def run_actual_assc(self, genes_study_arg, ntdesc):
"""Simulate the significance of the user-provided study vs. the population gene sets."""
genes_study = set(genes_study_arg)
assc_desc = 'actual'
alpha = self.objbase.alpha
genes_pop_masked = self.get_pop_genes_masked(genes_study)
goeaobj = self.objbase.get_goeaobj(genes_pop_masked,
self.objassc.get_assc())
goea_results = goeaobj.run_study(
genes_study, keep_if=lambda nt: nt.p_fdr_bh < alpha)
fout_txt = "goea_{DESC}_sig_{N:04}.txt".format(DESC=ntdesc.name,
N=len(genes_study))
goeaobj.wr_txt(fout_txt, goea_results)
genes_sig = get_study_items(goea_results)
# if genes_study != genes_sig:
# msg = "FOUND {STUSIG:4} OF {STU:4} {DESC} GENES TO BE SIGNIFICANT\n"
# genes_study_sig = genes_study.intersection(genes_sig)
# sys.stdout.write(msg.format(
# STU=len(genes_study), STUSIG=len(genes_study_sig), DESC=ntdesc.name))
return {
'goea_results': goea_results,
'genes_sig': genes_sig,
'genes_study': genes_study,
'assc_desc': assc_desc
}
def __init__(self, num_study_genes, num_null, pobj):
self.pobj = pobj # RunParams object
# I. Genes in two groups: Different than population AND no different than population
self.gene_expsig_list = mk_stochastic_goeasim_source(
num_study_genes, num_null, pobj.gene_lists['study_bg'],
pobj.gene_lists['null_bg']) # [(gene, expsig),
self.assc_geneid2gos = self._init_assc()
self.goea_results = self._init_goea_results()
# for g in self.goea_results:
# print "HHHH", g
self.genes_sig = get_study_items(self.goea_results)
if self.pobj.params['log'] is not None:
self._wrlog_summary(num_study_genes, num_null)
def run_random_assc(self, genes_study_arg, ntdesc):
"""Simulate no significance"""
genes_study = set(genes_study_arg)
assc_desc = 'random'
alpha = self.objbase.alpha
rand_assoc = RandAssc(
self.objassc.get_assc()).get_shuffled_associations()
goeaobj = self.objbase.get_goeaobj(self.objassc.pop_genes, rand_assoc)
goea_results = goeaobj.run_study(
genes_study, keep_if=lambda nt: nt.p_fdr_bh < alpha)
fout_txt = "goea_{DESC}_rnd_{N:04}.txt".format(DESC=ntdesc.name,
N=len(genes_study))
goeaobj.wr_txt(fout_txt, goea_results)
genes_rnd = get_study_items(goea_results)
assert len(goea_results) == 0, \
"EXPECTED NO SIGNIFICANT GO TERMS IN RANDOM SIMULATION. FOUND {N}".format(
N=len(goea_results))
return {
'goea_results': goea_results,
'genes_sig': genes_rnd,
'genes_study': genes_study,
'assc_desc': assc_desc
}
def _init_get_goids_tgtd(self):
"""Run baseline GOEA to obtain list of 'other' GO IDs which are truly significant."""
# Run Gene Ontology Analysis w/study genes being entire study gene background.
attrname = "p_{METHOD}".format(METHOD=self.objbase.method)
keep_if = lambda nt: getattr(nt, attrname) < self.objbase.alpha
# Association subset containing only population genes
assc_all = self.objassc.objassc_all.assc_geneid2gos
objgoea = self.objbase.get_goeaobj(self.genes['population'], assc_all)
goea_results = objgoea.run_study(self.genes['study_bg'],
keep_if=keep_if)
# Check study background genes
genes_signif = get_study_items(goea_results)
assert self.genes['study_bg'] == genes_signif
# Get GO IDs to randomize or remove
goids_signif = set([nt.GO for nt in goea_results])
goids_study_bg = self.params['goids_study_bg']
assert goids_signif.intersection(goids_study_bg) == goids_study_bg
# GO IDs targeted for removal or randomization
goids_artifacts = goids_signif.difference(goids_study_bg)
log = self.params['log']
if log is not None and goea_results:
self._prt_significant_artifacts(goea_results, goids_artifacts, log)
return goids_artifacts
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)
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)