Hilldiv: 便捷的计算及可视化Hill number

>install.packages("devtools")
>library(devtools)
>install_github("anttonalberdi/hilldiv")
>library(hilldiv)
#依赖的重要的包: ggplot2, RColorBrewer, data.table, ape, ade4, iNEXT, iNextPD.
#ggplot2:可视化
#RColorBrewer #调色板，用于可视化
#ape #系统发育分析
#ade4 #生态和环境领域广泛使用
#data.table:数据清洗
#iNEXT和iNextPD:计算Hill number

>data(bat.diet.otutable) #OTU
>data(bat.diet.hierarchy) #分组文件
>data(bat.diet.tree)  #有根树

#Create simple objects
>otu.table <- bat.diet.otutable
>otu.vector <- bat.diet.otutable[,1]
>names(otu.vector) <- rownames(otu.table)
>hierarchy.table <- bat.diet.hierarchy
>tree <- bat.diet.tree

#Usage
#true.div(abund,qvalue,tree,type)
#qvalue:hill的阶数。q>=0
#type:abundance或者incidence
>hill.div(otu.table,1)
    Msc1     Msc2     Msc3     Msc4     Msc5     Mmy1     Mmy2     Mmy3     Mmy4 
1.505545 3.122265 2.012664 1.000000 3.238900 2.394539 1.410046 2.276801 2.000301 
    Mmy5     Rme1     Rme2     Rme3     Rme4     Rme5     Mda1     Mda2     Mda3 
1.006834 1.008112 1.000000 1.156695 1.081390 2.173392 1.009006 2.987138 4.576420 
    Mda4     Mda5     Mca1     Mca2     Mca3     Mca4     Mca5     Reu1     Reu2 
3.528198 1.237994 2.503107 3.204414 1.118893 3.472679 1.010790 2.079217 1.905770 
    Reu3     Reu4     Reu5     Mem1     Mem2     Mem3     Mem4     Mem5     Rhi1 
2.994990 1.435739 1.496800 4.265005 1.159175 7.853560 2.426102 3.156563 4.838018 
    Rhi2     Rhi3     Rhi4     Rhi5 
2.331697 1.414016 3.972967 5.138890 

>hill.div(otu.table,1,tree)
    Msc1     Msc2     Msc3     Msc4     Msc5     Mmy1     Mmy2     Mmy3     Mmy4 
1.152278 1.199440 1.071783 1.000000 1.120999 1.202859 1.011519 1.237840 1.223297 
    Mmy5     Rme1     Rme2     Rme3     Rme4     Rme5     Mda1     Mda2     Mda3 
1.001831 1.000436 1.000000 1.017877 1.006787 1.058370 1.003925 1.265173 1.669337 
    Mda4     Mda5     Mca1     Mca2     Mca3     Mca4     Mca5     Reu1     Reu2 
1.475675 1.077681 1.250052 1.401592 1.030458 1.174893 1.002285 1.098038 1.048106 
    Reu3     Reu4     Reu5     Mem1     Mem2     Mem3     Mem4     Mem5     Rhi1 
1.111219 1.224831 1.050408 1.220784 1.013132 1.573986 1.265680 1.368388 1.253371 
    Rhi2     Rhi3     Rhi4     Rhi5 
1.174519 1.077542 1.333563 1.390569 

>hill.div(otu.table,1,tree,type="incidence")
[1] 2.994891

#Usage
#div.profile(abund,qvalues,tree,values,hierarchy,level,colour,log)
#qvalue: default from 0 to 5. order=seq(from = 0, to = 5, by = (0.1))
#level: alpha或gamma。默认为gamma多样性。
#log:Hill是否取对数
# One sample
>div.profile(otu.vector)# Multiple individual samples (first 5 samples of the OTU table)
>div.profile(otu.table[,c(1:5)])

#Usage
#div.test(otutable,qvalue,hierarchy,tree)
# 先进行Shapiro and Barlett tests检验数据的正态性和均质性；再根据结果选择显著性检验的方法。
#两组：Student’s t-test or a Wilcoxon Rank Sum Test
#多组：ANOVA or a Kruskal-Wallis test

#Contrast based on Hill numbers
>div.test(otu.table,qvalue=0,hierarchy=hierarchy.table)
$normality.pvalue
[1] 0.06082876

$homogeneity.pvalue
[1] 0.5009248

$groups
[1] 8

$method
[1] "ANOVA"

$test
            Df Sum Sq Mean Sq F value Pr(>F)  
Group        7  598.7   85.53    2.55 0.0332 *
Residuals   32 1073.2   33.54                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Usage
#div.test.plot(divtest,chart,colour)
#chart 可选择 boxplot 或 jitter 或 violin plot
>contrast.div.q0 <- div.test(otu.table,qvalue=0,hierarchy=hierarchy.table)
>colours <- c("#35a849","#9d1923","#f7ab1b","#ed7125","#cc4323","#b6d134","#fcee21","#085ba7")

#Box plot
>div.test.plot(contrast.div.q0,chart="box")
#Jitter plot
>div.test.plot(contrast.div.q0,chart="jitter",colour=colours)
#Violin plot
>div.test.plot(contrast.div.q0,chart="violin",colour=c("#35a849","#9d1923","#f7ab1b","#ed7125","#cc4323","#b6d134","#fcee21","#085ba7"))

#Usage
#div.part(otutable,qvalue,hierarchy,tree,type)

#Abundance-based
>div.part(otu.table,qvalue=1)
$Hierarchical_levels
[1] 2

$Type
[1] "abundance"

$Order_diversity
[1] 1

$Sample_size
[1] 40

$L1_diversity
[1] 2.087489

$L2_diversity
[1] 61.96846

$Beta_diversity
[1] 29.68564

#Usage
#beta.dis(beta,qvalue,N,metric,type)
#N:样本数
#matrix: "C", "U", "V" or "S". 
#type:"similarity" or "dissimilarity".

>beta.dis(beta=4.5,qvalue=1,N=8,metric="C",type="similarity")
[1] 0.2766937

#Usage
#pair.dis(otutable,qvalue,tree,hierarchy,level,measure)
#level:1 or 2.1表示在所有样本之间计算不相似性；2表示在组间计算不相似性。默认1
>pair.dis(otu.table,qvalue=0,hierarchy=hierarchy.table)
#结果会给出"C", "U", "V"，"S"这几种所有的不相似性。

#Usage
#pair.dis.plot(distance,hierarchy,type,level,colour,magnify)
#NMDS chart, a qgraph plot or a heatmap/correlogram.
#type:NMDS or qgraph 

>pair.div.q0.L2 <- pair.dis(otu.table[,sort(colnames(otu.table))],qvalue=0,hierarchy=hierarchy.table,level="2")
#Plot NMDS
>pair.dis.plot(pair.div.q0.L2$L2_CqN,hierarchy=hierarchy.table,type="NMDS",level=2)
#Plot qgraph
>pair.dis.plot(pair.div.q0.L2$L2_CqN,hierarchy=hierarchy.table,type="qgraph",level=2,magnify=TRUE)

#Usage
#alpha.div(otutable,qvalue,weight)
>alpha.div(otutable=otu.table,qvalue=0)
[1] 10.45