def create_top_correlations_html(data, fitter, fits, scores, regions, n_top=None):
if n_top is None:
n_top = len(scores)
basedir = join(results_dir(), fit_results_relative_path(data,fitter))
ensure_dir(basedir)
gene_dir = 'gene-subplot'
series_dir = 'gene-region-fits'
def key_func(score):
g,r,pval,lst_R2 = score
return r
scores.sort(key=key_func)
top_genes = [g for g,r,pval,lst_R2 in scores[:n_top]]
top_scores = {g:r for g,r,pval,lst_R2 in scores[:n_top]}
top_pvals = {g:pval for g,r,pval,lst_R2 in scores[:n_top]}
def get_onset_time(fit):
a,h,mu,_ = fit.theta
age = age_scaler.unscale(mu)
txt = 'onset = {:.3g} years'.format(age)
cls = ''
return txt,cls
create_html(
data, fitter, fits, basedir, gene_dir, series_dir,
gene_names = top_genes,
region_names = regions,
extra_columns = [('r',top_scores),('p-value',top_pvals)],
extra_fields_per_fit = [get_onset_time],
b_inline_images = True,
b_R2_dist = False,
ttl = 'Fit for genes with top Spearman correlations',
filename = 'top-gradual-maturation',
)
def create_top_genes_html(data, fitter, fits, scores, regions, n_top=None, filename_suffix=''):
if n_top is None:
n_top = len(scores)
basedir = join(results_dir(), fit_results_relative_path(data,fitter))
ensure_dir(basedir)
gene_dir = 'gene-subplot'
series_dir = 'gene-region-fits'
def key_func(score):
g,pval,qval = score
return pval
scores.sort(key=key_func)
top_genes = [g for g,pval,qval in scores[:n_top]]
top_pvals = {g:pval for g,pval,qval in scores[:n_top]}
top_qvals = {g:qval for g,pval,qval in scores[:n_top]}
n = len(scores)
n05 = len([g for g,pval,qval in scores if qval < 0.05])
n01 = len([g for g,pval,qval in scores if qval < 0.01])
top_text = """\
<pre>
one sided t-test: {regions[0]} < {regions[1]}
{n05}/{n} q-values < 0.05
{n01}/{n} q_values < 0.01
</pre>
""".format(**locals())
def get_onset_time(fit):
a,h,mu,_ = fit.theta
age = age_scaler.unscale(mu)
return 'onset = {:.3g} years'.format(age)
def get_onset_dist(fit):
mu_vals = fit.theta_samples[2,:]
mu = mu_vals.mean()
vLow,vHigh = np.percentile(mu_vals, (20,80))
mu = age_scaler.unscale(mu)
vLow = age_scaler.unscale(vLow)
vHigh = age_scaler.unscale(vHigh)
txt = 'onset reestimate (mean [20%, 80%]) = {:.3g} [{:.3g},{:.3g}]'.format(mu,vLow,vHigh)
cls = ''
return txt,cls
create_html(
data, fitter, fits, basedir, gene_dir, series_dir,
gene_names = top_genes,
region_names = regions,
extra_columns = [('p-value',top_pvals), ('q-value',top_qvals)],
extra_fields_per_fit = [get_onset_time, get_onset_dist],
b_inline_images = True,
inline_image_size = '30%',
b_R2_dist = False,
ttl = 'Fit for genes with top t-test scores',
top_text = top_text,
filename = 'gradual-maturation-t-test' + filename_suffix,
)
def create_top_correlations_html(data,
fitter,
fits,
scores,
regions,
n_top=None):
if n_top is None:
n_top = len(scores)
basedir = join(results_dir(), fit_results_relative_path(data, fitter))
ensure_dir(basedir)
gene_dir = 'gene-subplot'
series_dir = 'gene-region-fits'
def key_func(score):
g, r, pval, lst_R2 = score
return r
scores.sort(key=key_func)
top_genes = [g for g, r, pval, lst_R2 in scores[:n_top]]
top_scores = {g: r for g, r, pval, lst_R2 in scores[:n_top]}
top_pvals = {g: pval for g, r, pval, lst_R2 in scores[:n_top]}
def get_onset_time(fit):
a, h, mu, _ = fit.theta
age = age_scaler.unscale(mu)
txt = 'onset = {:.3g} years'.format(age)
cls = ''
return txt, cls
create_html(
data,
fitter,
fits,
basedir,
gene_dir,
series_dir,
gene_names=top_genes,
region_names=regions,
extra_columns=[('r', top_scores), ('p-value', top_pvals)],
extra_fields_per_fit=[get_onset_time],
b_inline_images=True,
b_R2_dist=False,
ttl='Fit for genes with top Spearman correlations',
filename='top-gradual-maturation',
)
def create_top_genes_html(data,
fitter,
fits,
scores,
regions,
n_top=None,
filename_suffix=''):
if n_top is None:
n_top = len(scores)
basedir = join(results_dir(), fit_results_relative_path(data, fitter))
ensure_dir(basedir)
gene_dir = 'gene-subplot'
series_dir = 'gene-region-fits'
def key_func(score):
g, pval, qval = score
return pval
scores.sort(key=key_func)
top_genes = [g for g, pval, qval in scores[:n_top]]
top_pvals = {g: pval for g, pval, qval in scores[:n_top]}
top_qvals = {g: qval for g, pval, qval in scores[:n_top]}
n = len(scores)
n05 = len([g for g, pval, qval in scores if qval < 0.05])
n01 = len([g for g, pval, qval in scores if qval < 0.01])
top_text = """\
<pre>
one sided t-test: {regions[0]} < {regions[1]}
{n05}/{n} q-values < 0.05
{n01}/{n} q_values < 0.01
</pre>
""".format(**locals())
def get_onset_time(fit):
a, h, mu, _ = fit.theta
age = age_scaler.unscale(mu)
return 'onset = {:.3g} years'.format(age)
def get_onset_dist(fit):
mu_vals = fit.theta_samples[2, :]
mu = mu_vals.mean()
vLow, vHigh = np.percentile(mu_vals, (20, 80))
mu = age_scaler.unscale(mu)
vLow = age_scaler.unscale(vLow)
vHigh = age_scaler.unscale(vHigh)
txt = 'onset reestimate (mean [20%, 80%]) = {:.3g} [{:.3g},{:.3g}]'.format(
mu, vLow, vHigh)
cls = ''
return txt, cls
create_html(
data,
fitter,
fits,
basedir,
gene_dir,
series_dir,
gene_names=top_genes,
region_names=regions,
extra_columns=[('p-value', top_pvals), ('q-value', top_qvals)],
extra_fields_per_fit=[get_onset_time, get_onset_dist],
b_inline_images=True,
inline_image_size='30%',
b_R2_dist=False,
ttl='Fit for genes with top t-test scores',
top_text=top_text,
filename='gradual-maturation-t-test' + filename_suffix,
)
