R语言完整的Lefse分析写在前面怎么说呢，我在之前的推送中，在R语言中实现了LEFse分析过程。但是我没有将数据前处理和结果的物种分类图形做出来。这是一定要在R语言中操作的，要不然，仅仅是实现了lefse算法，也是没有什么作用。 所以这篇贴子主要的工作在于： 注意： 物种注释不同数据库格式不同，如果你的注释文件同参考不同，转化为和示例文件相同的格式，方可进行后续操作。 所需R包library("tidyverse")
library(microbiomeViz)
library(ggtree)
library(tidyverse)
library(phyloseq)
##--功能函数#---------
# 功能函数
### 提取OTU表格
vegan_otu <- function(physeq){
OTU <- otu_table(physeq)
if(taxa_are_rows(OTU)){
OTU <- t(OTU)
}
return(as(OTU,"matrix"))
}
### 添加OTU注释信息
vegan_tax <- function(physeq){
tax <- tax_table(physeq)
return(as(tax,"matrix"))
}整理LEFSE分析的数据这里如果大家熟悉phyloseq对象，直接用这个phyloseq对象就是了 #导入数据
ps = readRDS("../../data/ps_liu.rds")如果不熟悉phyloseq可以使用普通数据导入# 导入otu表格
otu = read.delim("../../data/otutab.txt",row.names = 1)
head(otu)
#导入注释文件
tax = read.delim("../..//data/taxonomy.txt",row.names = 1)
head(tax)
#导入分组文件
map = read.delim("../../data/metadata.tsv",row.names = 1)
head(map)
# head(otu)
otu = as.matrix(otu)
str(otu)
tax = as.matrix(tax)
# taxa_names(tax)
ps <- phyloseq(otu_table(otu, taxa_are_rows=TRUE),
sample_data(map) ,
tax_table(tax)
# phy_tree(tree)
)没有不要用全部OTU，我按照序列数过滤，当然可以不过滤# otu数量很多，所以选择一部分展示，一般树展示200个作用最为合适
ps = filter_taxa(ps, function(x) sum(x ) > 400 , TRUE)
ps提取注释文件这一步是我我们准备Lefse物种注释表格整理的开始。 #-提取otu表和tax表格#--------
otu_table = as.data.frame(t(vegan_otu(ps )))
tax_table = (vegan_tax(ps ))整理物种注释文件首先去除未注释出来的物种，这里为：Unassigned。 其次将物种不同级别之间的分隔符号修改为|。 再有将每一级的名称都加上前面级别的全部注释名称，形成结构。 最后按照不同级别数据合并otu表格，然后将注释文件和otu文件合并。 初步整理，去除Unassignedtax_table[tax_table == "Unassigned"] = NA
tax_table = as.data.frame(
tax_table
)物种数据再整理其次将物种不同级别之间的分隔符号修改为|。 # design = as.data.frame(sample_data(ps))
#合并otu表格和tax表格#---------
otu_tax = merge(otu_table,tax_table,by = "row.names",all = F)
# head(otu_tax)
#--------添加门类标记，未注释的结果去掉#----
otu_tax$Kingdom = paste("k",otu_tax$Kingdom,sep = "__")
otu_tax$Kingdom[otu_tax$Kingdom == "k__NA"] =""
otu_tax$Phylum = paste("|p",otu_tax$Phylum,sep = "__")
otu_tax$Phylum[otu_tax$Phylum == "|p__NA"] = ""
otu_tax$Class = paste("|c",otu_tax$Class,sep = "__")
otu_tax$Class[otu_tax$Class == "|c__NA"] = ""
otu_tax$Order = paste("|o",otu_tax$Order,sep = "__")
otu_tax$Order[otu_tax$Order == "|o__NA"] = ""
otu_tax$Family = paste("|f",otu_tax$Family,sep = "__")
otu_tax$Family [otu_tax$Family == "|f__NA"] = ""
otu_tax$Genus = paste("|g",otu_tax$Genus,sep = "__")
otu_tax$Genus[ otu_tax$Genus == "|g__NA"] = ""
otu_tax$Species = paste("|s",otu_tax$Species,sep = "__")
otu_tax$Species[otu_tax$Species == "|s__NA"] = ""
otu_tax$Row.names = paste("|t",otu_tax$Row.names,sep = "__")
