def test_example():
"""Test GoeaResults in plotting package."""
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Gene Ontology Enrichment Analysis (GOEA)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
taxid = 10090 # Mouse study
# Load ontologies, associations, and population ids
geneids_pop = GeneID2nt_mus.keys()
geneids2symbol_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
geneids_study = geneids2symbol_study.keys()
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
go2obj = goeaobj.obo_dag
# Run GOEA on study
goea_results_all = goeaobj.run_study(geneids_study)
goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
goea_results_nt = MgrNtGOEAs(goea_results_sig).get_goea_nts_all()
assert goea_results_nt
ns2gos = get_ns2gos(goea_results_sig)
# Test plotting GOEA results
gosubdag = GoSubDag(set(r.GO for r in goea_results_sig), go2obj)
plot_results("test_plot_goids_a_goea_{NS}.png",
goea_results_sig,
id2symbol=geneids2symbol_study,
parentcnt=True,
childcnt=True)
for nss, goids in ns2gos.items():
plt_goids(gosubdag, "test_plot_goids_b_{NS}.png".format(NS=nss), goids)
plot_gos("test_plot_goids_c_{NS}.png".format(NS=nss), goids, go2obj)
def test_example(prt=sys.stdout):
"""Test GoeaResults in plotting package."""
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Gene Ontology Enrichment Analysis (GOEA)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
taxid = 10090 # Mouse study
# Load ontologies, associations, and population ids
geneids_pop = GeneID2nt_mus.keys()
geneids2symbol_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
geneids_study = geneids2symbol_study.keys()
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# Run GOEA on study
goea_results_all = goeaobj.run_study(geneids_study)
goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
goea_results_nt = MgrNtGOEAs(goea_results_sig).get_goea_nts_all()
# Test managing GOEA results
objres = GoeaResults(goea_results_sig)
run(objres, prt)
objnts = GoeaResults(goea_results_nt)
run(objnts, prt)
# Plot GOEA results
fout_img = os.path.join(REPO, "test_plot_objgoearesults_{NS}.png")
plot_results(fout_img, goea_results_sig, id2symbol=geneids2symbol_study)
def test_wr_methods(log=sys.stdout):
"""Demonstrate printing a subset of all available fields using two methods."""
# 1. Gene Ontology Enrichment Analysis
# 1a. Initialize: Load ontologies, associations, and population gene IDs
taxid = 10090 # Mouse study
geneids_pop = GeneID2nt_mus.keys() # Mouse protein-coding genes
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# 1b. Run GOEA
geneids_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
keep_if = lambda nt: getattr(nt, "p_fdr_bh") < 0.05 # keep if results are significant
goea_results = goeaobj.run_study(geneids_study, keep_if=keep_if)
# 2. Write results
# Write parameters:
# The format_string names below are the same names as in the namedtuple field_names.
prtfmt = "{GO} {NS} {level:>2} {depth:>2} {p_fdr_bh:5.2e} {study_count:>5} {name}\n"
wr_params = {
# Format for printing in text format
'prtfmt' : prtfmt,
# Format for p-values in tsv and xlsx format
'fld2fmt' : {'p_fdr_bh' : '{:8.2e}'},
# Print a subset namedtuple fields, don't print all fields in namedtuple.
'prt_flds' : get_fmtflds(prtfmt)
}
# 2a. Use the write functions inside the GOEnrichmentStudy class.
_wr_3fmt_goeaobj(goea_results, goeaobj, wr_params, log)
# 2b. Use the write functions straight from the wr_tbl package to print a list of namedtuples.
_wr_3fmt_wrtbl(goea_results, wr_params, log)
assert filecmp.cmp('nbt3102_subset_obj.tsv', 'nbt3102_subset_nt.tsv')
def test_example():
"""Run Gene Ontology Enrichment Analysis (GOEA) on Nature data."""
