def main():
usage = 'usage: %prog anchor_results.txt anchor_results_null.txt\n'\
'Requires two input arguments:\n'\
'1) Interesting anchor results, output from run_anchor_batch.py\n'\
'2) Null anchor results, output from run_anchor_batch.py\n'
parser = OptionParser(usage=usage)
parser.add_option('-1',
'--exon_label1',
dest='exon_label1',
default='Exon label 1',
help='Exon label of anchor_results.txt.')
parser.add_option('-2',
'--exon_label2',
dest='exon_label2',
default='Exon label 2',
help='Exon label of anchor_results_null.txt')
parser.add_option(
'-t',
'--title',
dest='title',
default='Fraction of exons with predicted binding regions',
help='Title of plot.')
(options, args) = parser.parse_args()
if len(args) != 2:
print 'Two arguments need to be specified in command line.\n'
print usage
sys.exit()
anchor_results_path = args[0]
anchor_results_null_path = args[1]
exon_label1 = options.exon_label1
exon_label2 = options.exon_label2
mytitle = options.title
# init dic with keys and empty lists
anchor_dic = {}
for key in ['binding', 'non_binding', 'total']:
anchor_dic[key] = []
for results in [anchor_results_path, anchor_results_null_path]:
binding_count, total_count = count_anchor_results(results)
non_binding_count = total_count - binding_count
for key, val in zip(['binding', 'non_binding', 'total'],
[binding_count, non_binding_count, total_count]):
anchor_dic[key].append(val)
oddsratio, pvalue = \
fisher_exact([anchor_dic['binding'], anchor_dic['non_binding']])
print 'oddsratio: %s\npvalue: %s' % (oddsratio, pvalue)
# plot distributions (from plot_meme_motif_null_comparison.py)
mylabels = [exon_label1, exon_label2]
# Plot bargraphs
frac_binding = float(anchor_dic['binding'][0]) / anchor_dic['total'][0]
frac_binding_null = float(
anchor_dic['binding'][1]) / anchor_dic['total'][1]
myvals = [frac_binding, frac_binding_null]
plot_barplot(myvals, mytitle, mylabels,
ylabel='Fraction predicted binding regions',
mytext1="%i/%i" \
%(anchor_dic['binding'][0],
anchor_dic['total'][0]),
mytext2='%i/%i' %(anchor_dic['binding'][1],
anchor_dic['total'][1]),
mytext3="*Fisher's Exact Test\nP-value=%.2e" %pvalue,
ymin=0,
ymax=1,
width=0.5)
plt.show()
def main():
usage = 'usage: %prog anchor_results.txt anchor_results_null.txt\n'\
'Requires two input arguments:\n'\
'1) Interesting anchor results, output from run_anchor_batch.py\n'\
'2) Null anchor results, output from run_anchor_batch.py\n'
parser = OptionParser(usage=usage)
parser.add_option('-1', '--exon_label1', dest='exon_label1',
default='Exon label 1',
help='Exon label of anchor_results.txt.')
parser.add_option('-2', '--exon_label2', dest='exon_label2',
default='Exon label 2',
help='Exon label of anchor_results_null.txt')
parser.add_option('-t', '--title', dest='title',
default='Fraction of exons with predicted binding regions',
help='Title of plot.')
