R语言:microeco:一个用于微生物群落生态学数据挖掘的R包,第三，trans_venn class和trans_alpha class

这次我们分享，trans_venn类用于venn分析。trans_alpha class，Alpha多样性可以使用trans_alpha类进行转换和绘制。

为了分析组的唯一otu和共享otu，我们首先根据sample_table中的“Group”列合并样本。

dataset1 <- dataset$merge_samples(use_group = "Group")
t1 <- trans_venn$new(dataset1, ratio = "seqratio")
t1$plot_venn()

#当组数过多，无法用维恩图表示时，可以用花瓣图表示。
dataset1 <- dataset$merge_samples(use_group = "Type")
t1 <- trans_venn$new(dataset1)
t1$plot_venn(petal_plot = TRUE)

Alpha多样性可以使用trans_alpha类进行转换和绘制

> t1 <- trans_alpha$new(dataset = dataset, group = "Group")
The transformed diversity data is stored in object$data_alpha ...
The group statistics are stored in object$data_stat ...
> t1
trans_alpha object:
data_alpha have 7 columns:  Sample, Measure, Value, SampleID, Group, Type, Saline 
data_alpha$Measure:  Observed, Chao1, ACE, Shannon, Simpson, InvSimpson, Fisher, Pielou, Coverage 
data_stat have 6 columns:  Group, Measure, N, Mean, SD, SE 
> # return t1$alpha_stat
> t1$data_stat[1:5, ]
  Group  Measure  N        Mean           SD           SE
1    CW Observed 30 1843.166667 220.57918796 40.272065654
2    CW    Chao1 30 2552.635929 338.11686659 61.731411634
3    CW      ACE 30 2715.680687 367.02577198 67.009431500
4    CW  Shannon 30    6.307972   0.53551395  0.097771024
5    CW  Simpson 30    0.989680   0.01304767  0.002382167

> t1$cal_diff(method = "KW")
The result is stored in object$res_diff ...
> # return t1$res_alpha_diff
> t1$res_diff[1:5, ]
    Comparison  Measure                  Test_method Group    P.unadj      P.adj Significance
1 IW - CW - TW Observed Kruskal-Wallis Rank Sum Test    IW 0.15504659 0.27908386           ns
2 IW - CW - TW    Chao1 Kruskal-Wallis Rank Sum Test    IW 0.01696123 0.05088368           ns
3 IW - CW - TW      ACE Kruskal-Wallis Rank Sum Test    IW 0.01333436 0.05088368           ns
4 IW - CW - TW  Shannon Kruskal-Wallis Rank Sum Test    IW 0.53187046 0.79780569           ns
5 IW - CW - TW  Simpson Kruskal-Wallis Rank Sum Test    CW 0.80832094 0.90936106           ns

> t1$cal_diff(method = "anova")
Perform post hoc test with the method: duncan.test ...
The result is stored in object$res_diff ...
> t1$res_diff
      Measure Test_method Group Letter
1    Observed       anova    IW      a
2    Observed       anova    TW      a
3    Observed       anova    CW      a
4       Chao1       anova    IW      a
5       Chao1       anova    TW     ab
6       Chao1       anova    CW      b
7         ACE       anova    IW      a
8         ACE       anova    TW      b
9         ACE       anova    CW      b
10    Shannon       anova    IW      a
11    Shannon       anova    TW      a
12    Shannon       anova    CW      a
13    Simpson       anova    IW      a
14    Simpson       anova    TW      a
15    Simpson       anova    CW      a
16 InvSimpson       anova    IW      a
17 InvSimpson       anova    TW      a
18 InvSimpson       anova    CW      a
19     Fisher       anova    IW      a
20     Fisher       anova    TW      a
21     Fisher       anova    CW      a
22     Pielou       anova    IW      a
23     Pielou       anova    TW      a
24     Pielou       anova    CW      a
25   Coverage       anova    CW      a
26   Coverage       anova    TW      a
27   Coverage       anova    IW      b

#现在，让我们绘制每个组的alpha多样性的平均值和se，并添加duncan。
> t1$plot_alpha(add_letter = T, measure = "Chao1", use_boxplot = FALSE)

> t1$plot_alpha(pair_compare = F, measure = "Chao1", shape = "Group")

今天，我发现一个很尴尬的问题，就是这个包更新了，兄弟以前的代码不能直接跑了。我尽量把它跑完。这些都是我跑完之后分享的，大家可以先跑。