R/from_DEG_2pathways.R
In itamuria/immunoeasy: Make Immuno Therapies An Easier Process
Defines functions
from_DEG_2pathways
#' Search the most enriched pathways
#'
#' @description From DEG we want to obtain the information about the pathways
#'
#' @param res_frame Table with the DEG
#' @param wpgmtfile wpgmtfile from wikipath
#' @return Several excel file with information about enriched pathways
#' @export
#'
#' @examples
#' \dontrun{
#' from_DEG_2pathways (res_frame)
#' }
# http://yulab-smu.top/clusterProfiler-book/chapter3.html#wikipathways-analysis
from_DEG_2pathways <- function(res_frame,wpgmtfile)
{
# # Wikipath ------------------------------------------------------------
library(magrittr)
library(clusterProfiler)
# data(geneList, package="DOSE")
# gene <- names(geneList)[abs(geneList) > 2]
resframe8 <- res_frame[res_frame$padj < 0.1,]
resframe8 <- resframe8[!is.na(resframe8$trans),]
entrezid <- mapIds(org.Hs.eg.db,
keys=gsub("\\..*","",rownames(resframe8)),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
entrezid2 <- unname(entrezid)
entrezid2 <- entrezid2[!is.na(entrezid2)]
wpgmtfile <- "wikipathways-20210110-gmt-Homo_sapiens.gmt"
wp2gene <- read.gmt(wpgmtfile)
ont <- "term"
wp2gene <- wp2gene %>% tidyr::separate(ont, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
ewp <- enricher(entrezid2, TERM2GENE = wpid2gene, TERM2NAME = wpid2name)
head(ewp)
# GSEA
# res_val3 <- res_val3[order(res_val3$log2FoldChange, decreasing = TRUE),]
resframeGSEA <- res_frame[order(res_frame$log2FoldChange, decreasing = TRUE),]
resframeGSEA <- resframeGSEA[!is.na(resframeGSEA$trans),]
dGSEA <- resframeGSEA$trans
entrezidGSEA <- mapIds(org.Hs.eg.db,
keys=gsub("\\..*","",dGSEA),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
entrezid2GSEA <- unname(entrezidGSEA)
geneListGSEA <- resframeGSEA$log2FoldChange
names(geneListGSEA) <- entrezid2GSEA
entrezid2GSEA_sinNa <- !is.na(names(geneListGSEA))
geneListGSEA_sinNA <- geneListGSEA[entrezid2GSEA_sinNa]
geneListGSEA_sinNA2 <- geneListGSEA_sinNA[!is.na(geneListGSEA_sinNA)]
ewp2 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = wpid2gene, TERM2NAME = wpid2name, verbose=FALSE)
head(ewp2)
heatplot(ewp, foldChange=geneListGSEA_sinNA2)
emapplot(ewp2)
upsetplot(ewp)
library(org.Hs.eg.db)
ewp <- setReadable(ewp, org.Hs.eg.db, keyType = "ENTREZID")
ewp2 <- setReadable(ewp2, org.Hs.eg.db, keyType = "ENTREZID")
head(ewp)
# ridgeplot(ewp)
gseaplot(ewp2, geneSetID = 1, title = ewp2$Description[1])
gseaplot2(ewp2, geneSetID = 1, title = ewp2$Description[1])
gseaplot2(ewp2, geneSetID = 1:3, pvalue_table = TRUE,
color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot")
openxlsx::write.xlsx(data.frame(ewp), file = paste0("ResultsCounts2/",contraste,"_Wikipath_hipergeometrico.xlsx"))
openxlsx::write.xlsx(data.frame(ewp2), file = paste0("ResultsCounts2/",contraste,"_Wikipath_gsea.xlsx"))
# ploting
library(enrichplot)
barplot(ewp, showCategory=4)
dotplot(ewp, showCategory=30) + ggtitle("Wikipath for Progresion")
dotplot(ewp2, showCategory=30) + ggtitle("Wikipath for Progresion")
### Plot heatmap
df2 <- data.frame(ewp2)
df2_genes <- df2$core_enrichment[3]
sel_gen <- unlist(strsplit(df2_genes, "/"))
normalized_counts2 <- counts(dds, normalized=TRUE)
sample[,contraste] <- as.character(sample[,contraste])
