def get_data_from_args(dataset, pathway, from_age, scaling, shuffle):
data = GeneData.load(dataset).restrict_pathway(pathway)
if from_age is not None:
restriction_name = from_age
from_age = dct_ages[from_age]
data.restrict_ages(restriction_name, from_age=from_age)
scaler = build_scaler(scaling, data)
if scaler is not None:
data.scale_ages(scaler)
if shuffle:
data.shuffle()
return data
def get_data_from_args(dataset, pathway, from_age, scaling, shuffle):
data = GeneData.load(dataset).restrict_pathway(pathway)
if from_age is not None:
restriction_name = from_age
from_age = dct_ages[from_age]
data.restrict_ages(restriction_name,from_age=from_age)
scaler = build_scaler(scaling,data)
if scaler is not None:
data.scale_ages(scaler)
if shuffle:
data.shuffle()
return data
import setup
import config as cfg
from load_data import GeneData
from shapes.sigmoid import Sigmoid
from fitter import Fitter
from all_fits import get_all_fits, iterate_fits
from scalers import LogScaler
cfg.verbosity = 1
age_scaler = LogScaler()
pathway = 'serotonin'
data = GeneData.load('both').restrict_pathway(pathway).scale_ages(age_scaler)
fitter = Fitter(Sigmoid(priors=None))
fits = get_all_fits(data,fitter)
extreme = [(g,r) for dsname,g,r,fit in iterate_fits(fits, R2_threshold=0.5, return_keys=True) if abs(fit.theta[0]) > 100]
ax.set_xticks([stage.central_age for stage in stages])
ax.set_xticklabels([stage.short_name for stage in stages], fontsize=xtick_fontsize, fontstretch='condensed', rotation=90)
# set y ticks (first and last only)
ax.set_ylabel('expression level', fontsize=fontsize)
ticks = ax.get_yticks()
ticks = np.array([ticks[0], ticks[-1]])
ax.set_yticks(ticks)
ax.set_yticklabels(['{:g}'.format(t) for t in ticks], fontsize=fontsize)
return fig
cfg.verbosity = 1
age_scaler = LogScaler()
data = GeneData.load('both').scale_ages(age_scaler)
shapes = [Sigmoid('sigmoid_wide'), Poly(1,'poly1'), Poly(3,'poly3'), Spline()]
GRs = [
('ADRB1','A1C', (5, 8)),
('GLRA2','STC', (5, 12)),
('TUBA1A','V1C', (10, 14)),
]
for g,r,yrange in GRs:
print 'Doing {}@{}...'.format(g,r)
thetas = []
for shape in shapes:
series = data.get_one_series(g,r)
sigma_prior = 'normal' if not isinstance(shape,Spline) else None
fitter = Fitter(shape, sigma_prior=sigma_prior)
rotation=90)
# set y ticks (first and last only)
ax.set_ylabel('expression level', fontsize=fontsize)
ticks = ax.get_yticks()
ticks = np.array([ticks[0], ticks[-1]])
ax.set_yticks(ticks)
ax.set_yticklabels(['{:g}'.format(t) for t in ticks], fontsize=fontsize)
return fig
cfg.verbosity = 1
age_scaler = LogScaler()
data = GeneData.load('both').scale_ages(age_scaler)
shapes = [
Sigmoid('sigmoid_wide'),
Poly(1, 'poly1'),
Poly(3, 'poly3'),
Spline()
]
GRs = [
('ADRB1', 'A1C', (5, 8)),
('GLRA2', 'STC', (5, 12)),
('TUBA1A', 'V1C', (10, 14)),
]
for g, r, yrange in GRs:
print 'Doing {}@{}...'.format(g, r)
import setup
import config as cfg
from load_data import GeneData
from shapes.sigmoid import Sigmoid
from fitter import Fitter
from all_fits import get_all_fits, iterate_fits
from scalers import LogScaler
cfg.verbosity = 1
age_scaler = LogScaler()
pathway = 'serotonin'
data = GeneData.load('both').restrict_pathway(pathway).scale_ages(age_scaler)
fitter = Fitter(Sigmoid(priors=None))
fits = get_all_fits(data, fitter)
extreme = [(g, r) for dsname, g, r, fit in iterate_fits(
fits, R2_threshold=0.5, return_keys=True) if abs(fit.theta[0]) > 100]
ttl='Fit for genes with top t-test scores',
top_text=top_text,
filename='gradual-maturation-t-test' + filename_suffix,
)
cfg.verbosity = 1
age_scaler = LogScaler()
lst_pathways = [
'serotonin',
'dopamine',
]
for pathway in lst_pathways:
data = GeneData.load('both').restrict_pathway(pathway).restrict_ages(
'EF3', PCW(10)).scale_ages(age_scaler)
shape = Sigmoid(priors='sigmoid_wide')
fitter = Fitter(shape, sigma_prior='normal')
fits = get_all_fits(data, fitter, allow_new_computation=False)
ds_fits = fits['kang2011']
for b_reversed in [False, True]:
regions = ['V1C', 'OFC']
if b_reversed:
regions = regions[::-1]
scores = []
for i, g in enumerate(data.gene_names):
mu1 = ds_fits[(g, regions[0])].theta_samples[2, :]
mu2 = ds_fits[(g, regions[1])].theta_samples[2, :]
t, pval = ttest_ind(mu1, mu2)
counts, bin_edges = np.histogram(z_scores, 50)