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Gene Ontology Enrichment Analysis (GOEA)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
taxid = 10090 # Mouse study
# Load ontologies, associations, and population ids
geneids_pop = GeneID2nt_mus.keys()
geneids2symbol_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
geneids_study = geneids2symbol_study.keys()
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# Run GOEA on study
goea_results_all = goeaobj.run_study(geneids_study)
goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
# Print GOEA results to files: With study genes printed as geneids or symbols
goeaobj.wr_xlsx("nbt3102_sig_symbols.xlsx",
goea_results_sig,
itemid2name=geneids2symbol_study)
goeaobj.wr_xlsx("nbt3102_sig_geneids.xlsx", goea_results_sig)
goeaobj.wr_xlsx("nbt3102_all_symbols.xlsx",
goea_results_all,
itemid2name=geneids2symbol_study)
goeaobj.wr_xlsx("nbt3102_all_geneids.xlsx", goea_results_all)
def _get_pvals(pvalfnc_names, prt=sys.stdout):
fisher2pvals = {}
taxid = 10090 # Mouse study
obo_dag = GODag(download_go_basic_obo(prt=prt))
geneids_pop = GeneID2nt_mus.keys()
assoc_geneid2gos = get_assoc_ncbi_taxids([taxid])
geneids_study = _get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx", prt)
for fisher in pvalfnc_names:
goeaobj = GOEnrichmentStudy(
geneids_pop, assoc_geneid2gos, obo_dag, propagate_counts=False, alpha=0.05, methods=None, pvalcalc=fisher
)
fisher2pvals[fisher] = goeaobj._get_pval_uncorr(geneids_study, prt)
return fisher2pvals
def _get_pvals(pvalfnc_names, prt=sys.stdout):
fisher2pvals = {}
taxid = 10090 # Mouse study
obo_dag = GODag(download_go_basic_obo(prt=prt))
geneids_pop = GeneID2nt_mus.keys()
assoc_geneid2gos = get_assoc_ncbi_taxids([taxid])
geneids_study = _get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx", prt)
for fisher in pvalfnc_names:
goeaobj = GOEnrichmentStudy(geneids_pop,
assoc_geneid2gos,
obo_dag,
propagate_counts=False,
alpha=0.05,
methods=None,
pvalcalc=fisher)
fisher2pvals[fisher] = goeaobj._get_pval_uncorr(geneids_study, prt)
return fisher2pvals
def get_goea_results(keep_if=None):
"""Demonstrate printing a subset of all available fields using two methods."""
if keep_if is None:
keep_if = lambda nt: getattr(nt, "p_fdr_bh") < 0.05 # keep if results are significant
# 1. Gene Ontology Enrichment Analysis
# 1a. Initialize: Load ontologies, associations, and population gene IDs
taxid = 10090 # Mouse study
geneids_pop = GeneID2nt_mus.keys() # Mouse protein-coding genes
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# 1b. Run GOEA
geneids_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
return {
'goea_results' : goeaobj.run_study(geneids_study, keep_if=keep_if),
'goeaobj' : goeaobj,
'geneids_study' : geneids_study,
'geneids_pop' : geneids_pop,
'obo_dag':goeaobj.obo_dag}
def _get_pvals(pvalfnc_names, prt=sys.stdout):
fisher2pvals = {}
taxid = 10090 # Mouse study
file_obo = os.path.join(os.getcwd(), "go-basic.obo")
obo_dag = get_godag(file_obo, prt, loading_bar=None)
geneids_pop = GeneID2nt_mus.keys()
assoc_geneid2gos = get_assoc_ncbi_taxids([taxid], loading_bar=None)
geneids_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
for fisher in pvalfnc_names:
goeaobj = GOEnrichmentStudy(geneids_pop,
assoc_geneid2gos,
obo_dag,
propagate_counts=False,
alpha=0.05,
methods=None,
pvalcalc=fisher)
fisher2pvals[fisher] = goeaobj.get_pval_uncorr(geneids_study, prt)
return fisher2pvals
def get_goea_results(keep_if=None):
"""Demonstrate printing a subset of all available fields using two methods."""
if keep_if is None:
keep_if = lambda nt: getattr(
nt, "p_fdr_bh") < 0.05 # keep if results are significant
# 1. Gene Ontology Enrichment Analysis
# 1a. Initialize: Load ontologies, associations, and population gene IDs
taxid = 10090 # Mouse study
geneids_pop = GeneID2nt_mus.keys() # Mouse protein-coding genes
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# 1b. Run GOEA
geneids_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
return {
'goea_results': goeaobj.run_study(geneids_study, keep_if=keep_if),
'goeaobj': goeaobj,
'geneids_study': geneids_study,
'geneids_pop': geneids_pop,
'obo_dag': goeaobj.obo_dag
}
def test_example(log=sys.stdout):
"""Run Gene Ontology Enrichment Analysis (GOEA) on Nature data."""
