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enterotype.R
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library(tidyverse)
library(cluster)
library(clusterSim)
library(ade4)
# Linking long-term dietary patterns with gut microbial enterotypes
# Dirichlet multinomial mixtures: generative models for microbial metagenomics
# https://enterotype.embl.de/enterotypes.html
# https://github.com/abikoushi/enigma
m3 <- read_delim('/Users/congliu/prs/mv34/HZKS01_1148_mv34_otu_qiime_silva99v2_m3_std.txt', delim = '\t') %>%
dplyr::select(-taxonomy) %>% filter(str_detect(`OTU ID`, 'g__'))
OTU_ID <- dplyr::select(m3, `OTU ID`)
foo <- m3 %>% dplyr::select(-`OTU ID`) %>% mutate_each(function(x){x/sum(x)})
bar <- bind_cols(OTU_ID, foo) %>%
extract(`OTU ID`, into = 'OTU_ID', regex = 'g__([A-Z].*)')
write_delim(bar, '/Users/congliu/prs/hbrm_Alveoli/genus.txt', delim = '\t')
# 11年文献 -------------------------------------------------------------------
#df <- read.table("/Users/congliu/prs/enterotype/MetaHIT_SangerSamples.genus.txt", header=T, row.names=1, dec=".", sep="\t")
#df <- df[-1, ]
df <- read.table('/Users/congliu/prs/hbrm_Alveoli/genus.txt', header = T, row.names = 1, sep = '\t', dec = ".")
noise.removal <- function(dataframe, percent=0.01, top=NULL){
dataframe->Matrix
bigones <- rowSums(Matrix)*100/(sum(rowSums(Matrix))) > percent
Matrix_1 <- Matrix[bigones,]
print(percent)
return(Matrix_1)
}
#df <- noise.removal(df, percent=0.01)
dist.JSD <- function(inMatrix, pseudocount=0.000001, ...) {
KLD <- function(x,y) sum(x *log(x/y))
JSD<- function(x,y) sqrt(0.5 * KLD(x, (x+y)/2) + 0.5 * KLD(y, (x+y)/2))
matrixColSize <- length(colnames(inMatrix))
matrixRowSize <- length(rownames(inMatrix))
colnames <- colnames(inMatrix)
resultsMatrix <- matrix(0, matrixColSize, matrixColSize)
inMatrix = apply(inMatrix,1:2,function(x) ifelse (x==0,pseudocount,x))
for(i in 1:matrixColSize) {
for(j in 1:matrixColSize) {
resultsMatrix[i,j]=JSD(as.vector(inMatrix[,i]),
as.vector(inMatrix[,j]))
}
}
colnames -> colnames(resultsMatrix) -> rownames(resultsMatrix)
as.dist(resultsMatrix)->resultsMatrix
attr(resultsMatrix, "method") <- "dist"
return(resultsMatrix)
}
data.dist=dist.JSD(df)
pam.clustering=function(x,k) { # x is a distance matrix and k the number of clusters
require(cluster)
cluster = as.vector(pam(as.dist(x), k, diss=TRUE)$clustering)
return(cluster)
}
#data.cluster=pam.clustering(data.dist, k=3)
#确定最优的k，Calinski-Harabasz index
#nclusters = index.G1(t(df), data.cluster, d = data.dist, centrotypes = "medoids")
nclusters=NULL
for (k in 1:20) {
if (k==1) {
nclusters[k]=NA
} else {
data.cluster_temp=pam.clustering(data.dist, k)
nclusters[k]=index.G1(t(df),data.cluster_temp, d = data.dist,
centrotypes = "medoids")
}
}
plot(nclusters, type="h", xlab="k clusters", ylab="CH index")
data.cluster=pam.clustering(data.dist, k=3)
obs.silhouette=mean(silhouette(data.cluster, data.dist)[,3])
obs.silhouette
obs.pcoa=dudi.pco(data.dist, scannf=F, nf=3)
s.class(obs.pcoa$li, fac=as.factor(data.cluster), grid=F, cell=0, cstar=0, col=c(3,2,4))
###BCA 验证聚类情况 对于短序列测序情况 推荐去除
noise.removal <- function(dataframe, percent=0.01, top=NULL){
dataframe->Matrix
bigones <- rowSums(Matrix)*100/(sum(rowSums(Matrix))) > percent
Matrix_1 <- Matrix[bigones,]
print(percent)
return(Matrix_1)
