nethet (version 1.4.2)

ggmgsa_multisplit: Multi-split GGMGSA (parallelized computation)

Description

Multi-split GGMGSA (parallelized computation)

Usage

ggmgsa_multisplit(x1, x2, b.splits = 50, gene.sets, gene.names,
  gs.names = NULL, method.p.adjust = "fdr", order.adj.agg = "agg-adj",
  mc.flag = FALSE, mc.set.seed = TRUE, mc.preschedule = TRUE,
  mc.cores = getOption("mc.cores", 2L), verbose = TRUE, ...)

Arguments

x1

Expression matrix for condition 1 (mean zero is required).

x2

Expression matrix for condition 2 (mean zero is required).

b.splits

Number of random data splits (default=50).

gene.sets

List of gene-sets.

gene.names

Gene names. Each column in x1 (and x2) corresponds to a gene.

gs.names

Gene-set names (default=NULL).

method.p.adjust

Method for p-value adjustment (default='fdr').

order.adj.agg

Order of aggregation and adjustment of p-values. Options: 'agg-adj' (default), 'adj-agg'.

mc.flag

If TRUE use parallel execution for each b.splits via function mclapply of package parallel.

mc.set.seed

See mclapply. Default=TRUE

mc.preschedule

See mclapply. Default=TRUE

mc.cores

Number of cores to use in parallel execution. Defaults to mc.cores option if set, or 2 otherwise.

verbose

If TRUE, show output progess.

...

Other arguments (see diffnet_singlesplit).

Value

• List consisting of
• medagg.pval
  Median aggregated p-values
• meinshagg.pval
  Meinshausen aggregated p-values
• pval
  matrix of p-values before correction and adjustement, dim(pval)=(number of gene-sets)x(number of splits)
• teststatmed
  median aggregated test-statistic
• teststatmed.bic
  median aggregated bic-corrected test-statistic
• teststatmed.aic
  median aggregated aic-corrected test-statistic
• teststat
  matrix of test-statistics, dim(teststat)=(number of gene-sets)x(number of splits)
• rel.edgeinter
  normalized intersection of edges in condition 1 and 2
• df1
  degrees of freedom of GGM obtained from condition 1
• df2
  degrees of freedom of GGM obtained from condition 2
• df12
  degrees of freedom of GGM obtained from pooled data (condition 1 and 2)

Details

Computation can be parallelized over many data splits.

Examples

#######################################################
##This example illustrates the use of GGMGSA         ##
#######################################################


## Generate networks
set.seed(1)
p <- 9#network with p nodes
n <- 40
hub.net <- generate_2networks(p,graph='hub',n.hub=3,n.hub.diff=1)#generate hub networks
invcov1 <- hub.net[[1]]
invcov2 <- hub.net[[2]]
plot_2networks(invcov1,invcov2,label.pos=0,label.cex=0.7)

## Generate data
library('mvtnorm')
x1 <- rmvnorm(n,mean = rep(0,p), sigma = cov2cor(solve(invcov1)))
x2 <- rmvnorm(n,mean = rep(0,p), sigma = cov2cor(solve(invcov2)))

## Run DiffNet
# fit.dn <- diffnet_multisplit(x1,x2,b.splits=2,verbose=FALSE)
# fit.dn$medagg.pval

## Identify hubs with 'gene-sets'
gene.names <- paste('G',1:p,sep='')
gsets <- split(gene.names,rep(1:3,each=3))

## Run GGM-GSA
fit.ggmgsa <- ggmgsa_multisplit(x1,x2,b.splits=2,gsets,gene.names,verbose=FALSE)
summary(fit.ggmgsa)
fit.ggmgsa$medagg.pval#median aggregated p-values
p.adjust(apply(fit.ggmgsa$pval,1,median),method='fdr')#or: first median aggregation,
                                                      #second fdr-correction