values?
`fill.scale`	A ggplot object to specify heatmap coloring scheme. Permissible functions are: scale_fill_gradient,scale_fill_gradient2,scale_fill_gradientn,scale_fill_manual.
`border.col`	sunburst border color. Default is black.
`border.width`	sunburst border width. Default is 0.25.
`theme.adjust`	A ggplot object to specify theme for plotting.

Details

makes use of ggraph scheme to manipulate and draw sunburst plot in ggplot2 framework. fill.scale and theme.adjust provide flexibility to designate heatmap coloring schemes and figure aesthetics.

Value

ggplot object for the figure

Author(s)

Won-Min Song

Examples

  ## Not run: 
    rm(list = ls())
    data(Sample_Expression)
    ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
    el <- calculate.PFN(ijw[,1:3])
    g <- graph.data.frame(el,directed = FALSE)
    MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
    output.summary <- MEGENA.ModuleSummary(MEGENA.output,
    mod.pvalue = 0.05,hub.pvalue = 0.05,
    min.size = 10,max.size = 5000,
    annot.table = NULL,id.col = NULL,symbol.col = NULL,
    output.sig = TRUE)
    
    # no coloring 
    sbobj = draw_sunburst_wt_fill(module.df = output.summary$module.table,
    feat.col = NULL,id.col = "module.id",parent.col = "module.parent")
    sbobj
    
    # get some coloring (with log transform option)
    mdf= output.summary$module.table
    mdf$heat.pvalue = runif(nrow(mdf),0,0.1)
    
    sbobj = draw_sunburst_wt_fill(module.df = mdf,feat.col = "heat.pvalue",log.transform = TRUE,
    fill.type = "continuous",
    fill.scale = scale_fill_gradient2(low = "white",mid = "white",high = "red",
    midpoint = -log10(0.05),na.value = "white"), 
    id.col = "module.id",parent.col = "module.parent")
    sbobj
    
    # get discrete coloring done
    mdf$category = factor(sample(x = c("A","B"),size = nrow(mdf),replace = TRUE))
    sbobj = draw_sunburst_wt_fill(module.df = mdf,feat.col = "category",
    fill.type = "discrete",
    fill.scale = scale_fill_manual(values = c("A" = "red","B" = "blue")), 
    id.col = "module.id",parent.col = "module.parent")
    sbobj
  
## End(Not run)
## Not run: 
    rm(list = ls())
    data(Sample_Expression)
    ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
    el <- calculate.PFN(ijw[,1:3])
    g <- graph.data.frame(el,directed = FALSE)
    MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
    output.summary <- MEGENA.ModuleSummary(MEGENA.output,
    mod.pvalue = 0.05,hub.pvalue = 0.05,
    min.size = 10,max.size = 5000,
    annot.table = NULL,id.col = NULL,symbol.col = NULL,
    output.sig = TRUE)
    
    # no coloring 
    sbobj = draw_sunburst_wt_fill(module.df = output.summary$module.table,
    feat.col = NULL,id.col = "module.id",parent.col = "module.parent")
    sbobj
    
    # get some coloring (with log transform option)
    mdf= output.summary$module.table
    mdf$heat.pvalue = runif(nrow(mdf),0,0.1)
    
    sbobj = draw_sunburst_wt_fill(module.df = mdf,feat.col = "heat.pvalue",log.transform = TRUE,
    fill.type = "continuous",
    fill.scale = scale_fill_gradient2(low = "white",mid = "white",high = "red",
    midpoint = -log10(0.05),na.value = "white"), 
    id.col = "module.id",parent.col = "module.parent")
    sbobj
    
    # get discrete coloring done
    mdf$category = factor(sample(x = c("A","B"),size = nrow(mdf),replace = TRUE))
    sbobj = draw_sunburst_wt_fill(module.df = mdf,feat.col = "category",
    fill.type = "discrete",
    fill.scale = scale_fill_manual(values = c("A" = "red","B" = "blue")), 
    id.col = "module.id",parent.col = "module.parent")
    sbobj
  
## End(Not run)

Curated MSigDB signatures for testing funtion/pathway enrichments of MEGENA modules

Description

MSigDB v6.2 sets (BIOCARTA, GO, KEGG, REACTOME, HALLMARK and oncogenic signatures)

Usage

  data(FrequenctSets.v6.2)
data(FrequenctSets.v6.2)

Format

Contains a list object, "msigdb.sets". Use data(Sample_Expression) to load.

