R语言NetCoMi包Co-occurrence网络图微生物16S网络比较核心物种.pdf

R语⾔NetCoMi包Co-occurrence⽹络图微⽣物16S⽹络⽐较核 ⼼物种 # ⾃⼰⼀些问题 ：⾥⾯有sparcc？ # ithub本地安装 在上⾯⽹站找到下载 #devtools::install_local(C:/Users/xxx/Documents/NetCoMi- 1.0.2.tar.gz) #加载包 安装在了R-3.6版本 library (NetCoMi) #Control组 otu1=read.csv (L6绝对丰度filter10_Control.csv,row.names = 1,header=T) otu1=t(otu1) #Case组 otu2=read.csv (L6绝对丰度filter10_OSCC.csv,row.names = 1,header=T) otu2=t(otu2) #data= 【rows are samples】, 【columns are OTUs/taxa】 net_single2 - netConstruct(data=otu1, data2=otu2, measure = sparcc, # 可选 ：spearman spieceasi sparcc #使⽤ clr 变换作为归⼀化⽅法 normMethod = clr, zeroMethod = none, sparsMethod = threshold,adj ust = adaptBH, #采⽤0.3的阈值作为稀疏化⽅法，只连接绝对相关性⼤于或等于0.3的OTU thresh = 0.3, seed= 123456, filtTax = none, verbose = 3) summary (net_single2) #??netConstruct #⽹络分析和绘图 ??netAnalyze props_single2 - netAnalyze(net_single2, centrLCC = TRUE, clustMethod = cluster_fast_greedy, hubPar = c(degree, closeness,eigenvector)) #hubPar = c(degree, between, closeness,eigenvector)) 也可以⽤其中⼀个筛选出核⼼物种 summary (props_single2) p1=plot(props_single2, nodeColor = cluster, nodeSize = clr, repulsion = 0.8, rmSingles = TRUE, #所有标签⼤⼩相等=false labelScale = FALSE, #改变圆圈的字体⼤⼩ cexLabels = 0.6, #如果该值减⼩，则节点⼤⼩更相似 # （与cexNodes结合）对于扩⼤⼩节点同时保持⼤节点的⼤⼩很有⽤ nodeSizeSpread = 3, #改变圆圈的⼤⼩ cexNodes = 4, #显⽰圆圈⾥⾯标签的⼤⼩ shortenLabels= none, title1 = Network on OTU level with spearman correlations, showTitle = TRUE, cexTitle = 1.5) #加载 门信息，这个如下 主要要门