R/PolygonRrClusterG.R

In taowenmicro/ggClusterNet:

#' Construct a network layout. Calculate the layout according to grouping and random distribution
#'
#' @param cor Correlation matrix
#' @param nodeGroup Classification information of network nodes
#' @param zoom Set the distance between modules
#' @param zoom2 Scaling module radius size
#' @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
#' 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)
#' netClu$group = as.factor(netClu$group)
#' result2 = PolygonRrClusterG (cor = cor,nodeGroup =netClu )
#' node = result2[[1]]
#'
#'
#' @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



PolygonRrClusterG = function(cor = cor,nodeGroup =netClu,zoom = 1,zoom2 = 1,bio = F){

  #
  num = length(levels(nodeGroup$group))

  #
  xs = as.data.frame(table(nodeGroup$group))
  r = xs$Freq/10 *zoom

  # Calculate angle according to group
  arg = seq(0,360,360/(length(r))) - 180
  # i = 1
  rsum = sum(r)
  x= rep(0,length(r))
  y = rep(0,length(r))
  for (i in 1:length(r)) {
    x[i] = (rsum + r[i])* sin(arg[i]* 3.14/180)
    y[i] = (rsum + r[i])* cos(arg[i]* 3.14/180)
  }
  if (bio == T) {
    for (i in 1:length(r)) {
      x[i] = 0
      y[i] = 0
    }
  }

  da = data.frame(x = x,y = y)
  nodeGroup$group = as.factor(nodeGroup$group)
  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")
      # 提取半径
      s = r[i]
      s = s * zoom2

      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")

    }


    # 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))
}