TCGA-HCC-差異分析-富集分析-pathview

library(pathview)
library(TCGAbiolinks)
library(SummarizedExperiment)
library(clusterProfiler)
#下載HCC的Gene expression文件
query <- GDCquery(project = "TCGA-LIHC",
                  data.category = "Gene expression",
                  data.type = "Gene expression quantification",
                  platform = "Illumina HiSeq", 
                  file.type  = "results", 
                  sample.type = c("normal"),
                  legacy = TRUE)

GDCdownload(query)
#獲得表達文件
HCC.exp <- GDCprepare(query, 
                      save = TRUE, 
                      summarizedExperiment = TRUE, 
                      save.filename = "LIHCIllumina_HiSeq.rda")

dataPrep_HCC <- TCGAanalyze_Preprocessing(object = HCC.exp,
                                          cor.cut = 0.6,    
                                          datatype = "raw_count",
                                          filename = "HCC_IlluminaHiSeq_RNASeqV2.png")
dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep_HCC,
                                      geneInfo = TCGAbiolinks::geneInfo,
                                      method = "gcContent") #18323   672

dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm,
                                  method = "quantile",
                                  qnt.cut =  0.25)
HCC_clin <- GDCquery_clinic(project = "TCGA-LIHC", type = "Clinical")
dataFiltHCC <- subset(dataFilt, 
                      select = substr(colnames(dataFilt),1,12) %in% HCC_clin$bcr_patient_barcode)



#下載正常的樣本
query <- GDCquery(project = "TCGA-LIHC",
                  data.category = "Gene expression",
                  data.type = "Gene expression quantification",
                  platform = "Illumina HiSeq", 
                  file.type  = "results", 
                  sample.type = c("Solid Tissue Normal"),
                  legacy = TRUE)

GDCdownload(query)
#獲得表達文件
HCC.normal.exp <- GDCprepare(query, 
                      save = TRUE, 
                      summarizedExperiment = TRUE, 
                      save.filename = "LIHC_normal_Illumina_HiSeq.rda")
dataPrep_HCC_normal <- TCGAanalyze_Preprocessing(object = HCC.normal.exp,
                                          cor.cut = 0.6,    
                                          datatype = "raw_count",
                                          filename = "HCC_noraml_IlluminaHiSeq_RNASeqV2.png")
dataNorm_normal <- TCGAanalyze_Normalization(tabDF = dataPrep_HCC_normal,
                                      geneInfo = TCGAbiolinks::geneInfo,
                                      method = "gcContent") 
dataFilt_normal <- TCGAanalyze_Filtering(tabDF = dataNorm_normal,
                                  method = "quantile",
                                  qnt.cut =  0.25) 
#取正常樣本與腫瘤樣本中相同的gene交集
c <- intersect(row.names(dataFiltHCC),row.names(dataFilt_normal))
dim(dataFiltHCC)
class(dataFiltHCC)
dataFiltHCC <- as.data.frame(dataFiltHCC)
dataFiltHCC$geneid <- row.names(dataFiltHCC)
library(dplyr)
length(c)
dataFiltHCC1 <- dataFiltHCC %>%
  filter(geneid %in% c)
row.names(dataFiltHCC1) <- dataFiltHCC1$geneid
names(dataFiltHCC1)
dataFiltHCC1 <- dataFiltHCC1[,-372]
dataFiltHCC1 <- as.matrix(dataFiltHCC1)

dataFilt_normal <- as.data.frame(dataFilt_normal)
dataFilt_normal$geneid <- row.names(dataFilt_normal)
names(dataFilt_normal)
dataFilt_normal1 <- dataFilt_normal %>%
  filter(geneid %in% c)
row.names(dataFilt_normal1) <- dataFilt_normal1$geneid
colnames(dataFilt_normal1)
row.names(dataFilt_normal1)
dataFilt_normal1 <- dataFilt_normal1[,-51]
dataFilt_normal1 <- as.matrix(dataFilt_normal1)
row.names(dataFilt_normal1)
#排序
dataFilt_normal1 <- dataFilt_normal1[order(row.names(dataFilt_normal1),decreasing=FALSE),]
dataFiltHCC1 <- dataFiltHCC1[order(row.names(dataFiltHCC1),decreasing=FALSE),]
#  should be TRUE，TRUE說明兩個矩陣的行名一模一樣
all(row.names(dataFilt_normal1) == row.names(dataFiltHCC1))
#差異分析
dataDEGs <- TCGAanalyze_DEA(mat1 = dataFiltHCC1,
                            mat2 = dataFilt_normal1,
                            Cond1type = "Tumor",
                            Cond2type = "Normal",
                            fdr.cut = 0.01 ,
                            logFC.cut = 1,
                            method = "glmLRT")
> head(dataDEGs)
           logFC   logCPM        LR       PValue          FDR
155060 -2.571642 2.321918 156.36454 7.047070e-36 7.143353e-35
90288  -1.676365 1.398311  28.34992 1.012510e-07 1.675663e-07
A4GALT -1.396513 2.638181  53.84503 2.169434e-13 4.951607e-13
AACS   -1.616439 3.692283 117.12599 2.693991e-27 1.477036e-26
AADAT   2.179292 3.500549 135.76657 2.244217e-31 1.638009e-30
AAGAB  -1.150286 4.838394 244.67963 3.752987e-55 2.604312e-53
> dim(dataDEGs)
[1] 5461    5