Details

a list of curated gene signatures from MSigDB

Module Hierarchy Visualization

Description

Utilizes various layout algorithms from ggraph to visualize module hierarchy with desired root.

Usage

  get_module_hierarchy_graph(htbl,max.depth = 5,anchor.mid = NULL,h.scale = NULL,is.circular = TRUE,layout = "dendrogram")
get_module_hierarchy_graph(htbl,max.depth = 5,anchor.mid = NULL,h.scale = NULL,is.circular = TRUE,layout = "dendrogram")

Arguments

`htbl`	Two column data.frame table capturing module hierarchy. First column is the parent module, and second column is an immediate child module.
`max.depth`	Maximum number of hierarchy depth to propagate. Default value 5 is often sufficient to capture the entire MEGENA hierarchy.
`anchor.mid`	Default is NULL. If specified, it is taken as the root module, and its module hierarchy is subsetted.
`h.scale`	When layout = "dendrogram" is used, it scales the heights of the dendrogram branching points to make the plot less busy.
`is.circular`	Default is TRUE. If TRUE, circular dendrogram is drawn.
`layout`	Options are "dendrogram" and "treemap". See ?ggraph for layout option descriptions.
`add_names`	If TRUE, it adds modules names at respective branching points.

Details

Returns a list containing the results.

Value

A list object with the components:

`pobj`	ggplot object for the hierarchy graph.
`graph.obj`	igraph object for the hierarchy graph.
`anchor.mid`	The root module id.

Author(s)

Won-Min Song

Examples

  ## Not run: 
    rm(list = ls())
    data(Sample_Expression)
    ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
    el <- calculate.PFN(ijw[,1:3])
    g <- graph.data.frame(el,directed = FALSE)
    MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
    output.summary <- MEGENA.ModuleSummary(MEGENA.output,
                                           mod.pvalue = 0.05,hub.pvalue = 0.05,
                                           min.size = 10,max.size = 5000,
                                           annot.table = NULL,id.col = NULL,symbol.col = NULL,
                                           output.sig = TRUE)
    tbl = output.summary$module.table
    htbl = tbl[,c("module.parent","module.id")]
    hplot = get_module_hierarchy_graph(htbl,max.depth = 5,anchor.mid = NULL,h.scale = NULL,is.circular = FALSE,layout = "dendrogram",add_names = TRUE)
    print(hplot$pobj)
  
## End(Not run)
## Not run: 
    rm(list = ls())
    data(Sample_Expression)
    ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
    el <- calculate.PFN(ijw[,1:3])
    g <- graph.data.frame(el,directed = FALSE)
    MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
    output.summary <- MEGENA.ModuleSummary(MEGENA.output,
                                           mod.pvalue = 0.05,hub.pvalue = 0.05,
                                           min.size = 10,max.size = 5000,
                                           annot.table = NULL,id.col = NULL,symbol.col = NULL,
                                           output.sig = TRUE)
    tbl = output.summary$module.table
    htbl = tbl[,c("module.parent","module.id")]
    hplot = get_module_hierarchy_graph(htbl,max.depth = 5,anchor.mid = NULL,h.scale = NULL,is.circular = FALSE,layout = "dendrogram",add_names = TRUE)
    print(hplot$pobj)
  
## End(Not run)

calculate module degree statistics based on random triangulation model via T1 and T2 moves.

Description

calculation of module p-values.

Usage

get.DegreeHubStatistic(subnetwork,n.perm = 100,doPar = FALSE,n.core = 4)
get.DegreeHubStatistic(subnetwork,n.perm = 100,doPar = FALSE,n.core = 4)

Arguments

`subnetwork`	a planar network as an igraph object.
`n.perm`	number of random networks generated, constraint with number of links and nodes same to "subnetwork".
`doPar`	TRUE/FALSE to parallelize.
`n.core`	number of cores/threads to use.

Details

Hub significance calculation functionality. Make sure that, if doPar = TRUE, register cores using registerDoParallel() from doParallel package.

Value

a data.frame table showing node-wise statistics.

