如何用R語言做差異代謝物的kegg富集分析

編寫：王采荷

感覺已經(jīng)很久沒有在簡書上分享自己的東西了，今天在集合其他大佬的力量終于把“如何用R語言做差異代謝物的kegg富集分析”這個問題給解決了。我決定將這個過程分享到簡書上，一來可以給在這方面有需求的其他朋友一個參考；二來如果有錯誤的地方也能得到大佬的指點。這篇短文主要分為以下三個部分組成；

一、將代謝物及對應的KEGG數(shù)據(jù)庫信息進行下載并整理

  • 數(shù)據(jù)下載
# 加載所需要的R包
rm(list = ls())
library(XML)
library(RCurl)
library(tidyverse)
library(ggplot2)
library(magrittr)
library(clusterProfiler)
#' 第一步下載KEGG數(shù)據(jù)庫信息
extractCompounds <- function(pathwayId) {
  compoundUrl <- paste0("https://www.genome.jp/dbget-bin/get_linkdb?-t+compound+path:", pathwayId)
  compoundDoc <- htmlParse(getURL(compoundUrl), encoding = "utf-8")
  compoundLinks <- getNodeSet(compoundDoc, "/html/body/pre/a")
  compoundIds <- sapply(compoundLinks, function(node) xmlGetAttr(node, "href"))
  compoundNames <- sapply(getNodeSet(compoundDoc, "/html/body/pre/text()"), xmlValue)[-1]
  data.frame(compoundId = paste(compoundIds, collapse = ";"), compoundName = paste(compoundNames, collapse = ";"))
}

# Main process
keggUrl <- "https://www.genome.jp/kegg/pathway.html#global"
keggDoc <- htmlParse(getURL(keggUrl), encoding = "UTF-8")

pathwayLinks <- getNodeSet(keggDoc, "http://a[@href]")
pathwayIds <- sapply(pathwayLinks[65:276], function(node) gsub("/pathway/", "", xmlGetAttr(node, "href")))
pathwayNames <- sapply(pathwayLinks[65:276], xmlValue)

# Applying extractCompounds function to each pathwayId
compoundDataList <- Map(extractCompounds, pathwayIds)
pathwayData <- do.call(rbind, compoundDataList)

# Combine all data into a single data frame
finalData <- data.frame(pathwayId = pathwayIds, pathwayName = pathwayNames, pathwayData)
finalData %>% write_csv(paste0("KeggAllcompounds-",Sys.Date(),".csv"))
finalData2 <- finalData[finalData$compoundId != "", ]

==注意事項==
finalData2 <- finalData[finalData$compoundId != "", ]這一步的目的是為了將finalDate$compoundId中有空值的的行進行刪除，否則后面數(shù)據(jù)整理的時候會報錯。

  • 數(shù)據(jù)整理
#' 分別采用for和map的方式將結(jié)果進行整理
#' 整理成為result_data和result_data2
#' 采用for循環(huán)的方式 
result_data <- data.frame()
nrow(finalData2)
for (i in 1:nrow(finalData2)) {
  cid <- finalData2$compoundId[i]
  extracted_cid <- str_extract_all(cid, "C\\d+")
  CID <- unlist(extracted_cid)
  CName <- finalData2$compoundName[i]
  split_CName <- strsplit(CName, "\n;")
  CompoundName <- lapply(split_CName[[1]], trimws) %>% unlist()
  pathway <- cbind(CID, CompoundName)
  pathwayId <- rep(finalData2$pathwayId[i], nrow(pathway))
  pathwayName <- rep(finalData2$pathwayName[i], nrow(pathway))
  dat <- cbind(pathway, pathwayId, pathwayName)
  result_data <- rbind(result_data, dat)
}

#' 采用map的方式
# Define a function to process each row
process_row <- function(row) {
  cid <- row$compoundId
  extracted_cid <- str_extract_all(cid, "C\\d+")
  CID <- unlist(extracted_cid)
  
