clusterProfiler
 0 Posted Updated R / Bio / Anno/Enrich25 minutes read (About 3689 words)

clusterProfiler

clusterProfiler

Tutorial: yulab

Install

BiocManager::install("clusterProfiler")

Enrichment analysis and GSEA

Enrichment analysis is a computational method used in bioinformatics to determine whether a given set of genes or proteins is enriched for specific functions, pathways, or biological processes. It involves comparing the input gene set to a reference database, such as Gene Ontology, KEGG, or Reactome, and identifying the over-represented terms using statistical methods.

Gene Set Enrichment Analysis (GSEA) is a type of enrichment analysis that determines whether a particular gene set shows statistically significant differences between two biological states. Unlike traditional enrichment analysis, GSEA considers the entire gene set, rather than individual genes, and evaluates whether the gene set is significantly enriched at the top or bottom of a ranked list of genes based on their differential expression or correlation with a phenotype. GSEA is commonly used in transcriptomics and genomics studies to identify pathways or biological processes that are differentially regulated between disease and control samples.
© ChatGPT

Go ontology

GO_1. Supported Organism

For module species which added in OrgDb, we can turn the ID to GO_id;

For other species, you can build your own OrgDb database by following GOSemSim.

If genes are already annotated (in data.frame witch gene ID column followed by GO ID), we can use enricher() and geosGO() function to perform over-representation test.

GO_2. Example for OrgDb species

library(clusterProfiler)
library(org.Hs.eg.db)

data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
gene.df <- bitr(gene, fromType = "ENTREZID",
        toType = c("ENSEMBL", "SYMBOL"),
        OrgDb = org.Hs.eg.db)
head(gene.df)
ENTREZID         ENSEMBL SYMBOL
1     4312 ENSG00000196611   MMP1
2     8318 ENSG00000093009  CDC45
3    10874 ENSG00000109255    NMU
4    55143 ENSG00000134690  CDCA8
5    55388 ENSG00000065328  MCM10
6      991 ENSG00000117399  CDC20
4312 8318
ACCNUM AAA35699 NP_001139410
ENTREZID ENSG00000196611 ENSG00000093009
ENSEMBL SYMBOL MMP1 CDC45 
ggo <- groupGO(gene     = gene,
               OrgDb    = org.Hs.eg.db,
               ont      = "CC",
               level    = 3,
               readable = TRUE)

head(ggo)
barplot(ggo, showCategory = 20)
ID                    Description Count GeneRatio
GO:0005886 GO:0005886                plasma membrane    55    55/207
GO:0005628 GO:0005628              prospore membrane     0     0/207
GO:0005789 GO:0005789 endoplasmic reticulum membrane     8     8/207
...

132

GO_3. Over-Representation Test

ego <- enrichGO(gene          = gene,
                universe      = names(geneList),
                OrgDb         = org.Hs.eg.db,
                ont           = "CC",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.01,
                qvalueCutoff  = 0.05,
        readable      = TRUE)
head(ego)
ego2 <- enrichGO(gene         = gene.df$ENSEMBL,
                OrgDb         = org.Hs.eg.db,
                keyType       = 'ENSEMBL',
                ont           = "CC",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.01,
                qvalueCutoff  = 0.05)

## turn ENSEMBL ID to Samble
ego2_r <- setReadable(ego2, OrgDb = org.Hs.eg.db)

dotplot(ego2)
ego ego2
tzt3RK.md.png tztDRf.png

GO_4 GO Gene Set Enrichment Analysis

ego3 <- gseGO(geneList     = geneList,
              OrgDb        = org.Hs.eg.db,
              ont          = "CC",
              nPerm        = 1000,
              minGSSize    = 100,
              maxGSSize    = 500,
              pvalueCutoff = 0.05,
              verbose      = FALSE)

dotplot(ego3)
ID                              Description setSize enrichmentScore       NES      pvalue   p.adjust    qvalues rank                   leading_edge
GO:0031012 GO:0031012                     extracellular matrix     427      -0.4868578 -2.138854 0.001231527 0.03095063 0.02171974 1797 tags=37%, list=14%, signal=33%
GO:0099568 GO:0099568                       cytoplasmic region     368      -0.3426037 -1.488559 0.001259446 0.03095063 0.02171974 2613 tags=28%, list=21%, signal=23%

