#' Construct a network layout. Arrange network nodes to different locations according to grouping
#'
#' @param cor Correlation matrix
#' @param nodeGroup Classification information of network nodes.Group according to actual requirements, see example
#' @param r1 big cluster r
#' @param N break of each cluster
#' @param cut line need to cut off
#' @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]]
#' # building the node group
#' netClu = data.frame(ID = row.names(cor),group =rep(1:3,length(row.names(cor)))[1:length(row.names(cor))] )
#' netClu$group = as.factor(netClu$group)
#' result2 = PolygonModsquareG(cor = cor,nodeGroup =netClu,r1 = 1,N = 1.1,cut = 3,line = FALSE)
#'
#'
#' @return result2 Which contains 2 lists.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{2018203048@@njau.edu.cn} Jun Yuan \email{junyuan@@njau.edu.cn} Penghao Xie \email{2019103106@@njau.edu.cn}
#' @references
#'
#' Yuan J, Zhao J, Wen T, Zhao M, Li R, Goossens P, Huang Q, Bai Y, Vivanco JM, Kowalchuk GA, Berendsen RL, Shen Q
#' Root exudates drive the soil-borne legacy of aboveground pathogen infection
#' Microbiome 2018,DOI: \url{doi: 10.1186/s40168-018-0537-x}
#' @export
PolygonModsquareG <- function(cor = cor,nodeGroup =netClu,r1 = 1,N = 1.1,cut = 3){
mod = as.data.frame(table(nodeGroup$group)) %>%
arrange(desc(Freq))
r = c(r1,rep(0,(dim(mod)[1]-1)))
for (i in 2:dim(mod)[1]) {
r[i] = r1*mod[i,2]/mod[1,2]
}
#
# r * 2 + 1
data = data.frame(x = c((cumsum(r * 2 + N))[1] ,(cumsum(r * 2 + N))[-1]),y = 0 )
data
if (cut != 1) {
# A = r * 2 + N
num = 1
# i = 2
A = r * N
# B = data$x
r * 2 + N
for (i in num:length(A)) {
if (sum(A[num:i]) > sum((r * N))/cut | i == length(A) ) {
data$y[num:i] = min(data$x[num:i])
data$x[num:i] = data$x[num:i] - data$x[num]
print(i)
num = i + 1
}
}
}
data
da = data
nodeGroup$group = factor(nodeGroup$group ,levels = mod$Var1)
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$ID = as.character( as$ID)
# Calculation of a single circular coordinate
if (length(as$ID)!=1 ) {
m = cor[as$ID,as$ID]
d =m
d <- as.edgelist.sna(d)
# if (is.list(d))
# d <- d[[1]]
n <- attr(d, "n")
#
s = r[i]
data = cbind(sin(2 * pi * ((0:(n - 1))/n))*s +da[i,1], cos(2 * pi * ((0:(n - 1))/n))*s +da[i,2])
data =as.data.frame(data)
row.names(data ) = row.names(m)
data$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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