View source: R/PolygonRrClusterG.R
PolygonRrClusterG | R Documentation |
Construct a network layout. Calculate the layout according to grouping and random distribution
Construct a network layout. Calculate the layout according to grouping and random distribution
PolygonRrClusterG(cor = cor, nodeGroup = netClu, zoom = 1, zoom2 = 1, bio = F)
PolygonRrClusterG(cor = cor, nodeGroup = netClu, zoom = 1, zoom2 = 1, bio = F)
cor |
Correlation matrix |
nodeGroup |
Classification information of network nodes |
zoom |
Set the distance between modules |
zoom2 |
Scaling module radius size |
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
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
Contact: Tao Wen 2018203048@njau.edu.cn Jun Yuan junyuan@njau.edu.cn Penghao Xie 2019103106@njau.edu.cn
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: doi: 10.1186/s40168-018-0537-x
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: doi: 10.1186/s40168-018-0537-x
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]]
data
data(ps)
result = corMicro (ps = ps,N = 0.02,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]]
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