#合并得到结合全部门类的OTU名称#----
OTU_name = paste(otu_tax$Kingdom,otu_tax$Phylum,otu_tax$Class,otu_tax$Order,otu_tax$Family,
otu_tax$Genus,sep = "")再整理再有将每一级的名称都加上前面级别的全部注释名称，形成结构。这一步，我们去除了一些特殊的符号。主要是怕影响美观 #----不同等级注释修改#----
otu_tax$Phylum=paste(otu_tax$Kingdom,otu_tax$Phylum,sep = "")
otu_tax$Class=paste(otu_tax$Phylum,otu_tax$Class,sep = "")
otu_tax$Order=paste(otu_tax$Class,otu_tax$Order,sep = "")
otu_tax$Family=paste(otu_tax$Order,otu_tax$Family,sep = "")
otu_tax$Genus=paste(otu_tax$Family,otu_tax$Genus,sep = "")
otu_tax$Species=paste(otu_tax$Genus,otu_tax$Species,sep = "")
#替换两个括号等特殊符号#--------
library("tidyverse")
OTU_name = str_replace(OTU_name,"[(]","")
OTU_name = str_replace(OTU_name,"[)]","")
# as.character(OTU_name )
# OTU_name = gsub("(","",OTU_name[311])
# row.names(otu_tax) = OTU_name
# #将otu表格和tax文件分离#-----
# otu_table = otu_tax[2:(ncol(otu_table)+1)]
# tax_table = otu_tax[(ncol(otu_table)+2):(ncol(otu_table)+2+6)]
#再整理最后按照不同级别数据合并otu表格，然后将注释文件和otu文件合并。
#-------将不同分类登记的也添加上去
HA = otu_table
# 按Kingdom合并
grp <- otu_tax[rownames(otu_tax), "Kingdom", drop=F]
merge=cbind(HA, grp)
HA_Kingdom = merge %>% group_by(Kingdom) %>% summarise_all(sum)
colnames(HA_Kingdom)[1]="Class"
# 按Phylum合并
grp <- otu_tax[rownames(otu_tax), "Phylum", drop=F]
merge=cbind(HA, grp)
HA_Phylum = merge %>% group_by(Phylum) %>% summarise_all(sum)
colnames(HA_Phylum)[1]="Class"
# 按Class合并
grp <- otu_tax[rownames(otu_tax), "Class", drop=F]
merge=cbind(HA, grp)
HA_Class = merge %>% group_by(Class) %>% summarise_all(sum)
colnames(HA_Class)[1]="Class"
# 按Order合并
grp <- otu_tax[rownames(otu_tax), "Order", drop=F]
merge=cbind(HA, grp)
HA_Order = merge %>% group_by(Order) %>% summarise_all(sum)
colnames(HA_Order)[1]="Class"
# 按Family合并
grp <- otu_tax[rownames(otu_tax), "Family", drop=F]
merge=cbind(HA, grp)
HA_Family = merge %>% group_by(Family) %>% summarise_all(sum)
colnames(HA_Family)[1]="Class"
# 按Genus合并
grp <- otu_tax[rownames(otu_tax), "Genus", drop=F]
merge=cbind(HA, grp)
HA_Genus = merge %>% group_by(Genus) %>% summarise_all(sum)
colnames(HA_Genus)[1]="Class"
# colnames(otu_tax)
# # 按Species合并
# grp <- otu_tax[rownames(otu_tax), "Species", drop=F]
# merge=cbind(HA, grp)
# HA_Species = merge %>% group_by(Species) %>% summarise_all(sum)
# colnames(HA_Species)[1]="Class"
## OTU水平
# merge=cbind(HA, OTU_name)
# head(otu_table)
# HA_OTU = otu_table
# HA_OTU = data.frame(Class = row.names(otu_table),otu_table)
# colnames(HA_OTU )
# head(HA_OTU)
# 合并6个分类级
all = rbind(HA_Kingdom, HA_Phylum, HA_Class, HA_Order, HA_Family, HA_Genus)
