def export_cube():
cube = load_pickle(RegionPairTiming.cube_filename)
README = """\
d_mu:
mu(r2)-mu(r1) for every gene and region pair.
Dimensions: <n-genes> X <n-regions> X <n-regions>
combined_std:
The combined standard deviation of the two change distributions.
std = sqrt(0.5*(std1^2 + std2^2))
Dimensions: <n-genes> X <n-regions> X <n-regions>
score:
The d' for the two change distributions. Equal to d_mu ./ combined_std.
Dimensions: <n-genes> X <n-regions> X <n-regions>
genes:
Gene names for the genes represented in other arrays
regions:
Region names for the regions represented in other arrays
age_scaler:
The scaling used for ages (i.e. 'log' means x' = log(x + 38/52))
"""
mdict = dict(
README_CUBE=README,
genes=list_of_strings_to_matlab_cell_array(cube.genes),
regions=list_of_strings_to_matlab_cell_array(cube.regions),
age_scaler=scalers.unify(cube.age_scaler).cache_name(),
d_mu=cube.d_mu,
combined_std=cube.std,
scores=cube.d_mu / cube.std,
)
save_matfile(mdict, join(results_dir(), 'export', 'cube.mat'))
def export_cube():
cube = load_pickle(RegionPairTiming.cube_filename)
README = """\
d_mu:
mu(r2)-mu(r1) for every gene and region pair.
Dimensions: <n-genes> X <n-regions> X <n-regions>
combined_std:
The combined standard deviation of the two change distributions.
std = sqrt(0.5*(std1^2 + std2^2))
Dimensions: <n-genes> X <n-regions> X <n-regions>
score:
The d' for the two change distributions. Equal to d_mu ./ combined_std.
Dimensions: <n-genes> X <n-regions> X <n-regions>
genes:
Gene names for the genes represented in other arrays
regions:
Region names for the regions represented in other arrays
age_scaler:
The scaling used for ages (i.e. 'log' means x' = log(x + 38/52))
"""
mdict = dict(
README_CUBE = README,
genes = list_of_strings_to_matlab_cell_array(cube.genes),
regions = list_of_strings_to_matlab_cell_array(cube.regions),
age_scaler = scalers.unify(cube.age_scaler).cache_name(),
d_mu = cube.d_mu,
combined_std = cube.std,
scores = cube.d_mu / cube.std,
)
save_matfile(mdict, join(results_dir(), 'export', 'cube.mat'))
def export_pathways():
change_dist = load_pickle(SingleRegion.change_dist_filename)
matlab_g2i = {g:(i+1) for i,g in enumerate(change_dist.genes)} # NOTE that matlab is one based
pathways = pathway_lists.read_all_pathways()
pathway_names = pathways.keys() # make sure the order stays fixed
pathway_genes_names = np.array([list_of_strings_to_matlab_cell_array(pathways[p]) for p in pathway_names], dtype=object)
pathway_genes_idx = np.array([np.array([matlab_g2i[g] for g in pathways[p]]) for p in pathway_names], dtype=object)
matlab_p2i = {p:(i+1) for i,p in enumerate(pathway_names)} # NOTE matlab indexing is one based
list_names = pathway_lists.all_pathway_lists()
list_pathway_names = np.empty(len(list_names), dtype=object)
list_pathway_idx = np.empty(len(list_names), dtype=object)
for i,listname in enumerate(list_names):
pathways_in_list = pathway_lists.list_to_pathway_names(listname)
list_pathway_names[i] = list_of_strings_to_matlab_cell_array(pathways_in_list)
list_pathway_idx[i] = [matlab_p2i[p] for p in pathways_in_list]
README = """\
pathway_names:
Cell array of all pathway names. The name in cell number k is the name of the
pathway at position k in "pathway_genes_names" and "pathway_genes_idx".
pathway_genes_names:
Cell array (size <n-pathways>). Each cell contains a cell array of strings which
are the gene symbols of the genes in that pathway.
pathway_genes_idx:
Same as pathway_genes_names, but each cell in the outer cell array is now an
array of gene indices corresponding to the gene positions in cube.mat and change-distributions.mat.
Hopefully this should be easier to use in matlab.
list_names:
Names of pathway lists prepared by Noa
list_pathway_names:
Call array. One item per list. Each item is a cell array of strings which are
the names of the pathways belonging to that list.
list_pathway_idx:
Same as list_pathway_names, but instead of listing the pathways by name, they
are given as indices into the previous pathway_xxx structures.