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Gene Ontology Enrichment Analysis (GOEA)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
taxid = 10090 # Mouse study
# Load ontologies, associations, and population ids
geneids_pop = GeneID2nt_mus.keys()
geneids_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# Run GOEA on study
#keep_if = lambda nt: getattr(nt, "p_fdr_bh" ) < 0.05 # keep if results are significant
goea_results_all = goeaobj.run_study(geneids_study)
goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
compare_results(goea_results_all)
geneids = get_study_items(goea_results_sig)
# Print GOEA results to files
goeaobj.wr_xlsx("nbt3102.xlsx", goea_results_sig)
goeaobj.wr_txt("nbt3102_sig.txt", goea_results_sig)
goeaobj.wr_txt("nbt3102_all.txt", goea_results_all)
# Plot all significant GO terms w/annotated study info (large plots)
#plot_results("nbt3102_{NS}.png", goea_results_sig)
#plot_results("nbt3102_{NS}_sym.png", goea_results_sig, study_items=5, items_p_line=2, id2symbol=geneids_study)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Further examination of GOEA results...
# --------------------------------------------------------------------
# --------------------------------------------------------------------
obo = goeaobj.obo_dag
dpi = 150 # For review: Figures can be saved in .jpg, .gif, .tif or .eps, at 150 dpi
# --------------------------------------------------------------------
# Item 1) Words in GO names associated with large numbers of study genes
# --------------------------------------------------------------------
# What GO term words are associated with the largest number of study genes?
prt_word2genecnt("nbt3102_genecnt_GOword.txt", goea_results_sig, log)
# Curated selection of GO words associated with large numbers of study genes
freq_seen = ['RNA', 'translation', 'mitochondr', 'ribosom', # 'ribosomal', 'ribosome',
'adhesion', 'endoplasmic', 'nucleotide', 'apoptotic', 'myelin']
# Collect the GOs which contains the chosen frequently seen words
word2NS2gos = get_word2NS2gos(freq_seen, goea_results_sig)
go2res = {nt.GO:nt for nt in goea_results_sig}
# Print words of interest, the sig GO terms which contain that word, and study genes.
prt_word_GO_genes("nbt3102_GO_word_genes.txt", word2NS2gos, go2res, geneids_study, log)
# Plot each set of GOs along w/study gene info
for word, NS2gos in word2NS2gos.items():
for NS in ['BP', 'MF', 'CC']:
if NS in NS2gos:
gos = NS2gos[NS]
goid2goobj = {go:go2res[go].goterm for go in gos}
# dpi: 150 for review, 1200 for publication
#dpis = [150, 1200] if word == "RNA" else [150]
dpis = [150]
for dpi in dpis:
fmts = ['png', 'tif', 'eps'] if word == "RNA" else ['png']
for fmt in fmts:
plot_goid2goobj(
"nbt3102_{WORD}_{NS}_dpi{DPI}.{FMT}".format(WORD=word, NS=NS, DPI=dpi, FMT=fmt),
goid2goobj, # source GOs and their GOTerm object
items_p_line=3,
study_items=6, # Max number of gene symbols to print in each GO term
id2symbol=geneids_study, # Contains GeneID-to-Symbol
goea_results=goea_results_all, # pvals used for GO Term coloring
dpi=dpi)
# --------------------------------------------------------------------
# Item 2) Explore findings of Nature paper:
#
# Gene Ontology (GO) enrichment analysis showed that the
# differentially expressed genes contained statistically
# significant enrichments of genes involved in
# glycolysis,
# cellular response to IL-4 stimulation and
# positive regulation of B-cell proliferation
# --------------------------------------------------------------------
goid_subset = [
'GO:0006096', # BP 4.24e-12 10 glycolytic process
'GO:0071353', # BP 7.45e-06 5 cellular response to interleukin-4
'GO:0030890', # BP 8.22e-07 7 positive regulation of B cell proliferation
]
plot_gos("nbt3102_GOs.png", goid_subset, obo, dpi=dpi)
plot_gos("nbt3102_GOs_genecnt.png", goid_subset, obo, goea_results=goea_results_all, dpi=dpi)
plot_gos("nbt3102_GOs_genelst.png", goid_subset, obo,
study_items=True, goea_results=goea_results_all, dpi=dpi)
plot_gos("nbt3102_GOs_symlst.png", goid_subset, obo,