probs = counts / float(sum(counts))
width = bin_edges[1] - bin_edges[0]
ax.bar(bin_edges[:-1], probs, width=width, color='b')
ax.set_xlabel('z score', fontsize=fontsize)
ax.set_ylabel('probability', fontsize=fontsize)
ax.tick_params(axis='both', labelsize=fontsize)
return fig
cfg.verbosity = 1
age_scaler = LogScaler()
pathway = '17full'
data = GeneData.load('both').restrict_pathway(pathway).scale_ages(age_scaler)
data_shuffled = GeneData.load('both').restrict_pathway(pathway).scale_ages(
age_scaler).shuffle()
shape = Sigmoid('sigmoid_wide')
fitter = Fitter(shape, sigma_prior='normal')
fits = get_all_fits(data, fitter, allow_new_computation=False)
fits_shuffled = get_all_fits(data_shuffled,
fitter,
allow_new_computation=False)
R2_pairs = [(fit.LOO_score, fit2.LOO_score)
for fit, fit2 in iterate_fits(fits, fits_shuffled)]
R2 = np.array([r for r, r_shuffled in R2_pairs])
R2_shuffled = np.array([r_shuffled for r, r_shuffled in R2_pairs])
name = '{}-{}'.format(data.pathway, shape.cache_name())
b_R2_dist = False,
ttl = 'Fit for genes with top t-test scores',
top_text = top_text,
filename = 'gradual-maturation-t-test' + filename_suffix,
)
cfg.verbosity = 1
age_scaler = LogScaler()
lst_pathways = [
'serotonin',
'dopamine',
]
for pathway in lst_pathways:
data = GeneData.load('both').restrict_pathway(pathway).restrict_ages('EF3',PCW(10)).scale_ages(age_scaler)
shape = Sigmoid(priors='sigmoid_wide')
fitter = Fitter(shape, sigma_prior='normal')
fits = get_all_fits(data, fitter, allow_new_computation=False)
ds_fits = fits['kang2011']
for b_reversed in [False,True]:
regions = ['V1C', 'OFC']
if b_reversed:
regions = regions[::-1]
scores = []
for i,g in enumerate(data.gene_names):
mu1 = ds_fits[(g,regions[0])].theta_samples[2,:]
mu2 = ds_fits[(g,regions[1])].theta_samples[2,:]
t,pval = ttest_ind(mu1,mu2)
[stage.short_name for stage in stages], fontsize=xtick_fontsize, fontstretch="condensed", rotation=90
)
# set y ticks (first and last only)
ax.set_ylabel("expression level", fontsize=fontsize)
ticks = ax.get_yticks()
ticks = np.array([ticks[0], ticks[-1]])
ax.set_yticks(ticks)
ax.set_yticklabels(["{:g}".format(t) for t in ticks], fontsize=fontsize)
return fig
cfg.verbosity = 1
age_scaler = LogScaler()
data = GeneData.load("both").scale_ages(age_scaler)
# 0) GABRA2@HIP, delta-R2=0.669
# 1) GPR50@DFC, delta-R2=0.573
# 2) UCHL1@V1C, delta-R2=0.571
# 3) GRIN2B@HIP, delta-R2=0.548
# 4) SSTR1@A1C, delta-R2=0.533
# 5) GRM7@DFC, delta-R2=0.531
# 6) GRM7@STC, delta-R2=0.525
# 7) TOMM40L@VFC, delta-R2=0.514
# 8) CREB5@DFC, delta-R2=0.496
# 9) GRM7@S1C, delta-R2=0.474
GRs = [
("HTR5A", "S1C", (6, 10)), # example of extreme parameter values without priors
("GABRA2", "HIP", (4, 12)), # delta-R2=0.669 (not actual trend in data)
def get_fits():
data = GeneData.load('both').restrict_pathway('17pathways').scale_ages(age_scaler)
shape = Sigmoid(priors='sigmoid_wide')
fitter = Fitter(shape, sigma_prior='normal')
fits = get_all_fits(data, fitter)
return fits
ax = fig.add_subplot(111)
counts,bin_edges = np.histogram(z_scores,50)
probs = counts / float(sum(counts))
width = bin_edges[1] - bin_edges[0]
ax.bar(bin_edges[:-1], probs, width=width, color='b')
ax.set_xlabel('z score', fontsize=fontsize)
ax.set_ylabel('probability', fontsize=fontsize)
ax.tick_params(axis='both', labelsize=fontsize)
return fig
cfg.verbosity = 1
age_scaler = LogScaler()
pathway = '17full'
data = GeneData.load('both').restrict_pathway(pathway).scale_ages(age_scaler)
data_shuffled = GeneData.load('both').restrict_pathway(pathway).scale_ages(age_scaler).shuffle()
shape = Sigmoid('sigmoid_wide')
fitter = Fitter(shape,sigma_prior='normal')
fits = get_all_fits(data,fitter,allow_new_computation=False)
fits_shuffled = get_all_fits(data_shuffled,fitter,allow_new_computation=False)
R2_pairs = [(fit.LOO_score,fit2.LOO_score) for fit,fit2 in iterate_fits(fits,fits_shuffled)]
R2 = np.array([r for r,r_shuffled in R2_pairs])
R2_shuffled = np.array([r_shuffled for r,r_shuffled in R2_pairs])
name = '{}-{}'.format(data.pathway,shape.cache_name())
fig = plot_score_distribution(R2,R2_shuffled)
save_figure(fig,'RP/R2-distribution-{}.png'.format(name), under_results=True, b_close=True)
mu_shuffled = np.mean(R2_shuffled)
