def importKEGGAssignments(outfile, mart, host, biomart_dataset):
'''import the KEGG annotations from the R KEGG.db annotations
package. Note that since KEGG is no longer publically availible,
this is not up-to-date and maybe removed from bioconductor in
future releases
'''
R.library("KEGG.db")
E.info("getting entrez to ensembl mapping ...")
entrez2ensembl = PipelineBiomart.biomart_iterator(
("ensembl_gene_id", "entrezgene"),
biomart=mart,
dataset=biomart_dataset,
host=host,
path="/biomart/martservice")
entrez2ensembl = dict(
(x['entrezgene'], x['ensembl_gene_id']) for x in entrez2ensembl)
E.info("Done")
E.info("getting entrez to kegg mapping ... ")
entrez2path = R('as.list(KEGGEXTID2PATHID)')
E.info("Done")
E.info("Getting KEGG names")
pathnames = R('as.list(KEGGPATHID2NAME)')
pathid2name = dict(zip(pathnames.names, R.unlist(pathnames)))
E.info("Done")
outf = IOTools.openFile(outfile, "w")
outf.write("ontology\tgene_id\tkegg_ID\tkegg_name\tevidence\n")
# rx2 did not work in rpy2 2.4.2 - workaround uses
# absolute indices
for gene_column, gene in enumerate(entrez2path.names):
try:
gene = int(gene)
except ValueError:
continue
if gene in entrez2ensembl:
ensid = entrez2ensembl[gene]
else:
continue
for pathway in entrez2path[gene_column]:
pathid = re.match("[a-z]+([0-9]+)", pathway).groups()[0]
pathname = pathid2name[pathid]
outf.write("\t".join(["kegg", ensid,
str(pathway), pathname, "NA"]) + "\n")
def importKEGGAssignments(outfile, mart, host, biomart_dataset):
''' import the KEGG annotations from the R KEGG.db
annotations package. Note that since KEGG is no longer
publically availible, this is not up-to-date and maybe removed
from bioconductor in future releases '''
R.library("KEGG.db")
R.library("biomaRt")
E.info("getting entrez to ensembl mapping ...")
mart = R.useMart(biomart=mart,
host=host,
path="/biomart/martservice",
dataset=biomart_dataset)
entrez2ensembl = R.getBM(attributes=ro.StrVector(
["ensembl_gene_id", "entrezgene"]),
mart=mart)
entrez = entrez2ensembl.rx2("entrezgene")
ensembl = entrez2ensembl.rx2("ensembl_gene_id")
entrez2ensembl = dict(zip(entrez, ensembl))
E.info("Done")
E.info("getting entrez to kegg mapping ... ")
entrez2path = R('as.list(KEGGEXTID2PATHID)')
E.info("Done")
E.info("Getting KEGG names")
pathnames = R('as.list(KEGGPATHID2NAME)')
pathid2name = dict(zip(pathnames.names, R.unlist(pathnames)))
E.info("Done")
outf = IOTools.openFile(outfile, "w")
outf.write("ontology\tgene_id\tkegg_ID\tkegg_name\tevidence\n")
for gene in entrez2path.names:
try:
gene = int(gene)
except ValueError:
continue
if gene in entrez2ensembl:
ensid = entrez2ensembl[gene]
else:
continue
for pathway in entrez2path.rx2(str(gene)):
pathid = re.match("[a-z]+([0-9]+)", pathway).groups()[0]
pathname = pathid2name[pathid]
outf.write("\t".join(["kegg", ensid,
str(pathway), pathname, "NA"]) + "\n")
def importKEGGAssignments(outfile, mart, host, biomart_dataset):
''' import the KEGG annotations from the R KEGG.db
annotations package. Note that since KEGG is no longer
publically availible, this is not up-to-date and maybe removed
from bioconductor in future releases '''
R.library("KEGG.db")
R.library("biomaRt")
E.info("getting entrez to ensembl mapping ...")
mart = R.useMart(biomart=mart,
host=host,
path="/biomart/martservice",
dataset=biomart_dataset)
entrez2ensembl = R.getBM(attributes=ro.StrVector(["ensembl_gene_id", "entrezgene"]),
mart=mart)
entrez = entrez2ensembl.rx2("entrezgene")
ensembl = entrez2ensembl.rx2("ensembl_gene_id")
entrez2ensembl = dict(zip(entrez, ensembl))
E.info("Done")
E.info("getting entrez to kegg mapping ... ")
entrez2path = R('as.list(KEGGEXTID2PATHID)')
E.info("Done")
E.info("Getting KEGG names")
pathnames = R('as.list(KEGGPATHID2NAME)')
pathid2name = dict(zip(pathnames.names, R.unlist(pathnames)))
E.info("Done")
outf = IOTools.openFile(outfile, "w")
outf.write("ontology\tgene_id\tkegg_ID\tkegg_name\tevidence\n")
for gene in entrez2path.names:
try:
gene = int(gene)
except ValueError:
continue
if gene in entrez2ensembl:
ensid = entrez2ensembl[gene]
else:
continue
for pathway in entrez2path.rx2(str(gene)):
pathid = re.match("[a-z]+([0-9]+)", pathway).groups()[0]
pathname = pathid2name[pathid]
outf.write(
"\t".join(["kegg", ensid, str(pathway), pathname, "NA"]) + "\n")
def importKEGGAssignments(outfile, mart, host, biomart_dataset):
'''import the KEGG annotations from the R KEGG.db annotations
package. Note that since KEGG is no longer publically availible,
this is not up-to-date and maybe removed from bioconductor in
future releases
'''
if not re.match("rnorvegicus|scerevisiae|hsapiens|mmusculus",
biomart_dataset):
E.warn("KEGG.db doesn't map Entrez ids for %s, %s will"
" likely be empty" % (biomart_dataset, outfile))
R.library("KEGG.db")
E.info("getting entrez to ensembl mapping ...")