TTPlar <- ifelse(is.na(sample[,contraste]), "NA", sample[,contraste])
group_df <- data.frame(TTPlar[sele2])
rownames(group_df) <- sample$TUMOR_RNA[sele2]
names(group_df) <- contraste
symbol <- mapIds(org.Hs.eg.db,
keys=gsub("\\..*","",rownames(normalized_counts2)),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
symbol <- unname(ifelse(is.na(symbol), names(symbol), symbol))
heatdf <- normalized_counts2[symbol %in% sel_gen,]
symbol_df <- data.frame(symbol)
rownames(symbol_df) <- rownames(heatdf)
symbol2 <- symbol[symbol %in% sel_gen]
pheatmap(heatdf, scale = "row", labels_col = sample$ID[sele2], cluster_cols = FALSE,
annotation_col = group_df, labels_row = symbol2, fontsize_row = 5)
# # Cell marker -----------------------------------------------------------
cell_markers <- vroom::vroom('http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Human_cell_markers.txt') %>%
tidyr::unite("cellMarker", tissueType, cancerType, cellName, sep=", ") %>%
dplyr::select(cellMarker, geneID) %>%
dplyr::mutate(geneID = strsplit(geneID, ', '))
cell_markers
y <- enricher(entrezid2, TERM2GENE=cell_markers, minGSSize=1)
DT::datatable(as.data.frame(y))
openxlsx::write.xlsx( data.frame(y), file = paste0("ResultsCounts2/",contraste,"_CellMarker.xlsx"))
# MSigDb
library(msigdbr)
msigdbr_show_species()
m_df <- msigdbr(species = "Homo sapiens")
head(m_df, 2) %>% as.data.frame
m_t2g <- msigdbr(species = "Homo sapiens", category = "C6") %>%
dplyr::select(gs_name, entrez_gene)
head(m_t2g)
em <- enricher(entrezid2, TERM2GENE=m_t2g)
em2 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = m_t2g)
head(em)
library(org.Hs.eg.db)
em <- setReadable(em, org.Hs.eg.db, keyType = "ENTREZID")
em2 <- setReadable(em2, org.Hs.eg.db, keyType = "ENTREZID")
head(em)
head(em2)
openxlsx::write.xlsx(data.frame(em), file = paste0("ResultsCounts2/",contraste,"_MSigDb_hipergeometrico.xlsx"))
openxlsx::write.xlsx(data.frame(em2), file = paste0("ResultsCounts2/",contraste,"_MSigDb_gsea.xlsx"))
# C-s
m_t2g <- msigdbr(species = "Homo sapiens", category = "C2") %>%
dplyr::select(gs_name, entrez_gene)
head(m_t2g)
em3 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = m_t2g)
em3 <- setReadable(em3, org.Hs.eg.db, keyType = "ENTREZID")
d9 <- data.frame(em3)
gseaplot(em3, geneSetID = 7, title = em3$Description[7])
for(call in c(paste0("C",1:7),"H"))
{
print(call)
m_t2g <- NULL
m_t2g <- msigdbr(species = "Homo sapiens", category = call) %>%
dplyr::select(gs_name, entrez_gene)
em3 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = m_t2g)
em3 <- setReadable(em3, org.Hs.eg.db, keyType = "ENTREZID")
# openxlsx::write.xlsx(data.frame(em3), file = paste0("ResultsCounts2/",contraste,"_MSigDb_gsea_",call,".xlsx"))
}
# Disease
library(DOSE)
x <- enrichDO(gene = entrezid2,
ont = "DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe = names(geneListGSEA_sinNA2),
minGSSize = 5,
maxGSSize = 500,
qvalueCutoff = 0.05,
readable = FALSE)
head(x)
x <- setReadable(x, org.Hs.eg.db, keyType = "ENTREZID")
gene2 <- names(geneListGSEA_sinNA2)[abs(geneListGSEA_sinNA2) < 3]
ncg <- enrichNCG(gene2)
head(ncg)
dgn <- enrichDGN(entrezid2)
dgn(dgn)
dgn <- setReadable(dgn, org.Hs.eg.db, keyType = "ENTREZID")
openxlsx::write.xlsx(data.frame(dgn), file = paste0("ResultsCounts2/",contraste,"_dgn_.xlsx"))
library(DOSE)