NSMQfg.png

GO ontology for Non-module Species


KEGG Enrichment

KE_1. Over-Representation Test

library(clusterProfiler)
search_kegg_organism('ece', by='kegg_code')

ecoli <- search_kegg_organism('Escherichia coli', by='scientific_name')
dim(ecoli)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]

kk <- enrichKEGG(gene         = gene,
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
head(kk)
ID                                                   Description GeneRatio  BgRatio       pvalue     p.adjust       qvalue                                             geneID Count
hsa04110 hsa04110                                                    Cell cycle     11/93 124/8031 1.590786e-07 3.213388e-05 3.148083e-05 8318/991/9133/890/983/4085/7272/1111/891/4174/9232    11
hsa04114 hsa04114                                                Oocyte meiosis     10/93 128/8031 1.960649e-06 1.980256e-04 1.940011e-04    991/9133/983/4085/51806/6790/891/9232/3708/5241    10
hsa04218 hsa04218                                           Cellular senescence     10/93 160/8031 1.450991e-05 9.375595e-04 9.185054e-04     2305/4605/9133/890/983/51806/1111/891/776/3708    10
hsa04061 hsa04061 Viral protein interaction with cytokine and cytokine receptor      8/93 100/8031 1.856553e-05 9.375595e-04 9.185054e-04           3627/10563/6373/4283/6362/6355/9547/1524     8
hsa03320 hsa03320                                        PPAR signaling pathway      7/93  77/8031 2.765640e-05 1.117319e-03 1.094611e-03                 4312/9415/9370/5105/2167/3158/5346     7
hsa04914 hsa04914                       Progesterone-mediated oocyte maturation      7/93  99/8031 1.392778e-04 4.689019e-03 4.593724e-03                    9133/890/983/4085/6790/891/5241     7

KE_2. Gene Set Enrichment Analysis

kk2 <- gseKEGG(geneList     = geneList,
               organism     = 'hsa',
               nPerm        = 1000,
               minGSSize    = 120,
               pvalueCutoff = 0.05,
               verbose      = FALSE)
head(kk2)
ID                 Description setSize enrichmentScore       NES      pvalue  p.adjust    qvalues rank                   leading_edge
hsa04510 hsa04510              Focal adhesion     190      -0.4169068 -1.697372 0.001418440 0.0241065 0.01552815 2183 tags=27%, list=17%, signal=22%
hsa03013 hsa03013               RNA transport     131       0.4116488  1.742019 0.003067485 0.0241065 0.01552815 3383 tags=40%, list=27%, signal=29%
hsa05152 hsa05152                Tuberculosis     162       0.3745153  1.628558 0.003164557 0.0241065 0.01552815 2823 tags=34%, list=23%, signal=27%
hsa04218 hsa04218         Cellular senescence     143       0.4153718  1.761689 0.003225806 0.0241065 0.01552815 1155  tags=17%, list=9%, signal=16%
hsa05203 hsa05203        Viral carcinogenesis     164       0.3523856  1.535177 0.003246753 0.0241065 0.01552815 3112 tags=35%, list=25%, signal=26%
hsa04062 hsa04062 Chemokine signaling pathway     165       0.3754101  1.632867 0.003300330 0.0241065 0.01552815 1298 tags=21%, list=10%, signal=19%
kk kk2
NSY9Rf.md.png NSYpJP.md.png

KE_3. KEGG Module over-representation test

mkk <- enrichMKEGG(gene = gene,
                   organism = 'hsa')

KE_4. KEGG Module Gene Set Enrichment Analysis

mkk2 <- gseMKEGG(geneList = geneList,
                 organism = 'hsa')

A Pipeline Example

Result from: edgeR
species: dme

library(clusterProfiler)
library(org.Dm.eg.db)
library(pathview)
library(reshape2)
library(stringr)
library(ggplot2)
library(enrichplot)

args = commandArgs(trailingOnly=TRUE)

SampleA= args[1]#"G1F1"
SampleB= args[2]#"G50F"
Pvalue = 0.01
logFC= 2
C_cut = 10
Level= 2

Sample_Dir = paste(SampleA,"_VS_",SampleB,"/",sep="")
for(i in c("GO", "KEGG", "WIKI", "Reactome")){
    dir.create(i)
    dir.create(paste(i, Sample_Dir,sep='/'))
}

Gene_list <- read.table(paste("RSEM_transcript/transcript.isoform.counts.matrix.", SampleA, "_vs_", SampleB,".edgeR.DE_results", sep=""))


ENTREZID = bitr(row.names(Gene_list), fromType="FLYBASE", toType="ENTREZID", OrgDb="org.Dm.eg.db")
FLYBASECG = bitr(row.names(Gene_list), fromType="FLYBASE", toType="FLYBASECG", OrgDb="org.Dm.eg.db")
SYMBOL = bitr(row.names(Gene_list), fromType="FLYBASE", toType="SYMBOL", OrgDb="org.Dm.eg.db")


Gene_list$ENTREZID <- ENTREZID$ENTREZID[match(row.names(Gene_list), ENTREZID$FLYBASE)]
Gene_list$FLYBASECG <- FLYBASECG$FLYBASECG[match(row.names(Gene_list), FLYBASECG$FLYBASE)]
Gene_list$SYMBOL <- SYMBOL$SYMBOL[match(row.names(Gene_list), SYMBOL$FLYBASE)]

