#' Construct a network layout. Randomly distributed module positions
#'
#' @param cor Correlation matrix
#' @param nodeGroup Classification information of network nodes
#' @examples
#' data
#' data(ps)
#' result = corMicro (ps = ps,N = 100,r.threshold=0.8,p.threshold=0.05,method = "pearson")
#' #Extract correlation matrix
#' cor = result[[1]]
#' # Extract tax table for grouping
#' ps_net = result[[3]]
#' vegan_tax <- function(physeq){
#' tax <- tax_table(physeq)
#'
#' return(as(tax,"matrix"))
#' }
#' tax_table = as.data.frame(vegan_tax(ps_net))
#' group = as.data.frame(tax_table)
#' group$ID = row.names(group)
#' netClu = data.frame(ID = row.names(group),group = group$Phylum)
#' # Calculate the layout of netwok, extract the coordinate of node
#' result2 = randomClusterG (cor = cor,nodeGroup =netClu )
#' node = result2[[1]]
#'
#'
#' @return result2 Which contains 2 data.frame. Result2[[1]], consists of OTU and its corresponding coordinates.
#' result2[[2]], consists of the network center coordinates of each group
#' @author Contact: Tao Wen \email{taowen@@njau.edu.cn} Penghao Xie \email{2019103106@@njau.edu.cn} yongxin liu \email{yxliu@@genetics.ac.cn} Jun Yuan \email{junyuan@@njau.edu.cn}
#' @references
#'
#' Tao Wen#, Penghao Xie#, Shengdie Yang, Guoqing Niu, Xiaoyu Liu, Zhexu Ding, Chao Xue, Yong-Xin Liu *, Qirong Shen, Jun Yuan*
#' ggClusterNet: an R package for microbiome network analysis and modularity-based multiple network layouts
#' iMeta 2022,DOI: \url{doi: 10.1002/imt2.32}
#' @export
randomClusterG = function(cor = cor,nodeGroup =netClu ){
num = length(levels(nodeGroup$group))
xs = as.data.frame(table(nodeGroup$group))
r = xs$Freq/10
#--Calculate the total edge radius and#-----
rtotal = sum(r)
#---Start the coordinates of the clustering module#-----
whichnum = dim(combn(num,2))[2]
# Pairwise comparison, the radius is greater than a certain threshold is TRUE, all TRUE is qualified#---------
A = rep(FALSE,whichnum)
A
while (all(A) == FALSE) {
#--Randomly generate a set of coordinates-these coordinate ranges are limited to the sum of all cluster radii#----
da = data.frame(x =sample((0:rtotal),num ),y = sample((rtotal):0,num ))
head(da)
for (i in 1:whichnum) {
#--Comparison between the two groups
cs = combn(num,2)
cs
# i =13
cs[,i]
# Extract the coordinates of the two sets of cluster center coordinates
x1 = da[cs[,i],][1,1]
x2 = da[cs[,i],][2,1]
y1 = da[cs[,i],][1,2]
y2 = da[cs[,i],][2,2]
# Determine whether the distance between these two coordinates is greater than the sum of the two clustering radii
AS= (x1-x2)^2 +(y1-y2)^2
A[i]= sqrt(AS)> sum(r[cs[,i]])
}
}
#-Start calculating layout#-----
for (i in 1:length(levels(nodeGroup$group))) {
#--Extract all otu in this group
as = dplyr::filter(nodeGroup, group == levels(nodeGroup$group)[i])
if (length(as$ID) == 1) {
data = cbind(da[i,1],da[i,2] )
data =as.data.frame(data)
row.names(data ) = as$ID
data$elements = row.names(data )
colnames(data)[1:2] = c("X1","X2")
}
as$ID = as.character( as$ID)
#-----------Calculation of a single circular coordinate
if (length(as$ID)!=1 ) {
m = cor[as$ID,as$ID]
d =m
d <- as.edgelist.sna(d)
# if (is.list(d))
# d <- d[[1]]
n <- attr(d, "n")
# ζεεεΎ
s = r[i]
# if (i == 1) {
# sori = s*2
data = cbind(sin(2 * pi * ((0:(n - 1))/n))*s +da[i,1], cos(2 * pi * ((0:(n - 1))/n))*s +da[i,2])
# }
data =as.data.frame(data)
row.names(data ) = row.names(m)
data$elements = row.names(data )
colnames(data)[1:2] = c("X1","X2")
}
head(data)
# ggplot(data) + geom_point(aes(x = X1,y = X2))
if (i == 1) {
oridata = data
}
if (i != 1) {
oridata = rbind(oridata,data)
}
}
plotcord = oridata[match(oridata$elements,row.names(cor )),]
return(list(plotcord,da))
}
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