ggmgsa_multisplit {nethet} | R Documentation |
Multi-split GGMGSA (parallelized computation)
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, ...)
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 |
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). |
Computation can be parallelized over many data splits.
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) |
n.stadler
####################################################### ##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