dataDEGs$ID <- row.names(dataDEGs)
dataDEGs <- dataDEGs[,-6]
d <- intersect(MethylMixResults$MethylationDrivers,dataDEGs$ID )
HCC_GO <- dataDEGs %>%
  dplyr::filter(ID %in% d )
HCC_GO <- data.frame(ID = HCC_GO $ID,logFC =HCC_GO $logFC)
library(org.Hs.eg.db)
library(topGO)
library(GSEABase)
library(clusterProfiler)
#將txt中的一行或一列標成一個向量
x <- HCC_GO$ID #total_RNA是一個只有一行基因標準名的txt文件
#將標準的gene轉化爲ENTREZID格式
eg = bitr(x, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
#進行GO富集

ego <- enrichGO(gene         = eg$ENTREZID,
                OrgDb         = org.Hs.eg.db,
                ont           = "ALL",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.2,
                qvalueCutoff  = 0.2,
                readable = T)
df_ego <- as.data.frame(ego)
write.table(df_ego,"sample.csv",sep=",")

sample <- read.csv("I:/test/sample.csv", stringsAsFactors=FALSE)
#生成GOplot圖
library(GOplot)
# Generate the plotting object
circ <- circle_dat(sample, HCC_GO)
#sample的列名爲 ：Category ID Term Genes adj_pval
#GO_log的列名爲：ID logFC
#繪製GOplot
chord <- chord_dat(data = circ, genes = HCC_GO, process = sample$Term)
GOChord(chord, space = 0.02, gene.order = 'logFC',gene.space = 0.25, gene.size = 2,process.label = 6)

# DEGs TopTable
dataDEGsFiltLevel <- TCGAanalyze_LevelTab(dataDEGs,"Tumor","Normal",
                                          dataFiltHCC1[,colnames(dataFiltHCC1)],
                                          dataFilt_normal1[,colnames(dataFilt_normal1)])
dataDEGsFiltLevel$GeneID <- 0
> head(dataDEGsFiltLevel)
       mRNA     logFC          FDR      Tumor    Normal     Delta GeneID
ALB     ALB  1.159244 7.042187e-16 2491918.90 5975905.3 2888743.2      0
HP       HP  1.738657 1.612502e-16  640987.55 2297407.6 1114457.4      0
FTL     FTL -1.144761 3.796445e-10  515336.83  250421.6  589937.3      0
GLUL   GLUL -2.998764 5.611403e-14  187833.75   25261.8  563269.1      0
SPP1   SPP1 -5.972776 6.830736e-21   71733.61    1227.8  428448.7      0
APOA1 APOA1  1.226569 1.459487e-09  294893.27  740950.3  361706.8      0

dataDEGsFiltLevel$ID <- row.names(dataDEGsFiltLevel)
head(dataDEGsFiltLevel)
> head(dataDEGsFiltLevel)
       mRNA     logFC          FDR      Tumor    Normal     Delta GeneID
ALB     ALB  1.159244 7.042187e-16 2491918.90 5975905.3 2888743.2      0
HP       HP  1.738657 1.612502e-16  640987.55 2297407.6 1114457.4      0
FTL     FTL -1.144761 3.796445e-10  515336.83  250421.6  589937.3      0
GLUL   GLUL -2.998764 5.611403e-14  187833.75   25261.8  563269.1      0
SPP1   SPP1 -5.972776 6.830736e-21   71733.61    1227.8  428448.7      0
APOA1 APOA1  1.226569 1.459487e-09  294893.27  740950.3  361706.8      0
dataDEGsFiltLevel <- dataDEGsFiltLevel[,-8]
dataDEGsFiltLevel1 <- dataDEGsFiltLevel %>%
  dplyr::filter(mRNA %in% d)

a <- c(dataDEGsFiltLevel$mRNA)
dataDEGsFiltLevel$mRNA <- a

dim(dataDEGsFiltLevel1)

# Converting Gene symbol to geneID
eg = as.data.frame(bitr(dataDEGsFiltLevel1$mRNA,
                        fromType="SYMBOL",
                        toType="ENTREZID",
                        OrgDb="org.Hs.eg.db"))
eg <- eg[!duplicated(eg$SYMBOL),]


dataDEGsFiltLevel1 <- dataDEGsFiltLevel1[dataDEGsFiltLevel1$mRNA %in% eg$SYMBOL,]

dataDEGsFiltLevel1 <- dataDEGsFiltLevel1[order(dataDEGsFiltLevel1$mRNA,decreasing=FALSE),]
eg <- eg[order(eg$SYMBOL,decreasing=FALSE),]

# table(eg$SYMBOL == dataDEGsFiltLevel$mRNA) should be TRUE
all(eg$SYMBOL == dataDEGsFiltLevel1$mRNA)


dataDEGsFiltLevel1$GeneID <- eg$ENTREZID

dataDEGsFiltLevel_sub <- subset(dataDEGsFiltLevel1, select = c("GeneID", "logFC"))
genelistDEGs <- as.numeric(dataDEGsFiltLevel_sub$logFC)
names(genelistDEGs) <- dataDEGsFiltLevel_sub$GeneID

library(pathview)
# pathway.id: hsa05214 is the glioma pathway
# limit: sets the limit for gene expression legend and color
> head(genelistDEGs)
     4363       215     84519     54988    113622     60312 
-1.731574 -1.392554 -1.294594  1.384184 -1.312951 -1.418266 
hsa05225 <- pathview::pathview(gene.data  = genelistDEGs,
                               pathway.id = "hsa05225",
                               species    = "hsa",
                               limit = list(gene=as.integer(max(abs(genelistDEGs)))))
TCGA-HCC-差異分析-富集分析-pathview

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