(options, args) = parser.parse_args()
if len(args) != 2:
print 'Two arguments need to be specified in command line.\n'
print usage
sys.exit()
anchor_results_path = args[0]
anchor_results_null_path = args[1]
exon_label1 = options.exon_label1
exon_label2 = options.exon_label2
mytitle = options.title
# init dic with keys and empty lists
anchor_dic = {}
for key in ['binding', 'non_binding', 'total']:
anchor_dic[key] = []
for results in [anchor_results_path, anchor_results_null_path]:
binding_count, total_count = count_anchor_results(results)
non_binding_count = total_count - binding_count
for key, val in zip(['binding', 'non_binding', 'total'],
[binding_count, non_binding_count, total_count]):
anchor_dic[key].append(val)
oddsratio, pvalue = \
fisher_exact([anchor_dic['binding'], anchor_dic['non_binding']])
print 'oddsratio: %s\npvalue: %s' %(oddsratio, pvalue)
# plot distributions (from plot_meme_motif_null_comparison.py)
mylabels = [exon_label1, exon_label2]
# Plot bargraphs
frac_binding = float(anchor_dic['binding'][0]) / anchor_dic['total'][0]
frac_binding_null = float(anchor_dic['binding'][1]) / anchor_dic['total'][1]
myvals = [frac_binding, frac_binding_null]
plot_barplot(myvals, mytitle, mylabels,
ylabel='Fraction predicted binding regions',
mytext1="%i/%i" \
%(anchor_dic['binding'][0],
anchor_dic['total'][0]),
mytext2='%i/%i' %(anchor_dic['binding'][1],
anchor_dic['total'][1]),
mytext3="*Fisher's Exact Test\nP-value=%.2e" %pvalue,
ymin=0,
ymax=1,
width=0.5)
plt.show()
def main():
usage = 'usage: %prog meme_gerp_genename_filepath output_filepath\n'\
'Requires two input arguments:\n'\
'1) pkl file from summarize_meme_results: non-null\n'\
'2) pkl file from summarize_meme_results: null-mode\n'
parser = OptionParser(usage=usage)
parser.add_option('-t', '--threshold', dest='score_threshold',
default=2.0,
help='Float, threshold for what one considers conserved.')
parser.add_option('-y', '--ymax', dest='ymax',
type='float',
default=0.03,
help='Y max for density plot')
(options, args) = parser.parse_args()
if len(args) < 2:
print 'Two arguments need to be specified in command line.\n'
print usage
sys.exit()
non_null_pklpath = args[0]
null_pklpath = args[1]
# parse ops
score_threshold = float(options.score_threshold)
# get dics from pkl
non_null_dic = get_dic_from_pklpath(non_null_pklpath)
null_dic = get_dic_from_pklpath(null_pklpath)
non_null_gerp_scores = get_gerp_scores(non_null_dic, gerpkey='avg_rs_score')
null_gerp_scores = get_gerp_scores(null_dic, gerpkey='avg_rs_score')
plot_functions.plot_density([non_null_gerp_scores, null_gerp_scores],
mytitle='Density plot of conservation scores',
labels_lists=['MEME motifs', 'Controls'],
xlabel='GERP conservation score',
ylabel='Density',
xmin=-4, xmax=4,
ymax=options.ymax,
smoothness=0.15,
drawvline=score_threshold)
# find how many conserved regions are in each.
n_conserved_in_meme = \
gerp_utilities.conserved_regions(non_null_gerp_scores, fraction=False, threshold=score_threshold)
n_conserved_in_null = \
gerp_utilities.conserved_regions(null_gerp_scores, fraction=False, threshold=score_threshold)
n_total_in_meme = len(non_null_gerp_scores)
n_total_in_null = len(null_gerp_scores)
n_not_conserved_in_meme = n_total_in_meme - n_conserved_in_meme
n_not_conserved_in_null = n_total_in_null - n_conserved_in_null
print 'Threshold: %s' %score_threshold
print 'Number of conserved elements: %s' %n_conserved_in_meme
print 'Number of conserved elements found in control: %s' %n_conserved_in_null
# Perform fisher's exact test
oddsratio, pvalue = fisher_exact([[n_conserved_in_meme,
n_conserved_in_null],
[n_not_conserved_in_meme,
n_not_conserved_in_null]])
print 'Fishers Exact Test, Oddsratio: %s. Pvalue: %s' %(oddsratio, pvalue)
# plot distributions
mylabels = ['Meme motifs', 'Control region']
mytitle = 'Fraction of elements conserved compared to control region'
# Plot bargraphs
frac_conserved_meme = float(n_conserved_in_meme) / n_total_in_meme
frac_conserved_null = float(n_conserved_in_null) / n_total_in_null
myvals = [frac_conserved_meme, frac_conserved_null]
plot_functions.plot_barplot(myvals, mytitle, mylabels,
ylabel='Fraction of elements conserved',
mytext1="%i/%i" \
%(n_conserved_in_meme,
n_total_in_meme),
mytext2='%i/%i' %(n_conserved_in_null,
n_total_in_null),
mytext3="*Fisher's Exact Test\nP-value=%.2e" %pvalue,
ymin=0,
ymax=1,
width=0.5)
plt.show()
def main():
usage = 'usage: %prog meme_gerp_genename_filepath output_filepath\n'\
'Requires two input arguments:\n'\
'1) pkl file from summarize_meme_results: non-null\n'\
'2) pkl file from summarize_meme_results: null-mode\n'
parser = OptionParser(usage=usage)
parser.add_option(
'-t',
'--threshold',
dest='score_threshold',
default=2.0,
help='Float, threshold for what one considers conserved.')