KE2SY <-function(kk_GSEA, COL){
    if( nrow(kk_GSEA@result) >0){
        for(i in c(1:nrow(kk_GSEA@result))){
            LIST <- kk_GSEA@result[[COL]][i]
            kk_GSEA@result[[COL]][i] <- paste(Gene_list$SYMBOL[match(str_remove(str_split(LIST, "/")[[1]], "Dmel_"),Gene_list$FLYBASECG )], collapse = "/")
        }        
    }
    return(kk_GSEA)
}

EN2SY <-function(WikiP_enrich, COL){
    if(length(WikiP_enrich@result[[COL]])>0){
        for(i in c(1:length(WikiP_enrich@result[[COL]]))){
            LIST <- WikiP_enrich@result[[COL]][i]
            WikiP_enrich@result[[COL]][i] <- paste(Gene_list$SYMBOL[match(str_remove(str_split(LIST, "/")[[1]], "Dmel_"),Gene_list$ENTREZID )], collapse = "/")
        }
    }
    return(WikiP_enrich)
}

ggsave_GO <- function(NAME, LEN){
    BASE = 3.8
    RATE = 0.125
    if (BASE + RATE*LEN <= 40){
        W = BASE + RATE*LEN
    }else{
        W = 40
    }
    ggsave(NAME , w = W, h = 8.35, limitsize = FALSE )
}

ggsave_GO_enrich <- function(NAME, LEN){
    BASE = 2
    RATE = 0.1114
    if (BASE + RATE*LEN <= 40){
        H = BASE + RATE*LEN
    }else{
        H = 40
    }
    ggsave(NAME , w = 10, h = H, limitsize = FALSE)
}

ridgeplot_save <- function(NAME,LEN){
    BASE = 1.02
    RATE = 0.2
    if (BASE + RATE*LEN <= 40){
        H = BASE + RATE*LEN
    }else{
        H = 40
    }
    ggsave(NAME , w = 10, h = H, limitsize = FALSE)
}

## GO ontology
TMP <- Gene_list[abs(Gene_list$logFC) >=2,]
TMP <- TMP[TMP$PValue <= Pvalue,]


sig_genes = TMP$ENTREZID

gooc <- groupGO(gene     = sig_genes,
               OrgDb    = org.Dm.eg.db,
               ont      = "CC",
               level    = Level,
               readable = TRUE)
goom <- groupGO(gene     = sig_genes,
              OrgDb    = org.Dm.eg.db,
              ont      = "MF",
              level    = Level,
              readable = TRUE)
goob <- groupGO(gene     = sig_genes,
             OrgDb    = org.Dm.eg.db,
             ont      = "BP",
             level    = Level,
             readable = TRUE)

gooc@result$Group = "CC"
goom@result$Group = "MF"
goob@result$Group = "BP"


GO_TB <- rbind(gooc@result, goom@result, goob@result)
GO_TB <- GO_TB[GO_TB$Count!=0,]
File_name = paste("Ontology", SampleA,SampleB,Pvalue, logFC, sep="_" )
write.csv(GO_TB, paste("GO/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)

# [GO ontology plot]
ggplot(GO_TB, aes(x=Description, y=Count, fill=Group)) +
    geom_bar(stat = 'identity') +
    facet_grid(~Group, scales = 'free', space = 'free') + theme_bw() +
    theme(axis.text.x = element_text(angle = 270, hjust = 0, vjust = .5), legend.position = 'none', panel.grid =  element_blank(), strip.background = element_rect(fill = 'white'))
ggsave_GO(paste("GO/", Sample_Dir, File_name, ".png", sep="" ), nrow(GO_TB))

## Overrepresentation
File_name = paste("Enrichment", SampleA,SampleB,Pvalue, logFC, sep="_" )


ego <- enrichGO(gene          = sig_genes,
                universe      = Gene_list$ENTREZID,
                OrgDb         = org.Dm.eg.db,
                ont           = "All",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.01,
                qvalueCutoff  = 0.05,
        readable      = TRUE)
head(ego)
SIZE = as.numeric(as.character(as.data.frame(str_split_fixed(ego@result$GeneRatio, "/",2))[[1]]))/
as.numeric(as.character(as.data.frame(str_split_fixed(ego@result$BgRatio, "/",2))[[1]]))
ego@result$size = SIZE
write.csv(ego, paste("GO/", Sample_Dir, File_name, ".csv", sep="" ), row.names=F)

