NDMS(6海島樣本菌落組成差異分析)

1、精選優(yōu)質(zhì)文檔-傾情為你奉上精選優(yōu)質(zhì)文檔-傾情為你奉上專心-專注-專業(yè)專心-專注-專業(yè)精選優(yōu)質(zhì)文檔-傾情為你奉上專心-專注-專業(yè)NDMS(6海島樣本菌落組成差異分析）R Markdown同時(shí)考慮g樣本間距離，也考慮了樣本距離以及物種（OTU）在各樣本中的分布，由于參與排序的物種（OTU）種類過(guò)多不便全部展示，最終選擇展示相對(duì)豐度前10的OTU。對(duì)6種海島分組，在作圖結(jié)果中分別以不同顏色和不同形狀的點(diǎn)表示出各個(gè)海島的樣品分布，而主要的OTU，在圖中對(duì)應(yīng)的坐標(biāo)位置直接展示其名稱。library(vegan)# Loading required package: permute# Loading re

2、quired package: lattice# This is vegan 2.5-6library(ggplot2)#讀入 OTU 豐度表otu - read.delim(otu_table.txt, s = 1, sep = t, stringsAsFactors = FALSE, s = FALSE)otu - data.frame(t(otu)#讀入樣本分組文件group - read.delim(group.txt, sep = t, stringsAsFactors = FALSE)#排序，預(yù)設(shè) 2 個(gè)排序軸nmds1 - metaMDS(ot

3、u, distance = bray, k = 2)# Square root transformation# Wisconsin double standardization# Run 0 stress 0. # Run 1 stress 0. # . New best solution# . Procrustes: rmse 0. max resid 0. # Run 2 stress 0. # . New best solution# . Procrustes: rmse 0. max resid 0. # Run 3 stress 0. # . Procrustes: rmse 0.

4、max resid 0. # Run 4 stress 0. # . New best solution# . Procrustes: rmse 0. max resid 0. # Run 5 stress 0. # . New best solution# . Procrustes: rmse 0. max resid 0. # Run 6 stress 0. # . New best solution# . Procrustes: rmse 0. max resid 0. # Run 7 stress 0. # . New best solution# . Procrustes: rmse

5、 0. max resid 0. # Run 8 stress 0. # Run 9 stress 0. # Run 10 stress 0. # Run 11 stress 0. # Run 12 stress 0. # Run 13 stress 0. # Run 14 stress 0. # Run 15 stress 0. # Run 16 stress 0. # Run 17 stress 0. # Run 18 stress 0. # Run 19 stress 0. # . Procrustes: rmse 0. max resid 0. # . Similar to previ

6、ous best# Run 20 stress 0. # * Solution reached#=#提取應(yīng)力函數(shù)值（stress）nmds1.stress - nmds1$stress#提取樣本排序坐標(biāo)nmds1.point - data.frame(nmds1$point)#提取物種（OTU）排序坐標(biāo)nmds1.species - data.frame(nmds1$species)#簡(jiǎn)要繪圖展示nmds_plot - nmds1nmds_plot$species - nmds_plot$species1:10, #plot(nmds_plot, type = t, main = paste(

7、Stress =, round(nmds1$stress, 2)#=#讀入現(xiàn)有的距離矩陣dis - read.delim(bray.txt, s = 1, sep = t, stringsAsFactors = FALSE, s = FALSE)#排序#nmds2 - metaMDS(as.dist(dis), k = 2)#=#提取樣本點(diǎn)坐標(biāo)（前兩軸）sample_site - nmds1.point1:2sample_site$names - rownames(sample_site)names(sample_site)1:2 - c(NMDS1, NM

8、DS2)#為樣本點(diǎn)坐標(biāo)添加分組信息sample_site - merge(sample_site, group, by = names, all.x = TRUE)#提取相對(duì)豐度 top20 的 OTU 坐標(biāo)（前兩軸）otu - read.delim(otu_table.txt, s = 1, sep = t, stringsAsFactors = FALSE, s = FALSE)otu$sum - rowSums(otu)otu - otuorder(otu$sum, decreasing = TRUE), species_site -nmds1.speciesrownames(otu1:10, ), 1:2#整理為與 sample_site 相同的樣式，方便被 ggplot2 識(shí)別species_site$group - rownames(species_site)names(species_site)1:2 - c(NM