parser.add_option('-y',
'--ymax',
dest='ymax',
type='float',
default=0.03,
help='Y max for density plot')
(options, args) = parser.parse_args()
if len(args) < 2:
print 'Two arguments need to be specified in command line.\n'
print usage
sys.exit()
non_null_pklpath = args[0]
null_pklpath = args[1]
# parse ops
score_threshold = float(options.score_threshold)
# get dics from pkl
non_null_dic = get_dic_from_pklpath(non_null_pklpath)
null_dic = get_dic_from_pklpath(null_pklpath)
non_null_gerp_scores = get_gerp_scores(non_null_dic,
gerpkey='avg_rs_score')
null_gerp_scores = get_gerp_scores(null_dic, gerpkey='avg_rs_score')
plot_functions.plot_density([non_null_gerp_scores, null_gerp_scores],
mytitle='Density plot of conservation scores',
labels_lists=['MEME motifs', 'Controls'],
xlabel='GERP conservation score',
ylabel='Density',
xmin=-4,
xmax=4,
ymax=options.ymax,
smoothness=0.15,
drawvline=score_threshold)
# find how many conserved regions are in each.
n_conserved_in_meme = \
gerp_utilities.conserved_regions(non_null_gerp_scores, fraction=False, threshold=score_threshold)
n_conserved_in_null = \
gerp_utilities.conserved_regions(null_gerp_scores, fraction=False, threshold=score_threshold)
n_total_in_meme = len(non_null_gerp_scores)
n_total_in_null = len(null_gerp_scores)
n_not_conserved_in_meme = n_total_in_meme - n_conserved_in_meme
n_not_conserved_in_null = n_total_in_null - n_conserved_in_null
print 'Threshold: %s' % score_threshold
print 'Number of conserved elements: %s' % n_conserved_in_meme
print 'Number of conserved elements found in control: %s' % n_conserved_in_null
# Perform fisher's exact test
oddsratio, pvalue = fisher_exact(
[[n_conserved_in_meme, n_conserved_in_null],
[n_not_conserved_in_meme, n_not_conserved_in_null]])
print 'Fishers Exact Test, Oddsratio: %s. Pvalue: %s' % (oddsratio, pvalue)
# plot distributions
mylabels = ['Meme motifs', 'Control region']
mytitle = 'Fraction of elements conserved compared to control region'
# Plot bargraphs
frac_conserved_meme = float(n_conserved_in_meme) / n_total_in_meme
frac_conserved_null = float(n_conserved_in_null) / n_total_in_null
myvals = [frac_conserved_meme, frac_conserved_null]
plot_functions.plot_barplot(myvals, mytitle, mylabels,
ylabel='Fraction of elements conserved',
mytext1="%i/%i" \
%(n_conserved_in_meme,
n_total_in_meme),
mytext2='%i/%i' %(n_conserved_in_null,
n_total_in_null),
mytext3="*Fisher's Exact Test\nP-value=%.2e" %pvalue,
ymin=0,
ymax=1,
width=0.5)
plt.show()