def estimateEnrichmentOfTFBS(infiles, outfile):
'''
Test for enrichment of TFBS within a gene set by permutation.
'''
E.info("Running permutation testing for TFBS enrichment between %s" %
" & ".join([os.path.basename(x) for x in infiles]))
dbh = sqlite3.connect(PARAMS['database'])
# table from sql db
match_table = "match_result"
tfbs_state = '''SELECT seq_id, matrix_id FROM %s;''' % match_table
tfbs_table = pdsql.read_sql(tfbs_state, dbh, index_col='matrix_id')
# get foreground and background gene files
# setup gc content dataframes
background = [inf for inf in infiles
if re.search("background", inf)][0]
foreground = [inf for inf in infiles
if re.search("foreground", inf)][0]
back_gc = pandas.read_table(background,
sep="\t",
index_col=0,
header=0)
bg_gene_id = [x.split(" ")[0] for x in back_gc.index.tolist()]
back_gc['gene_id'] = bg_gene_id
back_gc.index = bg_gene_id
fore_gc = pandas.read_table(foreground,
sep="\t",
index_col=0,
header=0)
fg_gene_id = [x.split(" ")[0] for x in fore_gc.index.tolist()]
fore_gc['gene_id'] = fg_gene_id
fore_gc.index = fg_gene_id
# run permutation significance testing
perms = int(PARAMS['sig_testing_nperms'])
out_dict = PipelineTFM.permuteTFBSEnrich(tfbs_table=tfbs_table,
fg_gc=fore_gc,
bg_gc=back_gc,
nPerms=perms)
out_frame = pandas.DataFrame(out_dict).T
out_frame.to_csv(outfile, sep="\t", index_label='matrix_id')
def estimateEnrichmentOfTFBS(infiles, outfile):
'''
Test for enrichment of TFBS within a gene set by permutation.
'''
E.info("Running permutation testing for TFBS enrichment between %s" %
" & ".join([os.path.basename(x) for x in infiles]))
dbh = sqlite3.connect(PARAMS['database'])
# table from sql db
match_table = "match_result"
tfbs_state = '''SELECT seq_id, matrix_id FROM %s;''' % match_table
tfbs_table = pdsql.read_sql(tfbs_state, dbh, index_col='matrix_id')
# get foreground and background gene files
# setup gc content dataframes
background = [inf for inf in infiles if re.search("background",
inf)][0]
foreground = [inf for inf in infiles if re.search("foreground",
inf)][0]
back_gc = pandas.read_table(background,
sep="\t",
index_col=0,
header=0)
bg_gene_id = [x.split(" ")[0] for x in back_gc.index.tolist()]
back_gc['gene_id'] = bg_gene_id
back_gc.index = bg_gene_id
fore_gc = pandas.read_table(foreground,
sep="\t",
index_col=0,
header=0)
fg_gene_id = [x.split(" ")[0] for x in fore_gc.index.tolist()]
fore_gc['gene_id'] = fg_gene_id
fore_gc.index = fg_gene_id
# run permutation significance testing
perms = int(PARAMS['sig_testing_nperms'])
out_dict = PipelineTFM.permuteTFBSEnrich(tfbs_table=tfbs_table,
fg_gc=fore_gc,
bg_gc=back_gc,
nPerms=perms)
out_frame = pandas.DataFrame(out_dict).T
out_frame.to_csv(outfile, sep="\t", index_label='matrix_id')
def estimateEnrichmentOfTFBS(infiles, outfile):
'''
Test for enrichment of TFBS within a gene set by permutation.
'''
E.info("Running permutation testing for TFBS enrichment between %s" %
" & ".join([os.path.basename(x) for x in infiles]))
dbh = sqlite3.connect(PARAMS['database'])
# table from sql db
match_table = "match_result"
tfbs_state = '''SELECT matrix_id, seq_id FROM %s;''' % match_table
tfbs_table = pdsql.read_sql(sql=tfbs_state,
con=dbh,
index_col='matrix_id')
# get foreground and background gene files
# setup gc content dataframes
background = [inf for inf in infiles
if re.search("background", inf)][0]
foreground = [inf for inf in infiles
if re.search("foreground", inf)][0]
back_gc = pandas.read_table(background,
sep="\t",
index_col=0,
header=0)
bg_gene_id = [x.split(" ")[0] for x in back_gc.index.tolist()]
back_gc['gene_id'] = bg_gene_id
back_gc.index = bg_gene_id
fore_gc = pandas.read_table(foreground,
sep="\t",
index_col=0,
header=0)
fg_gene_id = [x.split(" ")[0] for x in fore_gc.index.tolist()]