study_items=True, id2symbol=geneids_study, goea_results=goea_results_all, dpi=dpi)
plot_gos("nbt3102_GOs_symlst_trunc.png", goid_subset, obo,
study_items=5, id2symbol=geneids_study, goea_results=goea_results_all, dpi=dpi)
plot_gos("nbt3102_GOs_GO0005743.png", ["GO:0005743"], obo,
items_p_line=2, study_items=6,
id2symbol=geneids_study, goea_results=goea_results_all, dpi=dpi)
# --------------------------------------------------------------------
# Item 3) Create one GO sub-plot per significant GO term from study
# --------------------------------------------------------------------
for rec in goea_results_sig:
png = "nbt3102_{NS}_{GO}.png".format(GO=rec.GO.replace(':', '_'), NS=rec.NS)
goid2goobj = {rec.GO:rec.goterm}
plot_goid2goobj(png,
goid2goobj, # source GOs and their GOTerm object
study_items=15, # Max number of gene symbols to print in each GO term
id2symbol=geneids_study, # Contains GeneID-to-Symbol
goea_results=goea_results_all, # pvals used for GO Term coloring
dpi=dpi)
# --------------------------------------------------------------------
# Item 4) Explore using manually curated lists of GO terms
# --------------------------------------------------------------------
goid_subset = [
'GO:0030529', # CC D03 intracellular ribonucleoprotein complex (42 genes)
'GO:0015934', # CC D05 large ribosomal subunit (4 genes)
'GO:0015935', # CC D05 small ribosomal subunit (13 genes)
'GO:0022625', # CC D06 cytosolic large ribosomal subunit (16 genes)
'GO:0022627', # CC D06 cytosolic small ribosomal subunit (19 genes)
'GO:0036464', # CC D06 cytoplasmic ribonucleoprotein granule (4 genes)
'GO:0005840', # CC D05 ribosome (35 genes)
'GO:0005844', # CC D04 polysome (6 genes)
]
plot_gos("nbt3102_CC_ribosome.png", goid_subset, obo,
study_items=6, id2symbol=geneids_study, items_p_line=3,
goea_results=goea_results_sig, dpi=dpi)
goid_subset = [
'GO:0003723', # MF D04 RNA binding (32 genes)
'GO:0044822', # MF D05 poly(A) RNA binding (86 genes)
'GO:0003729', # MF D06 mRNA binding (11 genes)
'GO:0019843', # MF D05 rRNA binding (6 genes)
'GO:0003746', # MF D06 translation elongation factor activity (5 genes)
]
plot_gos("nbt3102_MF_RNA_genecnt.png",
goid_subset,
obo,
goea_results=goea_results_all, dpi=150)
for dpi in [150, 1200]: # 150 for review, 1200 for publication
plot_gos("nbt3102_MF_RNA_dpi{DPI}.png".format(DPI=dpi),
goid_subset,
obo,
study_items=6, id2symbol=geneids_study, items_p_line=3,
goea_results=goea_results_all, dpi=dpi)
# --------------------------------------------------------------------
# Item 5) Are any significant geneids related to cell cycle?
# --------------------------------------------------------------------
import test_genes_cell_cycle as CC
genes_cell_cycle = CC.get_genes_cell_cycle(taxid, log=log)
genes_cell_cycle_sig = genes_cell_cycle.intersection(geneids)
CC.prt_genes("nbt3102_cell_cycle.txt", genes_cell_cycle_sig, taxid, log=None)
# Data will be stored in this variable
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import goatools
from goatools.base import download_go_basic_obo
from goatools.base import download_ncbi_associations
from goatools.obo_parser import GODag
from goatools.associations import read_ncbi_gene2go
from goatools.test_data.genes_NCBI_10090_ProteinCoding import GeneID2nt as GeneID2nt_mus
from goatools.go_enrichment import GOEnrichmentStudy
obo_fname = download_go_basic_obo()
gene2go = download_ncbi_associations()
obodag = GODag("go-basic.obo")
geneid2gos_mouse = read_ncbi_gene2go("gene2go", taxids=[10090])
geneid2symbol = {}
print("{N:,} annotated mouse genes".format(N=len(geneid2gos_mouse)))
print(GeneID2nt_mus.keys().head())
goeaobj = GOEnrichmentStudy(
GeneID2nt_mus.keys(), # List of mouse protein-coding genes
geneid2gos_mouse, # geneid/GO associations
obodag, # Ontologies
propagate_counts=False,
alpha=0.05, # default significance cut-off
methods=['fdr_bh']) # defult multipletest correction method
def test_example(log=sys.stdout):
"""Run Gene Ontology Enrichment Analysis (GOEA) on Nature data."""