"""
mdict = dict(
README_PATHWAYS = README,
pathway_names = list_of_strings_to_matlab_cell_array(pathway_names),
pathway_genes_names = pathway_genes_names,
pathway_genes_idx = pathway_genes_idx,
list_names = list_of_strings_to_matlab_cell_array(list_names),
list_pathway_names = list_pathway_names,
list_pathway_idx = list_pathway_idx,
)
save_matfile(mdict, join(results_dir(), 'export', 'pathways.mat'))
def export_timing_info_for_all_fits(data, fitter, fits):
change_dist = compute_timing_info_for_all_fits(data, fitter, fits)
README = """\
mu:
The mean age of the change distribution for given gene and region.
Dimensions: <n-genes> X <n-regions>
std:
The standard deviation of the change distribution for given gene and region.
Dimensions: <n-genes> X <n-regions>
genes:
Gene names for the genes represented in other arrays
weights:
The change distributions for each gene and region.
Dimensions: <n-genes> X <n-regions> X <n-bins>
bin_centers:
The ages for the center of each bin used in calculating the histogram in "weights".
Dimensions: <n-bins> X 1
bin_edges:
The edges of the bins used in calculating the change histogram.
(centers can be calculated from the bin_edges, but it's convenient to have it pre-calculated)
Dimensions: <n-bins + 1> X 1
regions:
Region names for the regions represented in other arrays
age_scaler:
The scaling used for ages (i.e. 'log' means x' = log(x + 38/52))
"""
mdict = dict(
README_CHANGE_DISTRIBUTIONS=README,
genes=list_of_strings_to_matlab_cell_array(change_dist.genes),
regions=list_of_strings_to_matlab_cell_array(change_dist.regions),
age_scaler=scalers.unify(change_dist.age_scaler).cache_name(),
mu=change_dist.mu,
std=change_dist.std,
bin_edges=change_dist.bin_edges,
bin_centers=change_dist.bin_centers,
weights=change_dist.weights,
)
filename = join(
cache_dir(),
fit_results_relative_path(data, fitter) + '-change-dist.mat')
save_matfile(mdict, filename)
def save_to_mat(self):
filename = join(cache_dir(), 'both', 'dprime-all-pathways-and-regions-{}.mat'.format(self._filename_suffix))
mdict = dict(
pathway = list_of_strings_to_matlab_cell_array([x.pathway for x in self.res]),
r1 = list_of_strings_to_matlab_cell_array([x.r1 for x in self.res]),
r2 = list_of_strings_to_matlab_cell_array([x.r2 for x in self.res]),
score = np.array([x.score for x in self.res]),
delta = np.array([x.delta for x in self.res]),
weighted_delta = np.array([x.weighted_delta for x in self.res]),
mu1_years = np.array([x.mu1_years for x in self.res]),
mu2_years = np.array([x.mu2_years for x in self.res]),
pval = np.array([x.pval for x in self.res]),
pathway_size = np.array([x.pathway_size for x in self.res]),
)
print 'Saving results to {}'.format(filename)
savemat(filename, mdict, oned_as='column')
def export_timing_info_for_all_fits(data, fitter, fits):
change_dist = compute_timing_info_for_all_fits(data, fitter, fits)
README = """\
mu:
The mean age of the change distribution for given gene and region.
Dimensions: <n-genes> X <n-regions>
std:
The standard deviation of the change distribution for given gene and region.
Dimensions: <n-genes> X <n-regions>
genes:
Gene names for the genes represented in other arrays
weights:
The change distributions for each gene and region.
Dimensions: <n-genes> X <n-regions> X <n-bins>
bin_centers:
The ages for the center of each bin used in calculating the histogram in "weights".
Dimensions: <n-bins> X 1
bin_edges:
The edges of the bins used in calculating the change histogram.