Author(s)

Won-Min Song

Examples

## Not run: 
	rm(list = ls())
	data(Sample_Expression)
	ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
	el <- calculate.PFN(ijw[,1:3])
	g <- graph.data.frame(el,directed = FALSE)

	out <- get.DegreeHubStatistic(subnetwork = g,n.perm = 100,doPar = FALSE,n.core = 4)

## End(Not run)
## Not run: 
	rm(list = ls())
	data(Sample_Expression)
	ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
	el <- calculate.PFN(ijw[,1:3])
	g <- graph.data.frame(el,directed = FALSE)

	out <- get.DegreeHubStatistic(subnetwork = g,n.perm = 100,doPar = FALSE,n.core = 4)

## End(Not run)

summarize hub information.

Description

hubs in different scales are summarized.

Usage

get.hub.summary(MEGENA.output)
get.hub.summary(MEGENA.output)

Arguments

MEGENA.output

A list object. The output from "do.MEGENA()".

Details

returns a data.frame object

Value

A data.frame object with columns:

`node`	hub gene node names
`S1`, `...`	binary vector indicating hubs in each scale
`frequency`	number of scales that respective gene emerges as hub.
`scale.summary`	list of scales that respective gene as hub.

Author(s)

Won-Min Song

Scale-thresholding of multiscale modules.

Description

obtain a discrete, disjoint clustering results from multiscale MEGENA modules for a given alpha value.

Usage

get.union.cut(module.output,alpha.cut,output.plot = T,
plotfname = "validModules_alpha",module.pval = 0.05,remove.unsig = T)
get.union.cut(module.output,alpha.cut,output.plot = T,
plotfname = "validModules_alpha",module.pval = 0.05,remove.unsig = T)

Arguments

`module.output`	A direct output from "do.MEGENA". (i.e. MEGENA.output$module.output).
`alpha.cut`	Resolution parameter cut-off (i.e. alpha) value. alpha.cut = 1 corresponds to classical definition of "small-world" compactness.
`output.plot`	TRUE/FALSE to indicate outputting a .png file showing hierarchical structure with final outputted modules highlighted in red.
`plotfname`	.png file outputname.
`module.pval`	module significance p-value.
`remove.unsig`	TRUE/FALSE indicating to remove insignificant clusters.

Details

Returns a list object where each entry is a module.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
get.union.cut(module.output = MEGENA.output$module.output,alpha.cut = 1,
output.plot = FALSE,plotfname = NULL,module.pval = 0.05,remove.unsig = TRUE)

## End(Not run)
## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
get.union.cut(module.output = MEGENA.output$module.output,alpha.cut = 1,
output.plot = FALSE,plotfname = NULL,module.pval = 0.05,remove.unsig = TRUE)

## End(Not run)

MEGENA module summary

Description

Summarizes modules into a table.

Usage

MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 2500,
annot.table = NULL,symbol.col = NULL,id.col = NULL, 
output.sig = TRUE)
MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 2500,
annot.table = NULL,symbol.col = NULL,id.col = NULL, 
output.sig = TRUE)

Arguments

`MEGENA.output`	A list object. The output from "do.MEGENA()".
`mod.pvalue`	module compactness significance p-value, to identify modules with significant compactness.
`hub.pvalue`	node degree significance p-value to identify nodes with significantly high degree.
`min.size`	minimum module size allowed to finalize in the summary output.
`max.size`	maximum module size allowed to finalize in the summary output.
`annot.table`	Default value is NULL, indicating no mapping is provided between node names to gene symbols. If provided, the mapping between node names (id.col) and gene symbol (symbol.col) are used.
`id.col`	column index of annot.table for node names.
`symbol.col`	column index of annot.table for gene symbols.
`output.sig`	Default value is TRUE, indicating significant modules are outputted.

Details

output$module.table contains many important information including module hierarchy, as indicated by

Value

A list object with the components:

`modules`	Final set of modules obtained upon apply mod.pvalue for significance, min.size and max.size for module size thresholding.
`mapped.modules`	gene symbol mapped modules when "annot.table" is provided.
`module.table`	data.frame object for module summary table. Columns include: id, module.size, module.parent, module.hub, module.scale and module.pvalue.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

## End(Not run)
## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

## End(Not run)

conversion of module list object to a data.frame table format

Description

Summarizes module hub/hierarchy/membership into a data.frame table format.

Usage

module_convert_to_table(MEGENA.output,mod.pval = 0.05,
hub.pval = 0.05,min.size = 10,max.size)
module_convert_to_table(MEGENA.output,mod.pval = 0.05,
hub.pval = 0.05,min.size = 10,max.size)

Arguments

`MEGENA.output`	A list object. The output from "do.MEGENA()".
`mod.pval`	module compactness significance p-value, to identify modules with significant compactness.
`hub.pval`	node degree significance p-value to identify nodes with significantly high degree.
`min.size`	minimum module size allowed to finalize in the summary output.
`max.size`	maximum module size allowed to finalize in the summary output.