# [GO Enrichment plot]
ggplot(ego@result, aes(x=Count, y=Description)) +
    geom_point(aes(size=size, color=pvalue)) + theme_bw() +
    facet_grid(ONTOLOGY~., space = 'free', scales = 'free')

ggsave_GO_enrich(paste("GO/", Sample_Dir, File_name, ".png", sep="" ), nrow(ego@result))

geneList <- Gene_list$logFC
names(geneList) <- Gene_list$ENTREZID
geneList <- sort(geneList, decreasing = T)

for(GROUP in c("CC", "BP", "MF")){
    egocc <- enrichGO(gene          = sig_genes,
            universe      = Gene_list$ENTREZID,
            OrgDb         = org.Dm.eg.db,
            ont           = GROUP,
            pAdjustMethod = "BH",
            pvalueCutoff  = 0.01,
            qvalueCutoff  = 0.05,
            readable      = TRUE)

    write.csv(egocc, paste("GO/",Sample_Dir, File_name, "_", GROUP, ".csv", sep="" ), row.names=F)
    # [GO Network category]
    Plot_and_Save <-function(){
        goplot(egocc)
        ggsave( paste("GO/",Sample_Dir, File_name,  "_", GROUP, "_category", ".png", sep="" ),
            w = 20, h = 10.9 )
    # [GO Network Genes]
        cnetplot(egocc, foldChange=geneList)
        ggsave( paste("GO/", Sample_Dir, File_name,  "_", GROUP, "_genes", ".png", sep="" ),
        w = 20, h = 10.9 )
    }
    try(Plot_and_Save(),silent=TRUE)

}


# GO Gene Set Enrichment Analysis
File_name = paste("GSEA",Pvalue, logFC, sep="_" )

for(GROUP in c("CC", "BP", "MF")){
    GOgse_CC <- gseGO(geneList     = geneList,
                  OrgDb        = org.Dm.eg.db,
                  ont          = GROUP,
                  minGSSize    = 100,
                  maxGSSize    = 500,
                  eps = 1e-10,
                  pvalueCutoff = 0.05,
                  verbose      = FALSE)
    write.csv(GOgse_CC, paste("GO/",Sample_Dir, File_name, "_", GROUP, ".csv", sep="" ), row.names=F)

    ridgeplot(GOgse_CC)
    ridgeplot_save(paste("GO/",Sample_Dir, File_name, "_", GROUP,"_ridgeplot.png", sep="" ), nrow(GOgse_CC@result))
    Plot_and_Save <-function(){
        goplot(GOgse_CC)
        ggsave( paste("GO/",Sample_Dir, File_name,  "_", GROUP, "_category", ".png", sep="" ),
        w = 20, h = 10.9 )
        cnetplot(GOgse_CC, foldChange=geneList)
        ggsave( paste("GO/",Sample_Dir, File_name,  "_", GROUP, "_genes", ".png", sep="" ),
        w = 20, h = 10.9 )
    }
    try(Plot_and_Save(),silent=TRUE)

    # [GO GSEA upsetplot]
    upsetplot(GOgse_CC)
    ggsave( paste("GO/",Sample_Dir, File_name,  "_", GROUP, "_upset", ".png", sep="" ),
        w = 20, h = 10.9 )
    # [GO GSEA Escore]
    for (i in c(1:nrow(GOgse_CC@result))){
        gseaplot2(GOgse_CC, geneSetID = i, title = GOgse_CC$Description[i], color = 'salmon', pvalue_table = TRUE)
        ggsave(paste("GO/",Sample_Dir, File_name,  "_", str_split(GOgse_CC@result$ID[i],":")[[1]][2], "_GSEA", ".png", sep="" ), w = 7.5, h = 5)
    }
}

#########################
## KEGG                 #
#########################

# gene set enrichment analysis
File_name = paste("GSEA",Pvalue, logFC, sep="_" )
geneList_kk <- Gene_list$logFC
names(geneList_kk) <- paste("Dmel", Gene_list$FLYBASECG, sep="_")
geneList_kk <- sort(geneList_kk, decreasing = T)

kk_GSEA <- gseKEGG(geneList     = geneList_kk,
               organism     = 'dme',
               minGSSize    = 10,
               pvalueCutoff = 0.05,
               verbose      = FALSE)

for (i in c(1:nrow(kk_GSEA@result))){
    gseaplot2(kk_GSEA, geneSetID = i, title = kk_GSEA$Description[i], color = 'salmon', pvalue_table = TRUE)
    ggsave(paste("KEGG/",Sample_Dir, File_name,  "_", kk_GSEA@result$ID[i], "_GSEA", ".png", sep="" ), w = 7.5, h = 5)
}


kk_GSEA2 <- KE2SY(kk_GSEA, "core_enrichment")

write.csv(kk_GSEA2, paste("KEGG/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)


geneList_smb <- Gene_list$logFC
names(geneList_smb) <-Gene_list$SYMBOL
geneList_smb <- sort(geneList_smb, decreasing = T)

Plot_and_Save <-function(){
    cnetplot(kk_GSEA2, foldChange=geneList_smb)
    ggsave( paste("KEGG/",Sample_Dir, File_name, "_genes", ".png", sep="" ), w = 20, h = 10.9 )
}
try(Plot_and_Save(),silent=TRUE)


sig_genes_kk =  paste("Dmel", TMP$FLYBASECG, sep="_")