dim(all)
# head(OTU_name)因为我们去除了未注释出来的，所以会有重复这里只需要简单去除就可以了。我们还构建了phyloseq对象，也就是说基于这个群落我们做其他分析也是是十分方便。 # 去除重复
all = distinct(all, Class, .keep_all = TRUE)
dim(all)
#-------------lefse分析构建
all1 = all
row.names(all1) = all1$Class
all1$Class = NULL
all1 = as.matrix(all1)
# head(all1)
#-构建phylose对象
ps_G_graphlan = phyloseq(otu_table(as.matrix(all1),taxa_are_rows = TRUE),
sample_data(ps))
ps_G_graphlan
#----提取OTU表格
otu_table = as.data.frame((vegan_otu(ps_G_graphlan)))
otu_table[otu_table==0] <- 1
# row.names(otu_table)
# head(design)
design = as.data.frame(sample_data(ps_G_graphlan))如果我们要使用功能python那个脚本运行，保存就可以使用以下代码这里我就不做运行了，之前的帖子相信大家都看过了，我是做了对比的，发现算法都是一致。 # filename = paste(lefsepath,"/LEFSE_to_run_G_level.txt",sep = "")
# write.table(otu_table, filename,append = F, quote = F,col.names= F,sep = "\t")
#文件预处理
# format_input.py LEFSE_to_run_G_level.txt pri_lefse.in -c 1 -u 2 -o 1000000
# ~/src/nsegata-lefse/run_lefse.py pri_lefse.in pri_lefse_2.res -l 4
# plot_res.py pri_lefse_2.res lefse_barplot.pdf --format pdf
# plot_cladogram.py pri_lefse_2.res lefse_tree.pdf --format pdf
# # 注意这里 –c用来指定分组信息-u 1指定样品信息
# #文件分析,这里-l设置LDA阈值，默认为2，我们使用4 会更加严格
# ~/src/nsegata-lefse/run_lefse.py pri_lefse.in pri_lefse_2.res -l 4
# #柱状图绘制
# plot_res.py pri_lefse_2.res lefse_barplot.pdf --format pdf
# #树状图绘制
# plot_cladogram.py pri_lefse_2.res lefse_tree.pdf --format pdf
# #做每个差异的柱状图
# mkdir biomarkers_raw_images
## plot_features.py pri_lefse.in pri_lefse_2.res biomarkers_raw_images/R语言lefse分析这部分如果大家不懂得原理建议查看之前的推送，基于R语言实现Lefse分析。 #-------------R语言进行lefse分析
# ps_sub <- subset_samples(ps_G_graphlan,Group %in% c("July-Orchard","July-cropland"));ps_sub
ps_sub = ps_G_graphlan
otu_table = as.data.frame((vegan_otu(ps_sub)))
otu_table[otu_table==0] <- 1
# head(otu_table)
map = as.data.frame(sample_data(ps_sub))
otu = (otu_table)
claslbl= map$Group
set.seed(56290);
#KW rank sum test
dat3t = otu_table
# head(otu)
# dat3t = as.matrix(dat3t)
# str(dat3t)
rawpvalues <- apply(dat3t, 2, function(x) kruskal.test(x, claslbl)$p.value);
#--得到计算后得到的p值
ord.inx <- order(rawpvalues);
rawpvalues <- rawpvalues[ord.inx];
clapvalues <- p.adjust(rawpvalues, method ="fdr");
# p.adjust
dat3t <- dat3t[,ord.inx];
dim(dat3t)
wil_datadf <- as.data.frame(dat3t);
# head(wil_datadf)
#if no subclass within classes then no wilcoxon rank sum test
#Linear Discriminant analysis (LDA)
library( MASS)
ldares <- lda(claslbl~ .,data = wil_datadf);
# ldares
ldamean <- as.data.frame(t(ldares$means));
# ldamean
class_no <<- length(unique(claslbl));