(centers can be calculated from the bin_edges, but it's convenient to have it pre-calculated)
Dimensions: <n-bins + 1> X 1
regions:
Region names for the regions represented in other arrays
age_scaler:
The scaling used for ages (i.e. 'log' means x' = log(x + 38/52))
"""
mdict = dict(
README_CHANGE_DISTRIBUTIONS = README,
genes = list_of_strings_to_matlab_cell_array(change_dist.genes),
regions = list_of_strings_to_matlab_cell_array(change_dist.regions),
age_scaler = scalers.unify(change_dist.age_scaler).cache_name(),
mu = change_dist.mu,
std = change_dist.std,
bin_edges = change_dist.bin_edges,
bin_centers = change_dist.bin_centers,
weights = change_dist.weights,
)
filename = join(cache_dir(), fit_results_relative_path(data,fitter) + '-change-dist.mat')
save_matfile(mdict, filename)
def save_to_mat(self):
filename = join(
cache_dir(), 'both',
'dprime-all-pathways-and-regions-{}.mat'.format(
self._filename_suffix))
mdict = dict(
pathway=list_of_strings_to_matlab_cell_array(
[x.pathway for x in self.res]),
r1=list_of_strings_to_matlab_cell_array([x.r1 for x in self.res]),
r2=list_of_strings_to_matlab_cell_array([x.r2 for x in self.res]),
score=np.array([x.score for x in self.res]),
delta=np.array([x.delta for x in self.res]),
weighted_delta=np.array([x.weighted_delta for x in self.res]),
mu1_years=np.array([x.mu1_years for x in self.res]),
mu2_years=np.array([x.mu2_years for x in self.res]),
pval=np.array([x.pval for x in self.res]),
pathway_size=np.array([x.pathway_size for x in self.res]),
)
print 'Saving results to {}'.format(filename)
savemat(filename, mdict, oned_as='column')
def export_pathways():
change_dist = load_pickle(SingleRegion.change_dist_filename)
matlab_g2i = {g: (i + 1)
for i, g in enumerate(change_dist.genes)
} # NOTE that matlab is one based
pathways = pathway_lists.read_all_pathways()
pathway_names = pathways.keys() # make sure the order stays fixed
pathway_genes_names = np.array([
list_of_strings_to_matlab_cell_array(pathways[p])
for p in pathway_names
],
dtype=object)
pathway_genes_idx = np.array([
np.array([matlab_g2i[g] for g in pathways[p]]) for p in pathway_names
],
dtype=object)
matlab_p2i = {p: (i + 1)
for i, p in enumerate(pathway_names)
} # NOTE matlab indexing is one based
list_names = pathway_lists.all_pathway_lists()
list_pathway_names = np.empty(len(list_names), dtype=object)
list_pathway_idx = np.empty(len(list_names), dtype=object)
for i, listname in enumerate(list_names):
pathways_in_list = pathway_lists.list_to_pathway_names(listname)
list_pathway_names[i] = list_of_strings_to_matlab_cell_array(
pathways_in_list)
list_pathway_idx[i] = [matlab_p2i[p] for p in pathways_in_list]
README = """\
pathway_names:
Cell array of all pathway names. The name in cell number k is the name of the
pathway at position k in "pathway_genes_names" and "pathway_genes_idx".
pathway_genes_names:
Cell array (size <n-pathways>). Each cell contains a cell array of strings which
are the gene symbols of the genes in that pathway.
pathway_genes_idx:
Same as pathway_genes_names, but each cell in the outer cell array is now an
array of gene indices corresponding to the gene positions in cube.mat and change-distributions.mat.
Hopefully this should be easier to use in matlab.
list_names:
Names of pathway lists prepared by Noa
list_pathway_names:
Call array. One item per list. Each item is a cell array of strings which are
the names of the pathways belonging to that list.
list_pathway_idx:
Same as list_pathway_names, but instead of listing the pathways by name, they
are given as indices into the previous pathway_xxx structures.
"""
mdict = dict(
README_PATHWAYS=README,
pathway_names=list_of_strings_to_matlab_cell_array(pathway_names),
pathway_genes_names=pathway_genes_names,
pathway_genes_idx=pathway_genes_idx,
list_names=list_of_strings_to_matlab_cell_array(list_names),
list_pathway_names=list_pathway_names,
list_pathway_idx=list_pathway_idx,
)
save_matfile(mdict, join(results_dir(), 'export', 'pathways.mat'))
def save_as_mat_files(data, fitter, fits, has_change_distributions):
for dataset in data.datasets:
filename = join(cache_dir(), fit_results_relative_path(dataset,fitter) + '.mat')
dataset_fits = fits[dataset.name]
print 'Saving mat file to {}'.format(filename)
shape = fitter.shape
gene_names = dataset.gene_names
gene_idx = {g:i for i,g in enumerate(gene_names)}
n_genes = len(gene_names)
region_names = dataset.region_names
region_idx = {r:i for i,r in enumerate(region_names)}
n_regions = len(region_names)
write_theta = shape.can_export_params_to_matlab()
if write_theta:
theta = init_array(np.NaN, shape.n_params(), n_genes,n_regions)
else:
theta = np.NaN
fit_scores = init_array(np.NaN, n_genes,n_regions)