  CName <- row$compoundName
  split_CName <- strsplit(CName, "\n;")
  CompoundName <- lapply(split_CName[[1]], trimws) %>% unlist()
  
  # Ensure the result is a data frame
  if (length(CID) > 0 && length(CompoundName) > 0) {
    pathway <- data.frame(CID, CompoundName, stringsAsFactors = FALSE)
    pathwayId <- rep(row$pathwayId, nrow(pathway))
    pathwayName <- rep(row$pathwayName, nrow(pathway))
    return(data.frame(pathway, pathwayId, pathwayName, stringsAsFactors = FALSE))
  } else {
    return(data.frame(CID = character(), CompoundName = character(), pathwayId = row$pathwayId, pathwayName = row$pathwayName, stringsAsFactors = FALSE))
  }
}

# Apply the process_row function to each row of finalData using map_df
result_data2 <- map_df(seq_len(nrow(finalData2)), ~process_row(finalData2[.x, ]))

result_data %>% write_csv(paste0("keggAllCompoundReshapedData2-",Sys.Date(),".csv")) #將整理好的結(jié)果進行儲存，后面可以直接讀取進來用，不用重復跑前面的代碼，畢竟也需要時間的
result_data2 %>% write_csv(paste0("keggAllCompoundReshapedData22-",Sys.Date(),".csv")
表1 初步整理后的代謝物kegg數(shù)據(jù)庫表
  • 對kegg代謝物數(shù)據(jù)庫進一步整理
# 后期富集分析只需要保留表1的第1和第4列
keggannotation <- result_data %>% 
  select(c(-2,-3)) %>% set_colnames(c("ID", "pathway"))

二、進行kegg富集分析

  • 先將自己測定的正負離子模式代謝物鑒定的表（注意要包含kegg id這一列，如果沒有，想方設法將代謝物的kegg id找到）讀取后進行整理
uploadfile1 <- "D:/其他人員2/王書/公司結(jié)果/非靶向/Result-X101SC23124721-Z01-J002-B1-42/Result-X101SC23124721-Z01-J002-B1-42/2.MetAnnotation/HMDB_KEGG_Lipidmaps"
uploadfile2 <- "D:/其他人員2/王書/公司結(jié)果/非靶向/Result-X101SC23124721-Z01-J002-B1-42/Result-X101SC23124721-Z01-J002-B1-42/3.MetDiffScreening/MPP.vs.MPN" 
allkeggid <- read.csv(paste0(uploadfile1,"/meta_intensity_neg_hmdb_kegg_lipidmaps.csv")) %>% select(Kegg_ID) %>% 
  bind_rows(.,read.csv(paste0(uploadfile1,"/meta_intensity_pos_hmdb_kegg_lipidmaps.csv")) %>% select(Kegg_ID)) %>% 
  filter(Kegg_ID != "--") %>% 
  set_colnames("ID") %>% 
  mutate(across("ID",str_replace,"cpd:", ""))

其實就是把正負離子代謝物所對應的KEGG ID合并，如表2：


表2 正負離子代謝物表讀取后只保留kegg id這一列
  • 讀取自己的差異代謝物并保留kegg id
#'差異代謝物
diffkeggid <- read.csv(paste0(uploadfile2,"/MPP.vs.MPN_neg_diff.anno.csv")) %>% select(Kegg_ID) %>% 
  bind_rows(.,read.csv(paste0(uploadfile2,"/MPP.vs.MPN_pos_diff.anno.csv")) %>% select(Kegg_ID)) %>% 
  filter(Kegg_ID != "--") %>% 
  set_colnames("ID") %>% 
  mutate(across("ID",str_replace,"cpd:", ""))
  • 數(shù)據(jù)整合
    這里需要大家理解的是，我們每個人做的課題不一樣，所測定到的代謝物也不一樣，所以需要根據(jù)自己實際測定到的代謝物具體多少，構(gòu)建自己的代謝物kegg背景庫，而不是直接采用表1來進行富集分析。如表3所示：
total <- right_join(keggannotation,allkeggid,by="ID") %>% select(2,1)
表3 基于自己項目測出的代謝物構(gòu)建kegg庫
  • kegg富集分析并導出結(jié)果
# 富集分析
x <- clusterProfiler::enricher(gene = diffkeggid$ID,TERM2GENE = total,minGSSize = 1,pvalueCutoff = 1,qvalueCutoff = 1)
# 結(jié)果導出
write.csv(as.data.frame(x@result) %>% select(-1,-2),
          file = paste0(uploadfile2,"/MP_KEGG_enrichment_result.csv"))