y <- gseDO(geneListGSEA_sinNA2,
nPerm = 100000,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
head(y, 3)
openxlsx::write.xlsx(data.frame(y), file = paste0("ResultsCounts2/",contraste,"_gseDO_gsea.xlsx"))
ncg1 <- enrichDGN(entrezid2)
ncg2 <- gseNCG(geneListGSEA_sinNA2,
nPerm = 10000,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
ncg1 <- setReadable(ncg1, 'org.Hs.eg.db')
ncg2 <- setReadable(ncg2, 'org.Hs.eg.db')
head(ncg1, 3)
openxlsx::write.xlsx(data.frame(ncg1), file = paste0("ResultsCounts2/",contraste,"_ncg_enrich.xlsx"))
openxlsx::write.xlsx(data.frame(ncg2), file = paste0("ResultsCounts2/",contraste,"_ncg_gsea.xlsx"))
p1 <- dotplot(ncg1, showCategory=30) + ggtitle("dotplot for Enrich")
p2 <- dotplot(ncg2, showCategory=30) + ggtitle("dotplot for GSEA")
plot_grid(p1, p2, ncol=2)
dgn <- gseDGN(geneListGSEA_sinNA2,
nPerm = 10000,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
dgn <- setReadable(dgn, 'org.Hs.eg.db')
head(dgn, 3)
openxlsx::write.xlsx(data.frame(dgn), file = paste0("ResultsCounts2/",contraste,"_dgn_gsea.xlsx"))
# Gene Ontology Analysis
library(clusterProfiler)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
gene.df <- bitr(entrezid2, fromType = "ENTREZID",
toType = c("ENSEMBL", "SYMBOL"),
OrgDb = org.Hs.eg.db)
head(gene.df)
ggo <- groupGO(gene = entrezid2,
OrgDb = org.Hs.eg.db,
ont = "CC",
level = 2,
readable = TRUE)
head(ggo)
openxlsx::write.xlsx(data.frame(ggo), file = paste0("ResultsCounts2/",contraste,"_ggo.xlsx"))
ego <- enrichGO(gene = entrezid2,
universe = names(geneListGSEA_sinNA2),
OrgDb = org.Hs.eg.db,
ont = "CC",
pAdjustMethod = "none",
readable = TRUE)
head(ego)
ego2 <- enrichGO(gene = gene.df$ENSEMBL,
OrgDb = org.Hs.eg.db,
keyType = 'ENSEMBL',
ont = "CC",
pAdjustMethod = "none")
head(ego2)
ego <- setReadable(ego, OrgDb = org.Hs.eg.db)
ego2 <- setReadable(ego2, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(ego), file = paste0("ResultsCounts2/",contraste,"_ego.xlsx"))
openxlsx::write.xlsx(data.frame(ego2), file = paste0("ResultsCounts2/",contraste,"_ego2.xlsx"))
ego3 <- gseGO(geneList = geneListGSEA_sinNA2,
OrgDb = org.Hs.eg.db,
ont = "CC",
nPerm = 1000,
minGSSize = 100,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
ego3 <- setReadable(ego3, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(ego3), file = paste0("ResultsCounts2/",contraste,"_ego3.xlsx"))
# KEGG
# data(geneList, package="DOSE")
# gene <- names(geneList)[abs(geneList) > 2]
kk <- enrichKEGG(gene = entrezid2,
organism = 'hsa',
pvalueCutoff = 0.05)
kk <- setReadable(ego3, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(kk), file = paste0("ResultsCounts2/",contraste,"_KEGG_enrich.xlsx"))
kk2 <- gseKEGG(geneList = geneListGSEA_sinNA2,
organism = 'hsa',
nPerm = 1000,
minGSSize = 120,
pvalueCutoff = 0.05,
verbose = FALSE)
head(kk2)
# kk2 <- setReadable(kk2, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(kk2), file = paste0("ResultsCounts2/",contraste,"_KEGG_gsea.xlsx"))
mkk <- enrichMKEGG(gene = entrezid2,
organism = 'hsa')
# mkk <- setReadable(mkk, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(mkk), file = paste0("ResultsCounts2/",contraste,"_KEGG_mkk.xlsx"))
mkk2 <- gseMKEGG(geneList = geneListGSEA_sinNA2,
organism = 'hsa')