# over-representation analysis
File_name = paste("Enrichment", Pvalue, logFC, sep="_" )
kk_Enrich <- enrichKEGG(gene = sig_genes_kk,
                   universe = names(geneList_kk),
                   organism = 'dme',
                   pvalueCutoff = 0.05)
write.csv(kk_Enrich, paste("KEGG/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)

ggplot(kk_Enrich@result, aes(x=Count, y=Description)) +
    geom_point(aes(size=GeneRatio, color=pvalue)) + theme_bw()
ggsave_GO_enrich(paste("KEGG/",Sample_Dir, File_name, ".png", sep="" ), nrow(kk_Enrich@result))


Plot_and_Save <-function(){
    cnetplot(kk_Enrich, foldChange=geneList_kk)
    ggsave( paste("KEGG/",Sample_Dir, File_name, "_genes", ".png", sep="" ), w = 20, h = 10.9 )
}
try(Plot_and_Save(),silent=TRUE)


neList_kk <- Gene_list$logFC
names(geneList_kk) <-Gene_list$ENTREZID
geneList_kk <- sort(geneList_kk, decreasing = T)

library("pathview")
#dme04624 <- pathview(gene.data  = geneList_kk,
#                        pathway.id = "04624",
#                        species    = "dme",
#                        out.prefix = paste("KEGG/",Sample_Dir, sep=""),
#                        limit      =2.5)

## WikiPathways
File_name = paste("Enrichment", Pvalue, logFC, sep="_" )
WikiP_enrich <- enrichWP(sig_genes, universe = names(geneList_kk), organism = "Drosophila melanogaster")
ggplot(WikiP_enrich@result, aes(x=Count, y=Description)) +
    geom_point(aes(size=GeneRatio, color=pvalue)) + theme_bw()
ggsave_GO_enrich(paste("WIKI/",Sample_Dir, File_name, ".png", sep="" ), nrow(kk_Enrich@result))

WikiP_enrich2 <- EN2SY(WikiP_enrich, "geneID")
write.csv(WikiP_enrich2@result, paste("WIKI/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)

Plot_and_Save <-function(){
    cnetplot(WikiP_enrich2, foldChange=geneList_smb)
    ggsave( paste("WIKI/",Sample_Dir, File_name, "_genes", ".png", sep="" ), w = 20, h = 10.9 )
}
try(Plot_and_Save(),silent=TRUE)


File_name = paste("GSEA",Pvalue, logFC, sep="_" )
WikiP_gse <- gseWP(geneList, organism = "Drosophila melanogaster")
WikiP_gse2 <- EN2SY(WikiP_gse, "core_enrichment")

if (nrow(WikiP_gse@result) >0 ){
    for (i in c(1:nrow(WikiP_gse@result))){
        gseaplot2(WikiP_gse, geneSetID = i, title = WikiP_gse$Description[i], color = 'salmon', pvalue_table = TRUE)
        ggsave(paste("WIKI/",Sample_Dir, File_name,  "_", WikiP_gse@result$ID[i], "_GSEA", ".png", sep="" ), w = 7.5, h = 5)
    }
}

write.csv(WikiP_gse2@result, paste("WIKI/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)

Plot_and_Save <-function(){
    ridgeplot(WikiP_gse)
    ridgeplot_save(paste("WIKI/",Sample_Dir, File_name, "_", GROUP,"_ridgeplot.png", sep="" ), nrow(WikiP_gse@result))
}
try(Plot_and_Save(),silent=TRUE)



## Reactome Pathway
library(ReactomePA)
File_name = paste("Enrichment", Pvalue, logFC, sep="_" )
Reactome_enrich <- enrichPathway(gene= sig_genes, pvalueCutoff = 0.05,
    readable=TRUE, organism ="fly", universe = names(geneList))
write.csv(Reactome_enrich, paste("Reactome/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)

Reactome_enrich@result <- Reactome_enrich@result[Reactome_enrich@result$pvalue<=0.05,]
ggplot(Reactome_enrich@result, aes(x=Count, y=Description)) +
    geom_point(aes(size=GeneRatio, color=pvalue)) + theme_bw()
ggsave_GO_enrich(paste("Reactome/",Sample_Dir, File_name, ".png", sep="" ), nrow(kk_Enrich@result))


File_name = paste("GSEA",Pvalue, logFC, sep="_" )
Reactome_gse <- gsePathway(geneList,
                pvalueCutoff = 0.05,
                organism = "fly",
                pAdjustMethod = "BH")
# [Reactome_gse ridgeplot]
ridgeplot(Reactome_gse)
ridgeplot_save(paste("Reactome/",Sample_Dir, File_name, "_", GROUP,"_ridgeplot.png", sep="" ), nrow(Reactome_gse@result))
write.csv(Reactome_gse, paste("Reactome/",Sample_Dir, File_name, ".csv", sep="" ), row.names=F)


for (i in c(1:nrow(Reactome_gse@result))){
    gseaplot2(Reactome_gse, geneSetID = i, title = Reactome_gse$Description[i], color = 'salmon', pvalue_table = TRUE)
    ggsave(paste("Reactome/",Sample_Dir, File_name,  "_", Reactome_gse@result$ID[i], "_GSEA", ".png", sep="" ), w = 7.5, h = 5)
}