Details

the resulting data.frame contains the following essential columns: id, module.parent and module. If the co-expression network bears significant hubs, it will additionally have node.degree (connectivity), node.strength (sum of edge weights) and is.hub column to supplement hub information.

Value

A data.frame with the columns:

`id`	gene name
`module.parent`	parent module id
`module`	module name.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)
module.df = module_convert_to_table(MEGENA.output,mod.pval = 0.05,
hub.pval = 0.05,min.size = 10,max.size)
head(module.df)

## End(Not run)
## Not run: 
rm(list = ls())
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)
module.df = module_convert_to_table(MEGENA.output,mod.pval = 0.05,
hub.pval = 0.05,min.size = 10,max.size)
head(module.df)

## End(Not run)

output gene signatures into .gmt file format

Description

An interface function to output .gmt format gene signature file.

Usage

output.geneSet.file(geneSet,outputfname)
output.geneSet.file(geneSet,outputfname)

Arguments

`geneSet`	a list object
`outputfname`	output file name

Details

Outputs each signature into a single line of lists in outputfname.

Author(s)

Won-Min Song

Fisher Exact Test (FET) analysis for ALL possible pairs of two gene set lists.

Description

FET for all pairs.

Usage

  perform.AllPairs.FET(geneSets1,geneSets2,background,adjust.FET.pvalue = T)
perform.AllPairs.FET(geneSets1,geneSets2,background,adjust.FET.pvalue = T)

Arguments

`geneSets1`	a list object containing gene signatures as character vector in each list entry.
`geneSets2`	a list object containing gene signatures as character vector in each list entry.
`background`	a character vector containing the background gene set.
`adjust.FET.pvalue`	If set TRUE, bonferroni correction is performed and output in corrected.FET.pvalue column.

Details

Returns a data.frame entailing all comparisons.

Value

In comparison to perform.ijPairs.FET, this function is designed to perform FET on ALL comparisons.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
data(Sample_Expression)
data(FrequentSets.v6.2)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

FET.results = perform.AllPairs.FET(geneSets1 = output.summary$modules,geneSets2 = msigdb.sets[[2]],background = V(g)$name)
FET.results = FET.results[order(FET.results$FET_pvalue),]
head(FET.results)


## End(Not run)
## Not run: 
rm(list = ls())
data(Sample_Expression)
data(FrequentSets.v6.2)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

FET.results = perform.AllPairs.FET(geneSets1 = output.summary$modules,geneSets2 = msigdb.sets[[2]],background = V(g)$name)
FET.results = FET.results[order(FET.results$FET_pvalue),]
head(FET.results)


## End(Not run)

Fisher Exact Test (FET) analysis for user specific pairs of gene sets

Description

FET with given pairs

Usage

  perform.ijPairs.FET(geneSets1,geneSets2,ij,background,adjust.FET.pvalue = T)
perform.ijPairs.FET(geneSets1,geneSets2,ij,background,adjust.FET.pvalue = T)

Arguments

`geneSets1`	a list object containing gene signatures as character vector in each list entry.
`geneSets2`	a list object containing gene signatures as character vector in each list entry.
`ij`	two column integer matrix. Each row contains a pair of indices to compare between geneSet1 and geneSet2. For instance, ij[1,] = c(1,2), then FET is performed between geneSets1[[1]] and geneSets2[[2]].
`background`	a character vector containing the background gene set.
`adjust.FET.pvalue`	If set TRUE, bonferroni correction is performed and output in corrected.FET.pvalue column.

Details

Returns a data.frame entailing all comparisons.

Value

In comparison to perform.AllPairs.FET, this function is designed to perform FET on a specified set of comparisons to save time.

Author(s)

Won-Min Song

Boyer-Myvold Planarity test of a network

Description

wrapper function of _MEGENA_planaritytest. imports from Boost graph library, and test planarity of a network

Usage

planaritytest(N, rows, cols)
planaritytest(N, rows, cols)

Arguments

`N`	must be an integer. number of nodes in the network.
`rows`	first column of edgelist. a vector of integers.
`cols`	second column of edgelist. a vector of integers.