fore_gc['gene_id'] = fg_gene_id
fore_gc.index = fg_gene_id
# run permutation significance testing
# check if there are sufficient genes in the background
# to do a permutation test. If not, do Fishers' exact.
# if yes, but less than specific number of permutations,
# limit to this number.
perms = int(PARAMS['sig_testing_nperms'])
poss_perms = PipelineTFM.nCr(n=len(back_gc.index),
r=len(fore_gc.index))
if poss_perms < 1000:
E.warn("Insufficient background genes to perform"
"permutations. Please use Fisher's Exact test")
raise ValueError("Insufficient background size. "
"Cannot use permutation test")
elif poss_perms > 1000 and poss_perms < perms:
E.info("Maximum possible permutations with this background"
" set is %i. Running %i permutations only" %
(poss_perms, poss_perms))
out_dict = PipelineTFM.permuteTFBSEnrich(tfbs_table=tfbs_table,
fg_gc=fore_gc,
bg_gc=back_gc,
nPerms=poss_perms,
bg_stat=PARAMS[""])
else:
bg_stat = PARAMS["background_match_stat"]
out_dict = PipelineTFM.permuteTFBSEnrich(tfbs_table=tfbs_table,
fg_gc=fore_gc,
bg_gc=back_gc,
nPerms=perms,
bg_stat=bg_stat)
out_frame = pandas.DataFrame(out_dict).T
pyadjust = R['p.adjust']
pvs = robjects.FloatVector([p for p in out_frame['pvalue']])
out_frame['qvalue'] = pyadjust(pvs)
out_frame.to_csv(outfile, sep="\t", index_label='matrix_id')
def estimateEnrichmentOfTFBS(infiles, outfile):
'''
Test for enrichment of TFBS within a gene set by permutation.
'''
E.info("Running permutation testing for TFBS enrichment between %s" %
" & ".join([os.path.basename(x) for x in infiles]))
dbh = sqlite3.connect(PARAMS['database'])
# table from sql db
match_table = "match_result"
tfbs_state = '''SELECT matrix_id, seq_id FROM %s;''' % match_table
tfbs_table = pdsql.read_sql(sql=tfbs_state,
con=dbh,
index_col='matrix_id')
# get foreground and background gene files
# setup gc content dataframes
background = [inf for inf in infiles if re.search("background",
inf)][0]
foreground = [inf for inf in infiles if re.search("foreground",
inf)][0]
back_gc = pandas.read_table(background,
sep="\t",
index_col=0,
header=0)
bg_gene_id = [x.split(" ")[0] for x in back_gc.index.tolist()]
back_gc['gene_id'] = bg_gene_id
back_gc.index = bg_gene_id
fore_gc = pandas.read_table(foreground,
sep="\t",
index_col=0,
header=0)
fg_gene_id = [x.split(" ")[0] for x in fore_gc.index.tolist()]
fore_gc['gene_id'] = fg_gene_id
fore_gc.index = fg_gene_id
# run permutation significance testing
# check if there are sufficient genes in the background
# to do a permutation test. If not, do Fishers' exact.
# if yes, but less than specific number of permutations,
# limit to this number.
perms = int(PARAMS['sig_testing_nperms'])
poss_perms = PipelineTFM.nCr(n=len(back_gc.index),
r=len(fore_gc.index))
if poss_perms < 1000:
E.warn("Insufficient background genes to perform"
"permutations. Please use Fisher's Exact test")
raise ValueError("Insufficient background size. "
"Cannot use permutation test")
elif poss_perms > 1000 and poss_perms < perms:
E.info("Maximum possible permutations with this background"
" set is %i. Running %i permutations only" % (poss_perms,
poss_perms))
out_dict = PipelineTFM.permuteTFBSEnrich(tfbs_table=tfbs_table,
fg_gc=fore_gc,
bg_gc=back_gc,
nPerms=poss_perms,
bg_stat=PARAMS[""])
else:
bg_stat = PARAMS["background_match_stat"]
out_dict = PipelineTFM.permuteTFBSEnrich(tfbs_table=tfbs_table,
fg_gc=fore_gc,
bg_gc=back_gc,
nPerms=perms,
bg_stat=bg_stat)
out_frame = pandas.DataFrame(out_dict).T
pyadjust = R['p.adjust']
pvs = robjects.FloatVector([p for p in out_frame['pvalue']])
out_frame['qvalue'] = pyadjust(pvs)
out_frame.to_csv(outfile, sep="\t", index_label='matrix_id')