123
123
GO ontology plot
GO Enrichment plot
GO Network category
GO Network Genes
GO GSEA upsetplot
GO GSEA Escore
pathview
Reactome_gse ridgeplot 
##############
####eg
#############

eg = bitr(x, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
eg = bitr(x, fromType="ENTREZID", toType="SYMBOL", OrgDb="org.Hs.eg.db")
head(eg)

gene <- eg[[2]]


barplot(ggio, drop=TRUE, showCategory=122)
#####################################################################



###bitr_kegg: converting biological IDs using KEGG API

data(x)
hg <- x[[1]]
head(hg)
eg2np <- bitr_kegg(hg, fromType='kegg', toType='ncbi-proteinid', organism='hsa')
head(eg2np)
bitr_kegg("Z5100", fromType="kegg", toType='ncbi-geneid', organism='ece')
bitr_kegg("Z5100", fromType="kegg", toType='ncbi-proteinid', organism='ece')
bitr_kegg("Z5100", fromType="kegg", toType='uniprot', organism='ece')

################################################################################
############ GO classification
################################################################################
gene <- names(geneList)[abs(geneList) > 2]

gobp <- groupGO(gene     = gene,
               OrgDb    = org.Hs.eg.db,
               ont      = "BP",
               level    = 2,
               readable = TRUE)

head(ggo)

go <- groupGO(gene = gene, OrgDb = org.Hs.eg.db, ont = "BP", level = 2, readable = TRUE)
barplot(go, drop=TRUE, showCategory=122)
##############################
GO over-representation test
###############################
ego1 <- enrichGO(gene =gene, universe= names(geneList), ont = "CC",pAdjustMethod = "BH",pvalueCutoff  = 0.01,qvalueCutoff = 0.05,readable = TRUE)
ego2 <- enrichGO(gene =gene, universe= names(geneList), ont = "BP",pAdjustMethod = "BH",pvalueCutoff  = 0.01,qvalueCutoff = 0.05,readable = TRUE)
ego3 <- enrichGO(gene =gene, universe= names(geneList), ont = "MF",pAdjustMethod = "BH",pvalueCutoff  = 0.01,qvalueCutoff = 0.05,readable = TRUE)

head(ego)
###################################
##################################
##################################
source("http://bioconductor.org/biocLite.R")
library(clusterProfiler)
library(org.Hs.eg.db)
keytypes(org.Hs.eg.db)





KEGG
kk <- enrichKEGG(gene = gene, organism = 'human', pvalueCutoff = 0.05)

head(kk)
barplot(kk, drop=TRUE, showCategory=122)


kk2 <- gseKEGG(geneList = geneList, organism = 'hsa', nPerm = 1000, minGSSize = 120, pvalueCutoff = 100,verbose = FALSE)
ead(kk2)

####
kk2 <- gseKEGG(geneList = geneList, organism = 'hsa', nPerm = 10, minGSSize = 10, pvalueCutoff = 100,verbose = FALSE)

gseaplot(kk2, geneSetID = "hsa05200")

hsa05205
hsa05165
##################################################################
#############pathview
##################################################################

library(clusterProfiler)
library(org.Hs.eg.db)
library("pathview")

hsa05418
 p  <- pathview(gene.data  = geneList,
                     pathway.id = "hsa04976",
                     species    = "hsa",
                     limit      = list(gene=max(abs(geneList)), cpd=1))

pathview(gene.data  = geneList, pathway.id = "hsa04976", species = "hsa", limit = list(gene=max(5), cpd=1))
pathview(gene.data  = geneList, pathway.id = i, species = "hsa", limit = list(gene=max(5), cpd=1))


####################################################################
####geneList
####################################################################


d = read.csv(your_csv_file)

d = read.table("geneList")
### assume 1st column is ID
### 2nd column is FC
### feature 1: numeric vector

geneList = d[,2]
names(geneList) = as.character(d[,1])  ## feature 2: named vector
geneList = sort(geneList, decreasing = TRUE)   ## feature 3: decreasing order
head(geneList)

eg = bitr(x, fromType="ENTREZID", toType="SYMBOL", annoDb="org.Hs.eg.db")

 p  <- pathview(gene.data  = geneList, pathway.id = "hsa04668", species    = "hsa", limit      = list(gene=max(abs(geneList)), cpd=1))


gene <- names(geneList)[abs(geneList) > 2]
gene.df <- bitr(gene, fromType = "ENTREZID",
        toType = c("ENSEMBL", "SYMBOL"),
        OrgDb = org.Hs.eg.db)
head(gene.df)




ego2 <- enrichGO(gene         = gene.df$ENSEMBL,
                OrgDb         = org.Hs.eg.db,
        keytype       = 'ENSEMBL',
                ont           = "CC",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.01,
                qvalueCutoff  = 0.05)
ego2 <- setReadable(ego2, OrgDb = org.Hs.eg.db)


biocLite("topGO")




barplot(ggo, drop=TRUE, showCategory=12)

