A workflow for identifying enterotypes based on the relative abundance of gut microbiota was implemented refereed on the reports of Arumugam[^2]
library(mbOmic)
library(data.table)First of all, the dataset of microbiota relative abundance was retrived from the enterotypes weblink. The missing value was imputed using KNN by impute package.
dat <- read.delim('http://enterotypes.org/ref_samples_abundance_MetaHIT.txt')
dat <- impute::impute.knn(as.matrix(dat), k = 100)
dat <- as.data.frame(dat$data+0.001)
setDT(dat, keep.rownames = TRUE)
datConstructe the bSet class and then estimate the the proper cluster number using the estimate_k function. The estimate_k function take advantage of Jensen-Shannon divergence to cluster the samples and the number of clusters was optimizated by Calinski-Harabasz (CH) Index and Silhouette Coefficient.
The estimate_k returns verCHI class, a S3 class containing a optimal cluster results, optimal number cluster, a minmum CHI, a minmum Silhouette value, and Jensen-Shannon divergence matrix.
dat <- bSet(b = dat)
res <- estimate_k(dat)
res
#> optimal number of cluster: 4
#> Max CHI: 164.642158008611
#> Silhouette: 0.181445495999067
The proper number of cluster is 4.
Next, the enterotyping function was used to identify the enterotype for each cluster and it returns a 3-length list. This list contains two enterotypes matrices and a unidentified samples vector. Cluster 2, 3, and 4 was enterotype Bacteroides, Prevotella, and Ruminococcus, resepectively.
ret=enterotyping(dat, res$verOptCluster)
ret
#> $enterotypes
#> Enterotype max which cluster
#> 1: Bacteroides 0.36724946 2 cluster 2
#> 2: Prevotella 0.29692944 3 cluster 3
#> 3: Ruminococcus 0.02416713 4 cluster 4
#>
#> $data
#> Samples Enterotype cluster
#> 1: MH0087 Bacteroides cluster 2
#> 2: MH0156 Bacteroides cluster 2
#> 3: MH0444 Bacteroides cluster 2
#> 4: MH0333 Bacteroides cluster 2
#> 5: MH0233 Bacteroides cluster 2
#> ---
#> 234: MH0012 Ruminococcus cluster 4
#> 235: MH0415 Ruminococcus cluster 4
#> 236: MH0457 Ruminococcus cluster 4
#> 237: MH0442 Ruminococcus cluster 4
#> 238: MH0448 Ruminococcus cluster 4
#>
#> $UnIdentifiedSamples
#> [1] "MH0277" "MH0161" "MH0046" "MH0175" "MH0152" "MH0104" "MH0151" "MH0189"
#> [9] "MH0030" "MH0157" "MH0063" "MH0075" "MH0141" "MH0169" "MH0050" "MH0286"
#> [17] "MH0096" "MH0053" "MH0217" "MH0098" "MH0009" "MH0197" "MH0065" "MH0173"
#> [25] "MH0168" "MH0070" "MH0077" "MH0288" "MH0200" "MH0031" "MH0183" "MH0132"
#> [33] "MH0144" "MH0124" "MH0430" "MH0276" "MH0407" "MH0428" "MH0126" "MH0447"Furthermore, this result was validated by enterotypes results given by the enterotype website.
enterotypes <- read.table(system.file('extdata', 'enterotype.txt', package = 'mbOmic'))
enterotypes <- enterotypes[samples(dat),]
table(res$verOptCluster, enterotypes$ET)
#>
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#> 2 67 5 0
#> 3 0 0 40
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