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Gene Ontology Enrichment Analysis (GOEA)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
taxid = 10090 # Mouse study
# Load ontologies, associations, and population ids
geneids_pop = GeneID2nt_mus.keys()
geneids_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
# Run GOEA on study
#keep_if = lambda nt: getattr(nt, "p_fdr_bh" ) < 0.05 # keep if results are significant
goea_results_all = goeaobj.run_study(geneids_study)
goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
compare_results(goea_results_all)
geneids = get_study_items(goea_results_sig)
# Print GOEA results to files
goeaobj.wr_xlsx("nbt3102.xlsx", goea_results_sig)
goeaobj.wr_txt("nbt3102_sig.txt", goea_results_sig)
goeaobj.wr_txt("nbt3102_all.txt", goea_results_all)
# Plot all significant GO terms w/annotated study info (large plots)
#plot_results("nbt3102_{NS}.png", goea_results_sig)
#plot_results("nbt3102_{NS}_sym.png", goea_results_sig, study_items=5, items_p_line=2, id2symbol=geneids_study)
# --------------------------------------------------------------------
# --------------------------------------------------------------------
# Further examination of GOEA results...
# --------------------------------------------------------------------
# --------------------------------------------------------------------
obo = goeaobj.obo_dag
dpi = 150 # For review: Figures can be saved in .jpg, .gif, .tif or .eps, at 150 dpi
# --------------------------------------------------------------------
# Item 1) Words in GO names associated with large numbers of study genes
# --------------------------------------------------------------------
# What GO term words are associated with the largest number of study genes?
prt_word2genecnt("nbt3102_genecnt_GOword.txt", goea_results_sig, log)
# Curated selection of GO words associated with large numbers of study genes
freq_seen = [
'RNA',
'translation',
'mitochondr',
'ribosom', # 'ribosomal', 'ribosome',
'adhesion',
'endoplasmic',
'nucleotide',
'apoptotic',
'myelin'
]
# Collect the GOs which contains the chosen frequently seen words
word2NS2gos = get_word2NS2gos(freq_seen, goea_results_sig)
go2res = {nt.GO: nt for nt in goea_results_sig}
# Print words of interest, the sig GO terms which contain that word, and study genes.
prt_word_GO_genes("nbt3102_GO_word_genes.txt", word2NS2gos, go2res,
geneids_study, log)
# Plot each set of GOs along w/study gene info
for word, NS2gos in word2NS2gos.items():
for NS in ['BP', 'MF', 'CC']:
if NS in NS2gos:
gos = NS2gos[NS]
goid2goobj = {go: go2res[go].goterm for go in gos}
# dpi: 150 for review, 1200 for publication
#dpis = [150, 1200] if word == "RNA" else [150]
dpis = [150]
for dpi in dpis:
fmts = ['png', 'tif', 'eps'] if word == "RNA" else ['png']
for fmt in fmts:
plot_goid2goobj(
"nbt3102_{WORD}_{NS}_dpi{DPI}.{FMT}".format(
WORD=word, NS=NS, DPI=dpi, FMT=fmt),
goid2goobj, # source GOs and their GOTerm object
items_p_line=3,
study_items=
6, # Max number of gene symbols to print in each GO term
id2symbol=geneids_study, # Contains GeneID-to-Symbol
goea_results=
goea_results_all, # pvals used for GO Term coloring
dpi=dpi)
# --------------------------------------------------------------------
# Item 2) Explore findings of Nature paper:
#
# Gene Ontology (GO) enrichment analysis showed that the
# differentially expressed genes contained statistically
# significant enrichments of genes involved in
# glycolysis,
# cellular response to IL-4 stimulation and
# positive regulation of B-cell proliferation
# --------------------------------------------------------------------
goid_subset = [