ldamean$max <- apply(ldamean[,1:class_no],1,max);
ldamean$min <- apply(ldamean[,1:class_no],1,min);
#---计算LDA
ldamean$LDAscore <- signif(log10(1+abs(ldamean$max-ldamean$min)/2),digits=3);
# head(ldamean)
a = rep("A",length(ldamean$max))
i = 1
for (i in 1:length(ldamean$max)) {
name =colnames(ldamean[,1:class_no])
a[i] = name[ldamean[,1:class_no][i,] %in% ldamean$max[i]]
}
ldamean$class = a
# head(ldamean)
ldamean$Pvalues <- signif(rawpvalues,digits=5);
ldamean$FDR <- signif(clapvalues,digits=5);
resTable <- ldamean;
# head(ldamean)
# it seems lda add ` around names containing dash "-", need to strip this off
rawNms <- rownames(resTable);
rownames(resTable) <- gsub("`", '', rawNms);
# head(resTable)
# if(pvalOpt == "raw"){
# de.Num <- sum(rawpvalues<=p.lvl & ldamean$LDAscore>=lda.lvl)
# }else{
p.lvl =0.05
lda.lvl = 2
de.Num <- sum(clapvalues<=p.lvl & ldamean$LDAscore>=lda.lvl)
# }
if(de.Num == 0){
current.msg <<- "No significant features were identified with given criteria.";
}else{
current.msg <<- paste("A total of", de.Num, "significant features with given criteria.")
}
current.msg
# sort by p value
ord.inx <- order(resTable$Pvalues, resTable$LDAscore);
resTable <- resTable[ord.inx, ,drop=FALSE];
#p-values column to appear first; then FDR and then others
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))];
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))];
write.csv(resTable,"cs.csv")R语言绘制物种分类树这部分我首先进行了颜色的映射数据整理，然后使用microbiomeviz做了圈图的输出。 taxtree = resTable[clapvalues<=p.lvl & ldamean$LDAscore>=lda.lvl,]
delimiter = "\\|"
tax_split <- strsplit(row.names(taxtree), delimiter)
row.names(taxtree)<- vapply(tax_split, tail, n = 1, "")
# head(taxtree)
#-提取所需要的颜色
colour = c('darkgreen','red',"blue")
selececol = colour[1:length(levels(as.factor(taxtree$class)))]
names(selececol) = levels(as.factor(taxtree$class))
A = rep("a",length(row.names(taxtree)))
i = 1
for (i in 1:length(row.names(taxtree))) {
A[i] = selececol [taxtree$class[i]]
}
# A
lefse_lists = data.frame(node=row.names(taxtree),
color=A,
stringsAsFactors = FALSE
)
# str(all)
## 计算均值用于呈现结点大小
dat <- data.frame(V1=all[,1], V2=rowMeans(all[,-1]), stringsAsFactors = FALSE)
# head(dat)
# dim(dat)
# write.csv(dat,"./tree_tax.csv")
# dat$V2 = NULL
# 用物种和丰度生成树骨架
tr <- parseMetaphlanTSV(dat, node.size.offset=2, node.size.scale=0.8)
# tr
# 构造树#------
p =tree.backbone(tr, size=1,layout= 'circular')
p
# ?tree.backbone
# 注释树
p <- clade.anno(p, lefse_lists, alpha=0.3)
p
ggsave("./cs.pdf",p,width = 10,height = 10)
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