Details

cbind(rows,cols) is equivalent to the two column edge list of the network. We assume that the network is undirected.

Value

TRUE/FALSE is returned to indicate planarity. (TRUE -> network is planar).

Author(s)

Won-Min Song

Examples

# test simplest case of planar network (a 3-clique).
planaritytest(as.integer(3),c(1,1,2),c(2,3,3))
# test simplest case of planar network (a 3-clique).
planaritytest(as.integer(3),c(1,1,2),c(2,3,3))

Module plotting function.

Description

Extract subnetworks for modules and plot.

Usage

plot_module(output.summary, PFN, subset.module = NULL, node.rename=NULL,
col.names=NULL, gene.set = NULL,color.code = "logFC",show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,show.topn.hubs = 10,
node.sizeProp = 13,label.sizeProp = 13,label.scaleFactor = 10,label.alpha = 0.5,
layout = "kamada.kawai",output.plot = TRUE,out.dir = "modulePlot")
plot_module(output.summary, PFN, subset.module = NULL, node.rename=NULL,
col.names=NULL, gene.set = NULL,color.code = "logFC",show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,show.topn.hubs = 10,
node.sizeProp = 13,label.sizeProp = 13,label.scaleFactor = 10,label.alpha = 0.5,
layout = "kamada.kawai",output.plot = TRUE,out.dir = "modulePlot")

Arguments

`output.summary`	output from summary function, "MEGENA.ModuleSummary".
`PFN`	igraph object retaining PFN topology.
`subset.module`	A character vector for list of module names to plot. Default = NULL plots all modules in output.summary.
`node.rename`	NULL or a data.frame with the first column being the node ids in the network and the second column being node names for display purpose, eg, this could be used to map ensembl gene ids to gene symbols.
`col.names`	NULL or a character vector for list of colors to be used for coloring children modules.
`gene.set`	A list object containing signatures for customized coloring of nodes in resulting network plot.
`color.code`	A character vector with matched length to "gene.set", to specify colors for each signature.
`label.hubs.only`	TRUE/FALSE to show labels for significant hub genes only, or all genes. Defauly is TRUE.
`hubLabel.col`	Label color for hubs. Default is "red"
`show.legend`	TRUE/FALSE for showing node legend on the bottom of the figure.
`hubLabel.sizeProp`	A multiplicative factor to adjust hub label sizes with respect to node size values. Default is 0.5
`show.topn.hubs`	Maximal number of hubs to label on module subnetwork. Default is 10.
`node.sizeProp`	A multiplicative factor to adjust node sizes with respect to 90th percentile degree node size. Default is 13
`label.sizeProp`	A multiplicative factor to adjust node label sizes with respect to 90th percentile degree node size. Default is 13
`label.scaleFactor`	Overall scale factor to control the final size of node labels appearing in figure. Default is 10.
`label.alpha`	Transparency value ranging from 0 (transparent) to 1 (solid). Default is 0.5.
`layout`	Network layout algorithm to apply. Options are: "kamada.kawai", "fruchterman.reingold".
`output.plot`	logical value. output.plot = TRUE generates figure files under folder, "modulePlot".
`out.dir`	if output.plot = TRUE, then out.dir is created and resulting figures are exported to .png files to the folder.

Details

Subnetwork plot functionality with application of "ggrepel" package for node labeling. The most effective way to control overall node label size is through label.scaleFactor.

Value

A list object holding ggplot objects for plotted modules.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
library(MEGENA)

data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

pnet.obj <- plot_module(output = output.summary,PFN = g,subset.module = "comp1_2",
	layout = "kamada.kawai",label.hubs.only = FALSE,
	gene.set = list("hub.set" = c("CD3E","CD2")),color.code =  c("red"),
	output.plot = FALSE,out.dir = "modulePlot",col.names = c("grey","grey","grey"),
	hubLabel.col = "black",hubLabel.sizeProp = 1,show.topn.hubs = Inf,show.legend = TRUE)
	
pnet.obj

## End(Not run)
## Not run: 
rm(list = ls())
library(MEGENA)

data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

pnet.obj <- plot_module(output = output.summary,PFN = g,subset.module = "comp1_2",
	layout = "kamada.kawai",label.hubs.only = FALSE,
	gene.set = list("hub.set" = c("CD3E","CD2")),color.code =  c("red"),
	output.plot = FALSE,out.dir = "modulePlot",col.names = c("grey","grey","grey"),
	hubLabel.col = "black",hubLabel.sizeProp = 1,show.topn.hubs = Inf,show.legend = TRUE)
	
pnet.obj

## End(Not run)

Plot module hierarchy

Description

visualized module hierarchical structure.