'GO:0006096', # BP 4.24e-12 10 glycolytic process
'GO:0071353', # BP 7.45e-06 5 cellular response to interleukin-4
'GO:0030890', # BP 8.22e-07 7 positive regulation of B cell proliferation
]
plot_gos("nbt3102_GOs.png", goid_subset, obo, dpi=dpi)
plot_gos("nbt3102_GOs_genecnt.png",
goid_subset,
obo,
goea_results=goea_results_all,
dpi=dpi)
plot_gos("nbt3102_GOs_genelst.png",
goid_subset,
obo,
study_items=True,
goea_results=goea_results_all,
dpi=dpi)
plot_gos("nbt3102_GOs_symlst.png",
goid_subset,
obo,
study_items=True,
id2symbol=geneids_study,
goea_results=goea_results_all,
dpi=dpi)
plot_gos("nbt3102_GOs_symlst_trunc.png",
goid_subset,
obo,
study_items=5,
id2symbol=geneids_study,
goea_results=goea_results_all,
dpi=dpi)
plot_gos("nbt3102_GOs_GO0005743.png", ["GO:0005743"],
obo,
items_p_line=2,
study_items=6,
id2symbol=geneids_study,
goea_results=goea_results_all,
dpi=dpi)
# --------------------------------------------------------------------
# Item 3) Create one GO sub-plot per significant GO term from study
# --------------------------------------------------------------------
for rec in goea_results_sig:
png = "nbt3102_{NS}_{GO}.png".format(GO=rec.GO.replace(':', '_'),
NS=rec.NS)
goid2goobj = {rec.GO: rec.goterm}
plot_goid2goobj(
png,
goid2goobj, # source GOs and their GOTerm object
study_items=
15, # Max number of gene symbols to print in each GO term
id2symbol=geneids_study, # Contains GeneID-to-Symbol
goea_results=goea_results_all, # pvals used for GO Term coloring
dpi=dpi)
# --------------------------------------------------------------------
# Item 4) Explore using manually curated lists of GO terms
# --------------------------------------------------------------------
goid_subset = [
'GO:0030529', # CC D03 intracellular ribonucleoprotein complex (42 genes)
'GO:0015934', # CC D05 large ribosomal subunit (4 genes)
'GO:0015935', # CC D05 small ribosomal subunit (13 genes)
'GO:0022625', # CC D06 cytosolic large ribosomal subunit (16 genes)
'GO:0022627', # CC D06 cytosolic small ribosomal subunit (19 genes)
'GO:0036464', # CC D06 cytoplasmic ribonucleoprotein granule (4 genes)
'GO:0005840', # CC D05 ribosome (35 genes)
'GO:0005844', # CC D04 polysome (6 genes)
]
plot_gos("nbt3102_CC_ribosome.png",
goid_subset,
obo,
study_items=6,
id2symbol=geneids_study,
items_p_line=3,
goea_results=goea_results_sig,
dpi=dpi)
goid_subset = [
'GO:0003723', # MF D04 RNA binding (32 genes)
'GO:0044822', # MF D05 poly(A) RNA binding (86 genes)
'GO:0003729', # MF D06 mRNA binding (11 genes)
'GO:0019843', # MF D05 rRNA binding (6 genes)
'GO:0003746', # MF D06 translation elongation factor activity (5 genes)
]
plot_gos("nbt3102_MF_RNA_genecnt.png",
goid_subset,
obo,
goea_results=goea_results_all,
dpi=150)
for dpi in [150, 1200]: # 150 for review, 1200 for publication
plot_gos("nbt3102_MF_RNA_dpi{DPI}.png".format(DPI=dpi),
goid_subset,
obo,
study_items=6,
id2symbol=geneids_study,
items_p_line=3,
goea_results=goea_results_all,
dpi=dpi)
# --------------------------------------------------------------------
# Item 5) Are any significant geneids related to cell cycle?
# --------------------------------------------------------------------
import test_genes_cell_cycle as CC
genes_cell_cycle = CC.get_genes_cell_cycle(taxid, log=log)
genes_cell_cycle_sig = genes_cell_cycle.intersection(geneids)
CC.prt_genes("nbt3102_cell_cycle.txt",
genes_cell_cycle_sig,
taxid,
log=None)