# mkk2 <- setReadable(mkk2, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(mkk2), file = paste0("ResultsCounts2/",contraste,"_KEGG_mkk2_gsea.xlsx"))
# REACTOME
library(GOSemSim)
hsGO <- godata('org.Hs.eg.db', ont="MF")
library(AnnotationHub)
hub <- AnnotationHub()
q <- query(hub, "Cricetulus")
id <- q$ah_id[length(q)]
Cgriseus <- hub[[id]]
goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Jiang")
goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Wang")
go1 = c("GO:0004022","GO:0004024","GO:0004174")
go2 = c("GO:0009055","GO:0005515")
mgoSim(go1, go2, semData=hsGO, measure="Wang", combine=NULL)
mgoSim(go1, go2, semData=hsGO, measure="Wang", combine="BMA")
geneSim("241", "251", semData=hsGO, measure="Wang", combine="BMA")
hsGO2 <- godata('org.Hs.eg.db', keytype = "ENTREZID", ont="MF", computeIC=FALSE)
# genes <- c("CDC45", "MCM10", "CDC20", "NMU", "MMP1")
mgeneSim(entrezid2[1:4], semData=hsGO2, measure="Wang", combine="BMA", verbose=FALSE)
library(ReactomePA)
x <- enrichPathway(gene=entrezid2, pvalueCutoff = 0.05, readable=TRUE)
head(x)
openxlsx::write.xlsx(data.frame(x), file = paste0("ResultsCounts2/",contraste,"_ReactomePA_hiper.xlsx"))
y <- gsePathway(geneListGSEA_sinNA2,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
head(y)
# library(graphite)
# viewPathway(" The citric acid (TCA) cycle and respiratory electron transport",
# readable = TRUE,
# foldChange = geneListGSEA_sinNA2)
library("pathview")
hsa04080 <- pathview(gene.data = geneListGSEA_sinNA2,
pathway.id = "hsa04080",
species = "hsa",
limit = list(gene=max(abs(geneListGSEA_sinNA2)), cpd=1))
# ------------------------------------------------------------------------
}
itamuria/immunoeasy documentation built on Sept. 30, 2022, 5:53 a.m.
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Defines functions from_DEG_2pathways
#' Search the most enriched pathways
#'
#' @description From DEG we want to obtain the information about the pathways
#'
#' @param res_frame Table with the DEG
#' @param wpgmtfile wpgmtfile from wikipath
#' @return Several excel file with information about enriched pathways
#' @export
#'
#' @examples
#' \dontrun{
#' from_DEG_2pathways (res_frame)
#' }
# http://yulab-smu.top/clusterProfiler-book/chapter3.html#wikipathways-analysis
from_DEG_2pathways <- function(res_frame,wpgmtfile)
{
# # Wikipath ------------------------------------------------------------
library(magrittr)
library(clusterProfiler)
# data(geneList, package="DOSE")
# gene <- names(geneList)[abs(geneList) > 2]
resframe8 <- res_frame[res_frame$padj < 0.1,]
resframe8 <- resframe8[!is.na(resframe8$trans),]
entrezid <- mapIds(org.Hs.eg.db,
keys=gsub("\\..*","",rownames(resframe8)),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
entrezid2 <- unname(entrezid)
entrezid2 <- entrezid2[!is.na(entrezid2)]
wpgmtfile <- "wikipathways-20210110-gmt-Homo_sapiens.gmt"
wp2gene <- read.gmt(wpgmtfile)
ont <- "term"
wp2gene <- wp2gene %>% tidyr::separate(ont, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
ewp <- enricher(entrezid2, TERM2GENE = wpid2gene, TERM2NAME = wpid2name)
head(ewp)
# GSEA
# res_val3 <- res_val3[order(res_val3$log2FoldChange, decreasing = TRUE),]
resframeGSEA <- res_frame[order(res_frame$log2FoldChange, decreasing = TRUE),]
resframeGSEA <- resframeGSEA[!is.na(resframeGSEA$trans),]
dGSEA <- resframeGSEA$trans