###############################################################
##
###############################################################
geneList = d[,2]
names(geneList) = as.character(d[,1])  ## feature 2: named vector
geneList = sort(geneList, decreasing = TRUE)   ## feature 3: decreasing order
head(geneList)

kk2 <- gseKEGG(geneList     = geneList,
               organism     = 'hsa',
               nPerm        = 1000,
               minGSSize    = 120,
               pvalueCutoff = 0,
               verbose      = FALSE)
gseaplot(kk2, geneSetID = "hsa04668")


#######################
####write the results
#######################

write.csv(fortify(kk,showCategory=120),file="kk.matrix",quote=F,sep='\t')









##################################

a <- c("3","7","14","21")
b <- read.table("single_symol.matrix")

for(i in a)
 {
 x <- read.table("3")
 probes= x$V1
 probes1=match(probes,x$V1)
 probes2=match(probes,b$V1)
 sum(is.na(probes1))
 sum(is.na(probes1))
 List=data.frame(FC=(x$V4[probes1]* -1),S=b$V2[probes2],Co=b$V1[probes2])
 List=data.frame(Symbol=List$S, FC=List$FC )
 A<- List[order(List[,2],decreasing=F),]
 write.table(A,file=paste(i,"-tesst.txt",sep=''),sep='\t',quote=F,row.names=F)
 }

for (i in b[[1]])
 {
  probes= i
 probes1= match(probes,b$V1)
  c=b$V2[probes1]
 png(file=paste(i,"-",c,"-GSEA.png",sep=''),wi=400,he=400)
 gseaplot(kk2, geneSetID = i,title=paste(c,"0dpeVs7dpe"))
 dev.off()
 }


 for (i in b[[1]])
 {
 p  <- pathview(gene.data  = geneList,
 pathway.id = i,
  species    = "hsa",
                   limit      = list(gene=max(abs(geneList)), cpd=1))
 }


geneList=data.frame(List=List$Symbol,FC=List$FC* -1)


 png(file=paste(i,"-",title,".png",sep=''),wi=900,he=900)
 gseaplot(kk2, geneSetID = i,title= T)
 dev.off()}


a <- read.table("list")
 x <- a[[1]]
eg = bitr(x, fromType="ENTREZID", toType="SYMBOL", annoDb="org.Hs.eg.db")
dim(eg)
write.table(eg,file="",row.names=F,quote=F,sep='\t')

Error in installation

config.status: creating src/Makevars.tmp
config.status: creating src/Makevars
config.status: creating src/config.h
** libs
gfortran -fno-optimize-sibling-calls  -fpic  -g -O2  -c AMD/Source/amd.f -o AMD/Source/amd.o
make: gfortran: No such file or directory
make: *** [/usr/lib64/R/etc/Makeconf:195: AMD/Source/amd.o] Error 127
ERROR: compilation failed for package 'igraph'
* removing '/home/karobben/R/x86_64-pc-linux-gnu-library/4.0/igraph'
During startup - Warning message:

problem: lack of gcc-fortran

sudo pacman -S gcc-fortran

Error in using

fail to download KEGG data...
Error in download.KEGG.Path(species) : 
  'species' should be one of organisms listed in 'http://www.genome.jp/kegg/catalog/org_list.html'...
Calls: gseKEGG ... prepare_KEGG -> download_KEGG -> download.KEGG.Path
In addition: Warning message:
In download.file(url, method = method, ...) :
  URL 'https://rest.kegg.jp/link/hsa/pathway': status was 'Failure when receiving data from the peer'
Execution halted

This is such an annoying issue. The weird thing is that everything works fine when I use the code in the terminal, but as soon as I write the code into a script, it just doesn’t seem to work. The author suggests that the issue could be resolved by updating to the latest version. However, this would mean that I would also need to update either the Bioconductor or R base. I don’t want to update anything, I just want the job to be done. Thankfully, Slohr’s answer in 2022 provided a perfect solution to this problem.