}
#data.denoized=noise.removal(df, percent=0.01)
obs.pca=dudi.pca(data.frame(t(df)), scannf=F, nf=10)
obs.bet=bca(obs.pca, fac=as.factor(data.cluster), scannf=F, nf=k-1)
#s.class(obs.bet$ls, fac=as.factor(data.cluster), grid=F)
s.class(obs.bet$ls, fac=as.factor(data.cluster), grid=F, cell=0, cstar=0, col=c(4,2,3))
# 对比 ----------------------------------------------------------------------
foo <- pam(as.dist(data.dist), k=3, diss=TRUE)
summary(foo)
sample_type <- as.data.frame(foo$clustering)
get_top10 <- function(tax){
sample_type_1 <- subset(sample_type, foo$clustering==tax)
df_1 <- df[rownames(sample_type_1)]
df_1['total'] <- rowSums(df_1)
bar <- df_1[order(df_1$total, decreasing = T), ]
df_top10 <- subset(bar[1:10, ], select = total)
return(df_top10)
}
get_top10(1)
get_top10_m3 <- function(tax){
sample_type_1 <- subset(sample_type, foo$clustering==tax)
df_1 <- m3[rownames(sample_type_1)]
df_1['total'] <- rowSums(df_1)
df_1 <- bind_cols(dplyr::select(m3, `OTU ID`), df_1)
bar <- df_1[order(df_1$total, decreasing = T), ]
df_top10 <- subset(bar[1:10, ], select = c(`OTU ID`,total))
df_last <- subset(bar[11:nrow(bar),], select = c(`OTU ID`,total))
df.final <- rbind(df_top10, c("Others", colSums(df_last[,2:length(df_last)])))
df.final <- df.final %>% mutate_at(vars(total), as.double) %>%
mutate_at(vars(total), funs(./sum(.)))
return(df.final)
}
get_top10_m3(1)
define_enterotype <- function(m3){
m3_rate <- m3 %>% arrange(desc(PRS003170019)) %>% mutate_if(is.double, ~(./sum(.))) %>%
rename(OTU_ID=`OTU ID`)
for (i in colnames(m3_rate)[2:length(m3_rate)]) {
one_sample <- m3_rate[c('OTU_ID', i)]
B <- filter(one_sample, OTU_ID == 'g__Bacteroides')[[1,2]]
P <- filter(one_sample, OTU_ID == 'g__Prevotella')[[1,2]]
if((B > 0.4) && (B > P)){
print(paste(i, 'TypeB', sep = '\t'))
cat(paste(i, 'TypeB', sep = '\t'), file = "/Users/congliu/prs/hbrm_Alveoli/enterotype.txt", sep='\n',append = TRUE)
} else if ((P >= 0.3) && (P >= B)){
print(paste(i, 'TypeP', sep = '\t'))
cat(paste(i, 'TypeP', sep = '\t'), file = "/Users/congliu/prs/hbrm_Alveoli/enterotype.txt", sep='\n',append = TRUE)
} else {
print(paste(i, 'TypeIII', sep = '\t'))
cat(paste(i, 'TypeIII', sep = '\t'), file = "/Users/congliu/prs/hbrm_Alveoli/enterotype.txt", sep='\n',append = TRUE)
}
}
}
define_enterotype(m3)
define_enterotype_2 <- function(m3){
m3_rate <- m3 %>% arrange(desc(PRS003170019)) %>% mutate_if(is.double, ~(./sum(.))) %>%
rename(OTU_ID=`OTU ID`)
for (i in colnames(m3_rate)[2:length(m3_rate)]) {
one_sample <- m3_rate[c('OTU_ID', i)]
B <- filter(one_sample, OTU_ID == 'g__Bacteroides')[[1,2]]
P <- filter(one_sample, OTU_ID == 'g__Prevotella')[[1,2]]
if((B > 0.2) && (B > P)){
print(paste(i, 'TypeB', sep = '\t'))
cat(paste(i, 'TypeB', sep = '\t'), file = "/Users/congliu/prs/hbrm_Alveoli/enterotype_2.txt", sep='\n',append = TRUE)
} else if ((P >= 0.3) && (P >= B)){
print(paste(i, 'TypeP', sep = '\t'))
cat(paste(i, 'TypeP', sep = '\t'), file = "/Users/congliu/prs/hbrm_Alveoli/enterotype_2.txt", sep='\n',append = TRUE)
} else {
print(paste(i, 'TypeIII', sep = '\t'))
cat(paste(i, 'TypeIII', sep = '\t'), file = "/Users/congliu/prs/hbrm_Alveoli/enterotype_2.txt", sep='\n',append = TRUE)
}
}
}
define_enterotype_2(m3)
# 18年文献 -------------------------------------------------------------------