entrezidGSEA <- mapIds(org.Hs.eg.db,
keys=gsub("\\..*","",dGSEA),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
entrezid2GSEA <- unname(entrezidGSEA)
geneListGSEA <- resframeGSEA$log2FoldChange
names(geneListGSEA) <- entrezid2GSEA
entrezid2GSEA_sinNa <- !is.na(names(geneListGSEA))
geneListGSEA_sinNA <- geneListGSEA[entrezid2GSEA_sinNa]
geneListGSEA_sinNA2 <- geneListGSEA_sinNA[!is.na(geneListGSEA_sinNA)]
ewp2 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = wpid2gene, TERM2NAME = wpid2name, verbose=FALSE)
head(ewp2)
heatplot(ewp, foldChange=geneListGSEA_sinNA2)
emapplot(ewp2)
upsetplot(ewp)
library(org.Hs.eg.db)
ewp <- setReadable(ewp, org.Hs.eg.db, keyType = "ENTREZID")
ewp2 <- setReadable(ewp2, org.Hs.eg.db, keyType = "ENTREZID")
head(ewp)
# ridgeplot(ewp)
gseaplot(ewp2, geneSetID = 1, title = ewp2$Description[1])
gseaplot2(ewp2, geneSetID = 1, title = ewp2$Description[1])
gseaplot2(ewp2, geneSetID = 1:3, pvalue_table = TRUE,
color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot")
openxlsx::write.xlsx(data.frame(ewp), file = paste0("ResultsCounts2/",contraste,"_Wikipath_hipergeometrico.xlsx"))
openxlsx::write.xlsx(data.frame(ewp2), file = paste0("ResultsCounts2/",contraste,"_Wikipath_gsea.xlsx"))
# ploting
library(enrichplot)
barplot(ewp, showCategory=4)
dotplot(ewp, showCategory=30) + ggtitle("Wikipath for Progresion")
dotplot(ewp2, showCategory=30) + ggtitle("Wikipath for Progresion")
### Plot heatmap
df2 <- data.frame(ewp2)
df2_genes <- df2$core_enrichment[3]
sel_gen <- unlist(strsplit(df2_genes, "/"))
normalized_counts2 <- counts(dds, normalized=TRUE)
sample[,contraste] <- as.character(sample[,contraste])
TTPlar <- ifelse(is.na(sample[,contraste]), "NA", sample[,contraste])
group_df <- data.frame(TTPlar[sele2])
rownames(group_df) <- sample$TUMOR_RNA[sele2]
names(group_df) <- contraste
symbol <- mapIds(org.Hs.eg.db,
keys=gsub("\\..*","",rownames(normalized_counts2)),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
symbol <- unname(ifelse(is.na(symbol), names(symbol), symbol))
heatdf <- normalized_counts2[symbol %in% sel_gen,]
symbol_df <- data.frame(symbol)
rownames(symbol_df) <- rownames(heatdf)
symbol2 <- symbol[symbol %in% sel_gen]
pheatmap(heatdf, scale = "row", labels_col = sample$ID[sele2], cluster_cols = FALSE,
annotation_col = group_df, labels_row = symbol2, fontsize_row = 5)
# # Cell marker -----------------------------------------------------------
cell_markers <- vroom::vroom('http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Human_cell_markers.txt') %>%
tidyr::unite("cellMarker", tissueType, cancerType, cellName, sep=", ") %>%
dplyr::select(cellMarker, geneID) %>%
dplyr::mutate(geneID = strsplit(geneID, ', '))
cell_markers
y <- enricher(entrezid2, TERM2GENE=cell_markers, minGSSize=1)
DT::datatable(as.data.frame(y))
openxlsx::write.xlsx( data.frame(y), file = paste0("ResultsCounts2/",contraste,"_CellMarker.xlsx"))
# MSigDb
library(msigdbr)
msigdbr_show_species()
m_df <- msigdbr(species = "Homo sapiens")
head(m_df, 2) %>% as.data.frame
m_t2g <- msigdbr(species = "Homo sapiens", category = "C6") %>%
dplyr::select(gs_name, entrez_gene)
head(m_t2g)
em <- enricher(entrezid2, TERM2GENE=m_t2g)
em2 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = m_t2g)
head(em)
library(org.Hs.eg.db)
