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The properties of the co-occurrence network and comparison of degree.Rmd
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---
title: "The properties of the co-occurrence network and comparison of degree"
author: "xyz"
date: "2020/4/19"
output: html_document
---
## CK
(1)Remove the connections with correlation Coefficient < 0.6 , (2)Remove self-connected nodes, (3)Remove nodes with abundance <%0.005
[igragh documents](https://igraph.org/r/doc/)
```{r}
library(igragh)
df<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network/CK/dot/genus.spearman.index.list")
# Remove self-connected nodes
df2<-df[df$V1!=df$V2,]
colnames(df2)<-c("from","to","Spearman")
df3<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network/CK/otu_table.g.relative.xls",header = T)
df4<-data.frame(Genus=df3$Taxonomy,Abundance=apply(df3[,c(-1,-29)],1,sum))
g<-graph_from_data_frame(df2, directed=F, vertices=df4)
g.attribute<-list(
# ND (Network diameter)
Diameter=diameter(g),
# MD (Modularity)
Modularity=modularity(g, membership(cluster_walktrap(g))),
# Clustering coefficient
Clustering=transitivity(g),
# Graph density
Density=edge_density(simplify(g), loops=FALSE),
# the number of its adjacent edges
Degree=degree(g),
# APL (Average path length or mean distance)
Distance=mean_distance(g)
)
```
## Encapsulated as a function
```{r}
graghAttribute<-function(edges,nodes){
edges<-edges[edges$V1!=edges$V2,]
g<-graph_from_data_frame(edges, directed=F, vertices=nodes)
list(
# ND (Network diameter)
Diameter=diameter(g),
# MD (Modularity)
Modularity=modularity(g, membership(cluster_walktrap(g))),
# Clustering coefficient
Clustering=transitivity(g),
# Graph density
Density=edge_density(simplify(g), loops=FALSE),
# the number of its adjacent edges
Degree=degree(g),
# APL (Average path length or mean distance)
Distance=mean_distance(g)
)
}
df1<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network/CK/dot/genus.spearman.index.list")
df2<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network/CK/otu_table.g.relative.xls",header = T)
CK.attribute<-graghAttribute(df1,df2)
df3<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network/U/dot/genus.spearman.index.list")
df4<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network/U/otu_table.g.relative.xls",header = T)
U.attribute<-graghAttribute(df3,df4)
# W = 45510, p-value = 0.01815 Significant difference between CK and U
wilcox.test(CK.attribute$Degree,U.attribute$Degree)
df5<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network2/F/dot/genus.spearman.index.list")
df6<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network2/F/otu_table.g.relative.xls",header = T)
F.attribute<-graghAttribute(df5,df6)
df7<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network2/N/dot/genus.spearman.index.list")
df8<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network2/N/otu_table.g.relative.xls",header = T)
N.attribute<-graghAttribute(df7,df8)
df9<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network2/R/dot/genus.spearman.index.list")
df10<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network2/R/otu_table.g.relative.xls",header = T)
R.attribute<-graghAttribute(df9,df10)
# Significant difference among F N R
# W = 31739, p-value = 3.493e-06 Significant difference
wilcox.test(F.attribute$Degree,R.attribute$Degree)
# W = 31746, p-value = 3.549e-06 Significant difference
wilcox.test(N.attribute$Degree,R.attribute$Degree)
# W = 40232, p-value = 0.7361 Significant difference
wilcox.test(F.attribute$Degree,N.attribute$Degree)
df11<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/CKF/dot/genus.spearman.index.list")
df12<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/CKF/otu_table.g.relative.xls",header = T)
CKF.attribute<-graghAttribute(df11,df12)
df13<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/CKN/dot/genus.spearman.index.list")
df14<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/CKN/otu_table.g.relative.xls",header = T)
CKN.attribute<-graghAttribute(df13,df14)
df15<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/CKR/dot/genus.spearman.index.list")
df16<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/CKR/otu_table.g.relative.xls",header = T)
CKR.attribute<-graghAttribute(df15,df16)
# Significant difference under CK among F N R
# non-significant difference
wilcox.test(CKF.attribute$Degree,CKR.attribute$Degree)
# non-significant difference
wilcox.test(CKN.attribute$Degree,CKR.attribute$Degree)
# non-significant difference
wilcox.test(CKF.attribute$Degree,CKN.attribute$Degree)
df17<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/UF/dot/genus.spearman.index.list")
df18<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/UF/otu_table.g.relative.xls",header = T)
UF.attribute<-graghAttribute(df17,df18)
df19<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/UN/dot/genus.spearman.index.list")
df20<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/UN/otu_table.g.relative.xls",header = T)
UN.attribute<-graghAttribute(df19,df20)
df21<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/UR/dot/genus.spearman.index.list")
df22<-read.table("X101SC19050239-Z01-F001-B1-41_result/04.BetaDiversity/Network3/UR/otu_table.g.relative.xls",header = T)
UR.attribute<-graghAttribute(df21,df22)
# Significant difference under U among F N R
# non-significant difference
wilcox.test(UF.attribute$Degree,UR.attribute$Degree)
# non-significant difference
wilcox.test(UN.attribute$Degree,UR.attribute$Degree)
# W = 36370, p-value = 0.02187 significant difference
wilcox.test(UF.attribute$Degree,UN.attribute$Degree)
```
## summarise significant difference among CK, U, N, F, R
```{r}
list2array<-function(x){
AD<-format(round(mean(x$Degree), 3), nsmall = 3,trim = T)
ADsd<-format(round(sd(x$Degree), 3), nsmall = 3,trim = T)
temp<-c(ND=x$Diameter,MD=x$Modularity,CC=x$Clustering,GD=x$Density,APL=x$Distance)
temp<-format(round(temp, 3), nsmall = 3,trim = T)
c(AD=paste0(AD,"±",ADsd),temp)
}
results<-rbind(ZN=list2array(CK.attribute),N=list2array(U.attribute),
R=list2array(R.attribute),N=list2array(N.attribute),`F`=list2array(F.attribute))
write.csv(results,"properties of the co-occurrence network.csv")
```