前言
在之前的文章MEGENA原理已经介绍了MEGENA的基本原理，那么基于示例数据本文来解读一下MEGENA的结果
示例数据
rm(list = ls()) # rm R working space
library(MEGENA)
# input parameters
n.cores <- 1; # number of cores/threads to call for PCP
doPar <-F; # do we want to parallelize?
method = "pearson" # method for correlation. either pearson or spearman.
FDR.cutoff = 0.05 # FDR threshold to define significant correlations upon shuffling samples.
module.pval = 0.05 # module significance p-value. Recommended is 0.05.
hub.pval = 0.05 # connectivity significance p-value based random tetrahedral networks
cor.perm = 10; # number of permutations for calculating FDRs for all correlation pairs.
hub.perm = 100; # number of permutations for calculating connectivity significance p-value.
# annotation to be done on the downstream
annot.table=NULL
id.col = 1
symbol.col= 2
###########
data(Sample_Expression) # load toy example data
ijw <- calculate.correlation(datExpr,doPerm = cor.perm,output.corTable = FALSE,output.permFDR = FALSE)
##### calculate PFN
el <- calculate.PFN(ijw[,1:3],doPar = doPar,num.cores = n.cores,keep.track = FALSE)
g <- graph.data.frame(el,directed = FALSE)
##### perform MCA clustering.
MEGENA.output <- do.MEGENA(g,
mod.pval = module.pval,hub.pval = hub.pval,remove.unsig = TRUE,
min.size = 10,max.size = vcount(g)/2,
doPar = doPar,num.cores = n.cores,n.perm = hub.perm,
save.output = FALSE)
###### unregister cores as these are not needed anymore.
summary.output <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = module.pval,hub.pvalue = hub.pval,
min.size = 10,max.size = vcount(g)/2,
annot.table = annot.table,id.col = id.col,symbol.col = symbol.col,
output.sig = TRUE)
最终summary.output：
1. summary.output$modules
summary.output可以把聚类的sub-cluster对应的基因获取到
2. summary.output$module.table
summary.output$module.table
可以把每个sub-cluster的hub gene（度比较大的基因）获取到
将sub-cluster的基因导入Cytoscape
由原理我们知道，基因之间的权重关系是由gene pair之间的相关性体现的，而Cytoscape的基本输入是node from，node to和weight
所以我们只需要在PFN网络中筛选出相应的sub-cluster的基因即可
el：
sub_cluster <- summary.output$modules$c1_2
df1 = el[el[,1] %in% sub_cluster,]
df2 = df1[df1[,2] %in% sub_cluster,]
然后将df2写入txt，导入Cytoscape即可
