NDMS(6海岛样本菌落组成差异分析)

1、精选优质文档-倾情为你奉上精选优质文档-倾情为你奉上专心-专注-专业专心-专注-专业精选优质文档-倾情为你奉上专心-专注-专业NDMS(6海岛样本菌落组成差异分析）R Markdown同时考虑g样本间距离，也考虑了样本距离以及物种（OTU）在各样本中的分布，由于参与排序的物种（OTU）种类过多不便全部展示，最终选择展示相对丰度前10的OTU。对6种海岛分组，在作图结果中分别以不同颜色和不同形状的点表示出各个海岛的样品分布，而主要的OTU，在图中对应的坐标位置直接展示其名称。library(vegan)# Loading required package: permute# Loading re

2、quired package: lattice# This is vegan 2.5-6library(ggplot2)#读入 OTU 丰度表otu - read.delim(otu_table.txt, s = 1, sep = t, stringsAsFactors = FALSE, s = FALSE)otu - data.frame(t(otu)#读入样本分组文件group - read.delim(group.txt, sep = t, stringsAsFactors = FALSE)#排序，预设 2 个排序轴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#=#提取应力函数值（stress）nmds1.stress - nmds1$stress#提取样本排序坐标nmds1.point - data.frame(nmds1$point)#提取物种（OTU）排序坐标nmds1.species - data.frame(nmds1$species)#简要绘图展示nmds_plot - nmds1nmds_plot$species - nmds_plot$species1:10, #plot(nmds_plot, type = t, main = paste(

7、Stress =, round(nmds1$stress, 2)#=#读入现有的距离矩阵dis - read.delim(bray.txt, s = 1, sep = t, stringsAsFactors = FALSE, s = FALSE)#排序#nmds2 - metaMDS(as.dist(dis), k = 2)#=#提取样本点坐标（前两轴）sample_site - nmds1.point1:2sample_site$names - rownames(sample_site)names(sample_site)1:2 - c(NMDS1, NM

8、DS2)#为样本点坐标添加分组信息sample_site - merge(sample_site, group, by = names, all.x = TRUE)#提取相对丰度 top20 的 OTU 坐标（前两轴）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#整理为与 sample_site 相同的样式，方便被 ggplot2 识别species_site$group - rownames(species_site)names(species_site)1:2 - c(NM