LOO_scores = init_array(np.NaN, n_genes,n_regions)
fit_predictions = init_array(np.NaN, *dataset.expression.shape)
LOO_predictions = init_array(np.NaN, *dataset.expression.shape)
high_res_predictions = init_array(np.NaN, cfg.n_curve_points_to_plot, n_genes, n_regions)
scaled_high_res_ages = np.linspace(dataset.ages.min(), dataset.ages.max(), cfg.n_curve_points_to_plot)
original_high_res_ages = scalers.unify(dataset.age_scaler).unscale(scaled_high_res_ages)
if has_change_distributions:
change_distribution_bin_centers = fits.change_distribution_params.bin_centers
n_bins = len(change_distribution_bin_centers)
change_distribution_weights = init_array(np.NaN, n_bins, n_genes, n_regions)
else:
change_distribution_bin_centers = []
change_distribution_weights = []
for (g,r),fit in dataset_fits.iteritems():
series = dataset.get_one_series(g,r)
ig = gene_idx[g]
ir = region_idx[r]
fit_scores[ig,ir] = fit.fit_score
LOO_scores[ig,ir] = fit.LOO_score
if write_theta and fit.theta is not None:
theta[:,ig,ir] = fit.theta
if fit.fit_predictions is not None:
fit_predictions[series.original_inds,ig,ir] = fit.fit_predictions
if fit.LOO_predictions is not None:
LOO_predictions[series.original_inds,ig,ir] = fit.LOO_predictions
if fit.theta is not None:
high_res_predictions[:,ig,ir] = shape.f(fit.theta, scaled_high_res_ages)
change_weights = getattr(fit,'change_distribution_weights',None)
if change_weights is not None:
change_distribution_weights[:,ig,ir] = change_weights
mdict = dict(
gene_names = list_of_strings_to_matlab_cell_array(gene_names),
region_names = list_of_strings_to_matlab_cell_array(region_names),
theta = theta,
fit_scores = fit_scores,
LOO_scores = LOO_scores,
fit_predictions = fit_predictions,
LOO_predictions = LOO_predictions,
high_res_predictions = high_res_predictions,
high_res_ages = original_high_res_ages,
change_distribution_bin_centers = change_distribution_bin_centers,
change_distribution_weights = change_distribution_weights,
)
savemat(filename, mdict, oned_as='column')
def save_as_mat_files(data, fitter, fits, has_change_distributions):
for dataset in data.datasets:
filename = join(cache_dir(),
fit_results_relative_path(dataset, fitter) + '.mat')
dataset_fits = fits[dataset.name]
print 'Saving mat file to {}'.format(filename)
shape = fitter.shape
gene_names = dataset.gene_names
gene_idx = {g: i for i, g in enumerate(gene_names)}
n_genes = len(gene_names)
region_names = dataset.region_names
region_idx = {r: i for i, r in enumerate(region_names)}
n_regions = len(region_names)
write_theta = shape.can_export_params_to_matlab()
if write_theta:
theta = init_array(np.NaN, shape.n_params(), n_genes, n_regions)
else:
theta = np.NaN
fit_scores = init_array(np.NaN, n_genes, n_regions)
LOO_scores = init_array(np.NaN, n_genes, n_regions)
fit_predictions = init_array(np.NaN, *dataset.expression.shape)
LOO_predictions = init_array(np.NaN, *dataset.expression.shape)
high_res_predictions = init_array(np.NaN, cfg.n_curve_points_to_plot,
n_genes, n_regions)
scaled_high_res_ages = np.linspace(dataset.ages.min(),
dataset.ages.max(),
cfg.n_curve_points_to_plot)
original_high_res_ages = scalers.unify(
dataset.age_scaler).unscale(scaled_high_res_ages)
if has_change_distributions:
change_distribution_bin_centers = fits.change_distribution_params.bin_centers
n_bins = len(change_distribution_bin_centers)
change_distribution_weights = init_array(np.NaN, n_bins, n_genes,
n_regions)
else:
change_distribution_bin_centers = []
change_distribution_weights = []
for (g, r), fit in dataset_fits.iteritems():
series = dataset.get_one_series(g, r)
ig = gene_idx[g]
ir = region_idx[r]
fit_scores[ig, ir] = fit.fit_score
LOO_scores[ig, ir] = fit.LOO_score
if write_theta and fit.theta is not None:
theta[:, ig, ir] = fit.theta
if fit.fit_predictions is not None:
fit_predictions[series.original_inds, ig,
ir] = fit.fit_predictions
if fit.LOO_predictions is not None:
LOO_predictions[series.original_inds, ig,
ir] = fit.LOO_predictions
if fit.theta is not None:
high_res_predictions[:, ig,
ir] = shape.f(fit.theta,
scaled_high_res_ages)
change_weights = getattr(fit, 'change_distribution_weights', None)
if change_weights is not None:
change_distribution_weights[:, ig, ir] = change_weights
mdict = dict(
gene_names=list_of_strings_to_matlab_cell_array(gene_names),
region_names=list_of_strings_to_matlab_cell_array(region_names),
theta=theta,
fit_scores=fit_scores,
LOO_scores=LOO_scores,
fit_predictions=fit_predictions,
LOO_predictions=LOO_predictions,
high_res_predictions=high_res_predictions,
high_res_ages=original_high_res_ages,
change_distribution_bin_centers=change_distribution_bin_centers,
change_distribution_weights=change_distribution_weights,
)
savemat(filename, mdict, oned_as='column')