entrez2ensembl = PipelineBiomart.biomart_iterator(
("ensembl_gene_id", "entrezgene"),
biomart=mart,
dataset=biomart_dataset,
host=host,
path="/biomart/martservice")
entrez2ensembl = dict((x['entrezgene'],
x['ensembl_gene_id'])
for x in entrez2ensembl)
E.info("Done")
E.info("getting entrez to kegg mapping ... ")
entrez2path = R('as.list(KEGGEXTID2PATHID)')
E.info("Done")
E.info("Getting KEGG names")
pathnames = R('as.list(KEGGPATHID2NAME)')
pathid2name = dict(zip(pathnames.names, R.unlist(pathnames)))
E.info("Done")
outf = IOTools.openFile(outfile, "w")
outf.write("ontology\tgene_id\tkegg_ID\tkegg_name\tevidence\n")
# rx2 did not work in rpy2 2.4.2 - workaround uses
# absolute indices
for gene_column, gene in enumerate(entrez2path.names):
try:
gene = int(gene)
except ValueError:
continue
if gene in entrez2ensembl:
ensid = entrez2ensembl[gene]
else:
continue
for pathway in entrez2path[gene_column]:
pathid = re.match("[a-z]+([0-9]+)", pathway).groups()[0]
pathname = pathid2name[pathid]
outf.write(
"\t".join(["kegg", ensid, str(pathway),
pathname, "NA"]) + "\n")
def importKEGGAssignments(outfile, mart, host, biomart_dataset):
'''import the KEGG annotations from the R KEGG.db annotations
package.
.. note::
Since KEGG is no longer publically available, this is not
up-to-date and maybe removed from bioconductor in future
releases
The table written to outfile has the following columns:
``ontology``, ``gene_id``, ``kegg_ID``, ``kegg_name``,
``evidence``.
Arguments
---------
outfile : string
Output filename in :term:`tsv` format.
mart : string
Name of the biomart
host : string
Host name of the biomart server
biomart_dataset : string
Biomart dataset
'''
if not re.match("rnorvegicus|scerevisiae|hsapiens|mmusculus",
biomart_dataset):
E.warn("KEGG.db doesn't map Entrez ids for %s, %s will"
" likely be empty" % (biomart_dataset, outfile))
R.library("KEGG.db")
E.info("getting entrez to ensembl mapping ...")
# Generates an iterator containing the data from biomart
entrez2ensembl = Biomart.biomart_iterator(
("ensembl_gene_id", "entrezgene"),
biomart=mart,
dataset=biomart_dataset,
host=host,
path="/biomart/martservice")
entrez2ensembl = dict((x['entrezgene'],
x['ensembl_gene_id'])
for x in entrez2ensembl)
E.info("Done")
E.info("getting entrez to kegg mapping ... ")
entrez2path = R('as.list(KEGGEXTID2PATHID)')
E.info("Done")
E.info("Getting KEGG names")
pathnames = R('as.list(KEGGPATHID2NAME)')
pathid2name = dict(list(zip(pathnames.names, R.unlist(pathnames))))
E.info("Done")
outf = IOTools.openFile(outfile, "w")
outf.write("ontology\tgene_id\tkegg_ID\tkegg_name\tevidence\n")
# rx2 did not work in rpy2 2.4.2 - workaround uses
# absolute indices
for gene_column, gene in enumerate(entrez2path.names):
try:
gene = int(gene)
except ValueError:
continue
if gene in entrez2ensembl:
ensid = entrez2ensembl[gene]
else:
continue
for pathway in entrez2path[gene_column]:
pathid = re.match("[a-z]+([0-9]+)", pathway).groups()[0]
pathname = pathid2name[pathid]
outf.write(
"\t".join(["kegg", ensid, str(pathway),
pathname, "NA"]) + "\n")
Differentially_significant_genes = nrDEG[nrDEG["P.Value"] < 0.05].query(
'''logFC>0.5849625007211562 or logFC<-0.5849625007211562''')
Differentially_significant_genes.to_csv(
f"{path}Differentially_significant_genes.txt", sep="\t")
################# 基因功能富集分析 #################
# 富集分析 https://www.jianshu.com/p/988d90484f77
# BiocManager::install("org.Hs.eg.db")
r("suppressMessages(library(clusterProfiler))")
r("suppressMessages(library(org.Hs.eg.db))")
symbol_id = robjects.StrVector(list(
Differentially_significant_genes.index))
# symbol_id 转 entre_id
entre_id = r.unlist(
r.bitr(symbol_id,
fromType="SYMBOL",
toType="ENTREZID",
OrgDb="org.Hs.eg.db").rx2('ENTREZID'))
# GO富集分析
# ALL_R = r.enrichGO(entre_id, "org.Hs.eg.db", keyType="ENTREZID", ont='ALL', pvalueCutoff=0.05, pAdjustMethod="BH",
# qvalueCutoff=0.1, readable=True) # 一步到位
BP_R = r.enrichGO(entre_id,
"org.Hs.eg.db",
keyType="ENTREZID",
ont="BP",
pvalueCutoff=0.05,
pAdjustMethod="BH",
qvalueCutoff=0.1,
readable=True) # 3种分开进行富集
MF_R = r.enrichGO(entre_id,
"org.Hs.eg.db",