em <- setReadable(em, org.Hs.eg.db, keyType = "ENTREZID")
em2 <- setReadable(em2, org.Hs.eg.db, keyType = "ENTREZID")
head(em)
head(em2)
openxlsx::write.xlsx(data.frame(em), file = paste0("ResultsCounts2/",contraste,"_MSigDb_hipergeometrico.xlsx"))
openxlsx::write.xlsx(data.frame(em2), file = paste0("ResultsCounts2/",contraste,"_MSigDb_gsea.xlsx"))
# C-s
m_t2g <- msigdbr(species = "Homo sapiens", category = "C2") %>%
dplyr::select(gs_name, entrez_gene)
head(m_t2g)
em3 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = m_t2g)
em3 <- setReadable(em3, org.Hs.eg.db, keyType = "ENTREZID")
d9 <- data.frame(em3)
gseaplot(em3, geneSetID = 7, title = em3$Description[7])
for(call in c(paste0("C",1:7),"H"))
{
print(call)
m_t2g <- NULL
m_t2g <- msigdbr(species = "Homo sapiens", category = call) %>%
dplyr::select(gs_name, entrez_gene)
em3 <- GSEA(geneListGSEA_sinNA2, TERM2GENE = m_t2g)
em3 <- setReadable(em3, org.Hs.eg.db, keyType = "ENTREZID")
# openxlsx::write.xlsx(data.frame(em3), file = paste0("ResultsCounts2/",contraste,"_MSigDb_gsea_",call,".xlsx"))
}
# Disease
library(DOSE)
x <- enrichDO(gene = entrezid2,
ont = "DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe = names(geneListGSEA_sinNA2),
minGSSize = 5,
maxGSSize = 500,
qvalueCutoff = 0.05,
readable = FALSE)
head(x)
x <- setReadable(x, org.Hs.eg.db, keyType = "ENTREZID")
gene2 <- names(geneListGSEA_sinNA2)[abs(geneListGSEA_sinNA2) < 3]
ncg <- enrichNCG(gene2)
head(ncg)
dgn <- enrichDGN(entrezid2)
dgn(dgn)
dgn <- setReadable(dgn, org.Hs.eg.db, keyType = "ENTREZID")
openxlsx::write.xlsx(data.frame(dgn), file = paste0("ResultsCounts2/",contraste,"_dgn_.xlsx"))
library(DOSE)
y <- gseDO(geneListGSEA_sinNA2,
nPerm = 100000,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
head(y, 3)
openxlsx::write.xlsx(data.frame(y), file = paste0("ResultsCounts2/",contraste,"_gseDO_gsea.xlsx"))
ncg1 <- enrichDGN(entrezid2)
ncg2 <- gseNCG(geneListGSEA_sinNA2,
nPerm = 10000,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
ncg1 <- setReadable(ncg1, 'org.Hs.eg.db')
ncg2 <- setReadable(ncg2, 'org.Hs.eg.db')
head(ncg1, 3)
openxlsx::write.xlsx(data.frame(ncg1), file = paste0("ResultsCounts2/",contraste,"_ncg_enrich.xlsx"))
openxlsx::write.xlsx(data.frame(ncg2), file = paste0("ResultsCounts2/",contraste,"_ncg_gsea.xlsx"))
p1 <- dotplot(ncg1, showCategory=30) + ggtitle("dotplot for Enrich")
p2 <- dotplot(ncg2, showCategory=30) + ggtitle("dotplot for GSEA")
plot_grid(p1, p2, ncol=2)
dgn <- gseDGN(geneListGSEA_sinNA2,
nPerm = 10000,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
dgn <- setReadable(dgn, 'org.Hs.eg.db')
head(dgn, 3)
openxlsx::write.xlsx(data.frame(dgn), file = paste0("ResultsCounts2/",contraste,"_dgn_gsea.xlsx"))
# Gene Ontology Analysis
library(clusterProfiler)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
gene.df <- bitr(entrezid2, fromType = "ENTREZID",
toType = c("ENSEMBL", "SYMBOL"),
OrgDb = org.Hs.eg.db)
head(gene.df)
ggo <- groupGO(gene = entrezid2,
OrgDb = org.Hs.eg.db,
ont = "CC",
level = 2,
readable = TRUE)
head(ggo)
openxlsx::write.xlsx(data.frame(ggo), file = paste0("ResultsCounts2/",contraste,"_ggo.xlsx"))
ego <- enrichGO(gene = entrezid2,
universe = names(geneListGSEA_sinNA2),
OrgDb = org.Hs.eg.db,
ont = "CC",
pAdjustMethod = "none",
readable = TRUE)
head(ego)
ego2 <- enrichGO(gene = gene.df$ENSEMBL,