## https://www.genome.jp/kegg/rest/keggapi.html
## kegg_link('hsa', 'pathway')
ttp_kegg_link <- function(target_db, source_db) {
    url <- paste0("https://rest.kegg.jp/link/", target_db, "/", source_db, collapse="")
    local_mydownload <- function (url, method, quiet = TRUE, ...) 
    {
        if (capabilities("libcurl")) {
            dl <- tryCatch(utils::download.file(url, quiet = quiet, 
                method = "libcurl", ...), error = function(e) NULL)
        }
        else {
            dl <- tryCatch(downloader::download(url, quiet = TRUE, 
                method = method, ...), error = function(e) NULL)
        }
        return(dl)
    }
    local_kegg_rest <- function (rest_url) 
    {
        message("Reading KEGG annotation online:\n")
        f <- tempfile()
        dl <- local_mydownload(rest_url, destfile = f)
        if (is.null(dl)) {
            message("fail to download KEGG data...")
            return(NULL)
        }
        content <- readLines(f)
        content %<>% strsplit(., "\t") %>% do.call("rbind", .)
        res <- data.frame(from = content[, 1], to = content[, 2])
        return(res)
    }
    local_kegg_rest(url)
}

ttp_kegg_list <- function(db) {
    url <- paste0("https://rest.kegg.jp/list/", db, collapse="")
    local_mydownload <- function (url, method, quiet = TRUE, ...) 
    {
        if (capabilities("libcurl")) {
            dl <- tryCatch(utils::download.file(url, quiet = quiet, 
                method = "libcurl", ...), error = function(e) NULL)
        }
        else {
            dl <- tryCatch(downloader::download(url, quiet = TRUE, 
                method = method, ...), error = function(e) NULL)
        }
        return(dl)
    }
    local_kegg_rest <- function (rest_url) 
    {
        message("Reading KEGG annotation online:\n")
        f <- tempfile()
        dl <- local_mydownload(rest_url, destfile = f)
        if (is.null(dl)) {
            message("fail to download KEGG data...")
            return(NULL)
        }
        content <- readLines(f)
        content %<>% strsplit(., "\t") %>% do.call("rbind", .)
        res <- data.frame(from = content[, 1], to = content[, 2])
        return(res)
    }
    local_kegg_rest(url)
}

rlang::env_unlock(env = asNamespace('clusterProfiler'))
rlang::env_binding_unlock(env = asNamespace('clusterProfiler'))
assign('kegg_link', ttp_kegg_link, envir = asNamespace('clusterProfiler'))
assign('kegg_list', ttp_kegg_list, envir = asNamespace('clusterProfiler'))
rlang::env_binding_lock(env = asNamespace('clusterProfiler'))
rlang::env_lock(asNamespace('clusterProfiler'))

clusterProfiler

https://karobben.github.io/2020/06/14/R/clusterProfiler/

Author

Karobben

Posted on

2020-06-14

Updated on

2024-01-11

Licensed under

#

Like this article? Support the author with

Comments

Follow

Tags

3D8
AI3
API3
ATAC-Seq1
Advanced Mathematics1
Aging1
Algorithm1
Align4
Android4
Antibody6
Audio1
Behavior2
BiliBili2
BioStatistics4
Biochemistry43
Biochmistry1
Bioinformatic5
Bioinformatics81
Biology46
Biopython5
Book2
CLI Tools15
Cancer1
Cell2
Cell Biology24
Chip-Seq3
Classes74
Cluster6
Conky2
Crawler5
Data14
Data Science16
Database10
Deep Learning1
Deepin1
Disk1
Distribution2
Dme3
English5
FTP1
Fasta3
Fish17
GATK1
GO3
Genome8
GitPage3
Graphviz1
HTML14
Hexo19
Hexo Plugin6
IDE3
Image10
Immunology8
Interoperable Plot2
Japanese1
Jupyter Notebook1
KEGG4
Kivy31
Latex1
Links1
Linux43
Logistic1
Machine Learning29
MarkDown7
MateGenome2
Math3
Matrix2
Meta-omics1
Metabloism1
Metabolism14
Mice1
Mind Plot1
Molecular Dynamic Simulation1
Mysql2
NGS33
Network5
Neuron1
Notes3
Nutrition matrix1
OpenCV17
Others3
PCA1
PDF1
PacBio1
Paper3
Papers22
Physics2
Plot65
Protein5
Protein Structure2
Proteomic1
Protocol9
PyMol2
Python131
Python Plot3
Python Seaborn1
Pyvista2
QC4
R88
R packages1
RNA-Seq24
RNN1
RasperryPi2
Regression4
Response Surface Methodology1
SNP4
Sanger Sequencing5
Scientific American1
Script7
Scripting13
Scripts2
Sequencing1
Software48
Statistic14
Super Conductor1
Synthetic Cell1
System8
TCGA1
TUI4
Tensorflow2
Termux1
Tools5
Transfer Learning1
Translation1
Trinity2
Tulane Classes39
Tutorials1
UIUC Classes8
Urwid4
VCF1
Video1
Vocabulary3
WGCNA5
WGS5
Waque3
Web1
Wet2
WordCloud2
Writing2
Yuque3
abi1
api4
apt1
bash19
blast1
bug1
dock1
email2
flybase1
ggkaboom1
ggplot45
ggplot_splitbar1
git1
map5
miRNA-Seq2
nCov2
openMM1
orc1
protein1
scATAC-Seq1
scRNA-Seq9
torch1

Subscribe for updates

Links

Recommends

Categories

×