Journal: Bioinformatics
Year: 2017
Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870771/
Rarefaction toolkit (RTK)是一个针对大数据做稀释曲线的工具，有C 和R两种版本。是对QIIME和Mothur有效的补充。
现在被引用了10次，引用它的还都是不错的文章。
RTK除了可以做稀释曲线，还能计算Pielou’s evenness, chao 1, Shannon,Simpson等多样性指数。
结果表明其内存占用与运行时间都优于QIIME、Mothur及R中的vegan包。
install.packages("rtk")
library(rtk)
#基本用法：
#rtk(input, repeats = 10, depth = 1000, ReturnMatrix = 1, margin = 2,
# verbose = FALSE, threads = 1, tmpdir = NULL )
#input可以是一个矩阵或路径
#repeats重复计算次数，默认10次
#depth重抽的深度，默认1000
#ReturnMatrix结果返回的矩阵数量，默认1
#margin：=1按行计算；=2按列计算
#verbose运行过程是否输出
#tmpdir临时文件储存位置
data <- matrix(sample(x = c(rep(0, 15000),rep(1:10, 100)),
size = 10000, replace = TRUE), ncol = 80)
data.r <- rtk(data, ReturnMatrix = 1, depth = min(colSums(data)))
#结果包含richness，evenness, chao 1, Shannon, Simpson，及稀释曲线的结果。
#collectors.curve画物种积累曲线
collectors.curve(data.r, xlab = "No. of samples (rarefied data)", ylab = "richness")
#分组上色
cls <- rep_len(c("a","b","c","d"), ncol(data)) # study origin of each sample
accumOrder <- c("b","a","d","c") # define the order, for the plot
colors <- c(1,2,3,4)
names(colors) <- accumOrder # names used for legend
collectors.curve(data, xlab = "No. of samples",
ylab = "richness", col = colors, bin = 1,cls = cls,
accumOrder = accumOrder)
####多样性曲线
data <- matrix(sample(x = c(rep(0, 1500),rep(1:10, 500),1:1000),
size = 120, replace = TRUE), 40)
samplesize <- min(colSums(data))
d1 <- rtk(input = data, depth = samplesize)
# 多个重抽深度
d2 <- rtk(input = data, depth = round(seq(1, samplesize, length.out = 10)))
# richness 曲线plot(d1, div = "richness")# richness与稀释曲线拟合
plot(d2, div = "eveness", fit = "arrhenius", pch = c(1,2,3))
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