OrgDb = org.Hs.eg.db,
keyType = 'ENSEMBL',
ont = "CC",
pAdjustMethod = "none")
head(ego2)
ego <- setReadable(ego, OrgDb = org.Hs.eg.db)
ego2 <- setReadable(ego2, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(ego), file = paste0("ResultsCounts2/",contraste,"_ego.xlsx"))
openxlsx::write.xlsx(data.frame(ego2), file = paste0("ResultsCounts2/",contraste,"_ego2.xlsx"))
ego3 <- gseGO(geneList = geneListGSEA_sinNA2,
OrgDb = org.Hs.eg.db,
ont = "CC",
nPerm = 1000,
minGSSize = 100,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
ego3 <- setReadable(ego3, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(ego3), file = paste0("ResultsCounts2/",contraste,"_ego3.xlsx"))
# KEGG
# data(geneList, package="DOSE")
# gene <- names(geneList)[abs(geneList) > 2]
kk <- enrichKEGG(gene = entrezid2,
organism = 'hsa',
pvalueCutoff = 0.05)
kk <- setReadable(ego3, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(kk), file = paste0("ResultsCounts2/",contraste,"_KEGG_enrich.xlsx"))
kk2 <- gseKEGG(geneList = geneListGSEA_sinNA2,
organism = 'hsa',
nPerm = 1000,
minGSSize = 120,
pvalueCutoff = 0.05,
verbose = FALSE)
head(kk2)
# kk2 <- setReadable(kk2, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(kk2), file = paste0("ResultsCounts2/",contraste,"_KEGG_gsea.xlsx"))
mkk <- enrichMKEGG(gene = entrezid2,
organism = 'hsa')
# mkk <- setReadable(mkk, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(mkk), file = paste0("ResultsCounts2/",contraste,"_KEGG_mkk.xlsx"))
mkk2 <- gseMKEGG(geneList = geneListGSEA_sinNA2,
organism = 'hsa')
# mkk2 <- setReadable(mkk2, OrgDb = org.Hs.eg.db)
openxlsx::write.xlsx(data.frame(mkk2), file = paste0("ResultsCounts2/",contraste,"_KEGG_mkk2_gsea.xlsx"))
# REACTOME
library(GOSemSim)
hsGO <- godata('org.Hs.eg.db', ont="MF")
library(AnnotationHub)
hub <- AnnotationHub()
q <- query(hub, "Cricetulus")
id <- q$ah_id[length(q)]
Cgriseus <- hub[[id]]
goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Jiang")
goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Wang")
go1 = c("GO:0004022","GO:0004024","GO:0004174")
go2 = c("GO:0009055","GO:0005515")
mgoSim(go1, go2, semData=hsGO, measure="Wang", combine=NULL)
mgoSim(go1, go2, semData=hsGO, measure="Wang", combine="BMA")
geneSim("241", "251", semData=hsGO, measure="Wang", combine="BMA")
hsGO2 <- godata('org.Hs.eg.db', keytype = "ENTREZID", ont="MF", computeIC=FALSE)
# genes <- c("CDC45", "MCM10", "CDC20", "NMU", "MMP1")
mgeneSim(entrezid2[1:4], semData=hsGO2, measure="Wang", combine="BMA", verbose=FALSE)
library(ReactomePA)
x <- enrichPathway(gene=entrezid2, pvalueCutoff = 0.05, readable=TRUE)
head(x)
openxlsx::write.xlsx(data.frame(x), file = paste0("ResultsCounts2/",contraste,"_ReactomePA_hiper.xlsx"))
y <- gsePathway(geneListGSEA_sinNA2,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
head(y)
# library(graphite)
# viewPathway(" The citric acid (TCA) cycle and respiratory electron transport",
# readable = TRUE,
# foldChange = geneListGSEA_sinNA2)
library("pathview")
hsa04080 <- pathview(gene.data = geneListGSEA_sinNA2,
pathway.id = "hsa04080",
species = "hsa",
limit = list(gene=max(abs(geneListGSEA_sinNA2)), cpd=1))
# ------------------------------------------------------------------------
}
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