Usage

plot_module_hierarchy(module.table,plot.coord = NULL,
edge.color = "grey",node.color = "black",node.label.color = "black",
label.scaleFactor = 0.5,node.scaleFactor = 0.2,arrow.size = 0.015,
data.col = NULL,low.color = "blue",mid.color = "white",
high.color = "red",mid.value = 0.05)
plot_module_hierarchy(module.table,plot.coord = NULL,
edge.color = "grey",node.color = "black",node.label.color = "black",
label.scaleFactor = 0.5,node.scaleFactor = 0.2,arrow.size = 0.015,
data.col = NULL,low.color = "blue",mid.color = "white",
high.color = "red",mid.value = 0.05)

Arguments

`module.table`	output from MEGENA.ModuleSummary. Specifically $module.table component of the output.
`plot.coord`	Two column coordinate matrix. rownames must be labelled according to module.table$id.
`edge.color`	Edge color to be shown.
`node.color`	If data.col = NULL, node.color is used to color nodes in figure.
`node.label.color`	Node label color.
`label.scaleFactor`	scale number to adjust node label sizes.
`node.scaleFactor`	scale number to adjust node sizes.
`arrow.size`	scale number to arrow size.
`data.col`	A character to specify data vector to color nodes in module.table.
`low.color`	If data.col != NULL, color to be used in lower value spectrum.
`mid.color`	If data.col != NULL, color to be used in middle value spectrum.
`high.color`	If data.col != NULL, color to be used in high value spectrum.
`mid.value`	If data.col != NULL, value to define middle value spectrum.

Details

Module hierarchy plotting functionality using ggplot2.

Value

A list containing output$hierarchy.obj = ggplot2 object, output$node.data = node attributes, output$edge.data = edge attributes.

Author(s)

Won-Min Song

Examples

## Not run: 
	rm(list = ls())
	data(Sample_Expression)
	ijw <- calculate.correlation(datExpr,doPerm = 2)
	el <- calculate.PFN(ijw[,1:3])
	g <- graph.data.frame(el,directed = FALSE)
	MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
	output.summary <- MEGENA.ModuleSummary(MEGENA.output,
	mod.pvalue = 0.05,hub.pvalue = 0.05,
	min.size = 10,max.size = 5000,
	annot.table = NULL,id.col = NULL,symbol.col = NULL,
	output.sig = TRUE)

	module.table = output.summary$module.table
	colnames(module.table)[1] <- "id"
	output.obj <- plot_module_hierarchy(module.table = module.table,
	label.scaleFactor = 0.15,arrow.size = 0.005,node.label.color = "blue")

	print(output.obj[[1]])

## End(Not run)
## Not run: 
	rm(list = ls())
	data(Sample_Expression)
	ijw <- calculate.correlation(datExpr,doPerm = 2)
	el <- calculate.PFN(ijw[,1:3])
	g <- graph.data.frame(el,directed = FALSE)
	MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
	output.summary <- MEGENA.ModuleSummary(MEGENA.output,
	mod.pvalue = 0.05,hub.pvalue = 0.05,
	min.size = 10,max.size = 5000,
	annot.table = NULL,id.col = NULL,symbol.col = NULL,
	output.sig = TRUE)

	module.table = output.summary$module.table
	colnames(module.table)[1] <- "id"
	output.obj <- plot_module_hierarchy(module.table = module.table,
	label.scaleFactor = 0.15,arrow.size = 0.005,node.label.color = "blue")

	print(output.obj[[1]])

## End(Not run)

subnetwork plotting functionality.

Description

A modification of plot_module() function for more general subnetwork plotting purpose.

