cor.network: Differential correlation network analysis

In wenbostar/metaX: A flexible and comprehensive software for processing omics data.

Differential correlation network analysis

Description

Differential correlation network analysis

Usage

cor.network(para, group, valueID = "value", cor.method = "spearman",
  threshold = 0.1, p.adjust.methods = "BH", plot = TRUE,
  graph_format = "gml", mark.groups = TRUE, top.groups = 1,
  cluster.method = 1, find.largest.component = TRUE, ...)

Arguments

para	A metaXpara object
group	Samples used for plot
valueID	The name of the column that used for plot
cor.method	Method for correlation:"pearson","spearman" or "kendall"
threshold	A threshold of significance levels of differential correlation
p.adjust.methods	c("local", holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")
plot	Whether to plot network figure
cluster.method	The function tries to find dense subgraph. 1=cluster_fast_greedy,2=cluster_walktrap,3=cluster_edge_betweenness, 4=cluster_optimal,5=cluster_leading_eigen,6=cluster_spinglass, 7=cluster_label_prop,8=cluster_louvain,9=cluster_infomap
...	Additional parameter

Value

The name of result file

Author(s)

Bo Wen [e-mail]

Examples

para <- new("metaXpara")
pfile <- system.file("extdata/MTBLS79.txt",package = "metaX")
sfile <- system.file("extdata/MTBLS79_sampleList.txt",package = "metaX")
rawPeaks(para) <- read.delim(pfile,check.names = FALSE)
sampleListFile(para) <- sfile
para <- reSetPeaksData(para)
para <- missingValueImpute(para)
resfile <- cor.network(para,group=c("S","C"))

wenbostar/metaX documentation built on July 4, 2023, 7:50 p.m.