我們來看看富集分析的結(jié)果怎么樣，如表4所示：


表4 代謝物kegg富集分析結(jié)果

三、對富集的結(jié)果進行整理和可視化

#' 1.數(shù)據(jù)清洗
df <- read_csv(paste0(uploadfile2,"/MP_KEGG_enrichment_result.csv")) %>%
  dplyr::rename("Description"="...1") %>% 
  arrange(desc(Count)) %>%
  select(1,2,3,4,8) %>% 
  separate(`GeneRatio`,into=c("A","B"),sep="/") %>%
  mutate(A=as.numeric(A),B=as.numeric(B)) %>%
  mutate(count=A/B) %>% head(10) %>% arrange(Count)

#' 2.定義因子
df$Description <- factor(df$Description,levels = c(df$Description %>% as.data.frame() %>% pull()))

#' 3.數(shù)據(jù)可視化
p <- df %>% ggplot(aes(count,Description))+
  geom_point(aes(size=Count,color=pvalue,fill=pvalue),pch=21)+
  scale_color_gradientn(colours = (rev(RColorBrewer::brewer.pal(11,"RdBu"))))+
  scale_fill_gradientn(colours =(rev(RColorBrewer::brewer.pal(11,"RdBu"))))+
  guides(size=guide_legend(title="Count"))+
  labs(x=NULL,y=NULL)+
  theme(axis.title = element_blank(),
        axis.text.x=element_text(color="black",angle =0,hjust=0.5,vjust=0.5, margin = margin(b =5)),
        axis.text.y=element_text(color="black",angle =0,hjust=1,vjust=0.5),
        panel.background = element_rect(fill = NA,color = NA),
        panel.grid.minor= element_line(size=0.2,color="#e5e5e5"),
        panel.grid.major = element_line(size=0.2,color="#e5e5e5"),
        panel.border = element_rect(fill=NA,color="black",size=1,linetype="solid"),
        legend.key=element_blank(),
        legend.title = element_text(color="black",size=9),
        legend.text = element_text(color="black",size=8),
        legend.spacing.x=unit(0.1,'cm'),
        legend.key.width=unit(0.5,'cm'),
        legend.key.height=unit(0.5,'cm'),
        legend.background=element_blank(),
        legend.box="horizontal",
        legend.box.background = element_rect(color="black"),
        legend.position = c(1,0),legend.justification = c(1,0))+
  scale_y_discrete(labels = function(y) str_wrap(y,width=30))
ggsave(paste0(uploadfile2, "/MP_Kegg_enrichment.png"), plot = p, width = 6, height = 6, dpi = 300)

  • 結(jié)果如圖所示


    圖1 kegg富集分析通路圖
最后編輯于 ：
?著作權(quán)歸作者所有,轉(zhuǎn)載或內(nèi)容合作請聯(lián)系作者
【社區(qū)內(nèi)容提示】社區(qū)部分內(nèi)容疑似由AI輔助生成，瀏覽時請結(jié)合常識與多方信息審慎甄別。
平臺聲明：文章內(nèi)容（如有圖片或視頻亦包括在內(nèi)）由作者上傳并發(fā)布，文章內(nèi)容僅代表作者本人觀點，簡書系信息發(fā)布平臺，僅提供信息存儲服務。

相關閱讀更多精彩內(nèi)容

友情鏈接更多精彩內(nèi)容