Usage

plot_subgraph(module,hub = NULL,PFN,node.default.color = "black",
gene.set = NULL,color.code = "grey",show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,show.topn.hubs = 10,
node.sizeProp = 13,label.sizeProp = 13,label.scaleFactor = 10,layout = "kamada.kawai")
plot_subgraph(module,hub = NULL,PFN,node.default.color = "black",
gene.set = NULL,color.code = "grey",show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,show.topn.hubs = 10,
node.sizeProp = 13,label.sizeProp = 13,label.scaleFactor = 10,layout = "kamada.kawai")

Arguments

`module`	A character vector containing gene names to be subsetted.
`hub`	If provided, genes in hub will be highlighted as triangles in resulting figure.
`PFN`	igraph object retaining PFN topology.
`node.default.color`	Default node colors for those that do not intersect with signatures in gene.set.
`gene.set`	A list object containing signatures for customized coloring of nodes in resulting network plot.
`color.code`	A character vector with matched length to "gene.set", to specify colors for each signature.
`show.legend`	TRUE/FALSE for showing node legend on the bottom of the figure.
`label.hubs.only`	TRUE/FALSE to show labels for significant hub genes only, or all genes. Defauly is TRUE.
`hubLabel.col`	Label color for hubs. Default is "red"
`hubLabel.sizeProp`	A multiplicative factor to adjust hub label sizes with respect to node size values. Default is 0.5
`show.topn.hubs`	Maximal number of hubs to label on module subnetwork. Default is 10.
`node.sizeProp`	A multiplicative factor to adjust node sizes with respect to 90th percentile degree node size. Default is 13
`label.sizeProp`	A multiplicative factor to adjust node label sizes with respect to 90th percentile degree node size. Default is 13
`label.scaleFactor`	Overall scale factor to control the final size of node labels appearing in figure. Default is 10.
`layout`	Network layout algorithm to apply. Options are: "kamada.kawai", "fruchterman.reingold".

Details

Subnetwork plot functionality with application of "ggrepel" package for node labeling. The most effective way to control overall node label size is through label.scaleFactor.

Value

A list object holding ggplot object and node annotation table.

Author(s)

Won-Min Song

Examples

## Not run: 
rm(list = ls())
library(MEGENA)

data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

pnet.obj <- plot_subgraph(module = output.summary$modules[[1]],
hub = c("CD3E","CD2"),PFN = g,node.default.color = "black",
gene.set = NULL,color.code = c("grey"),show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,
show.topn.hubs = 10,node.sizeProp = 13,label.sizeProp = 13,
label.scaleFactor = 10,layout = "kamada.kawai")

# the plot	
pnet.obj[[1]]

# the annotation
pnet.obj[[2]]

## End(Not run)
## Not run: 
rm(list = ls())
library(MEGENA)

data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)

pnet.obj <- plot_subgraph(module = output.summary$modules[[1]],
hub = c("CD3E","CD2"),PFN = g,node.default.color = "black",
gene.set = NULL,color.code = c("grey"),show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,
show.topn.hubs = 10,node.sizeProp = 13,label.sizeProp = 13,
label.scaleFactor = 10,layout = "kamada.kawai")

# the plot	
pnet.obj[[1]]

# the annotation
pnet.obj[[2]]

## End(Not run)

.gmt file reader function

Description

An interface function to read-in .gmt format gene signature file.

Usage

read.geneSet(geneSet.file)
read.geneSet(geneSet.file)

Arguments

geneSet.file

text file containing gene signatures in .gmt format

Details

Each line of lists in geneset.file is a single set of signature.

Value

loads signatures into a list object.

Author(s)

Won-Min Song

Toy example data

Description

A portion of TCGA breast cancer data to test run MEGENA.

Usage

data(Sample_Expression)
data(Sample_Expression)

Format

Contains a matrix object, "datExpr". Use data(Sample_Expression) to load.

Details

a gene expression matrix.

References

1. Song, W.M. and B. Zhang, Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol, 2015. 11(11): p. e1004574.

Package 'MEGENA'

Help Index

co-expression network analysis

Description

Details

Author(s)

References

PFN calculation

Description

Usage

Arguments

Details

Value

Author(s)

Examples

correlation calculation

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Parallelized PFN computation

Description

Usage

Arguments

Details

Value

Author(s)

Toy example data

Description

Usage

Format

Details

References

MEGENA clustering + MHA

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Draw sunburst plot showing MEGENA module hierarchy.

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Curated MSigDB signatures for testing funtion/pathway enrichments of MEGENA modules

Description

Usage

Format

Details

Module Hierarchy Visualization

Description

Usage

Arguments

Details

Value

Author(s)

Examples

calculate module degree statistics based on random triangulation model via T1 and T2 moves.

Description

Usage

Arguments

Details

Value

Author(s)

Examples

summarize hub information.

Description

Usage

Arguments

Details

Value

Author(s)