def load(dataset):
datadir = project_dirs.data_dir()
filename = '{}_allGenes.mat'.format(dataset)
path = join(datadir, filename)
if cfg.verbosity > 0:
print 'Loading dataset {} from {}'.format(dataset, path)
mat = loadmat(path)
ages = np.array(mat['ages'].flat)
gene_names = np.array(
matlab_cell_array_to_list_of_strings(mat['gene_names']))
region_names = np.array(
matlab_cell_array_to_list_of_strings(mat['region_names']))
if 'genders' in mat:
genders = np.array(
matlab_cell_array_to_list_of_strings(mat['genders']))
else:
genders = None
expression = mat['expression']
if expression.ndim == 2: # extend shape to represent a single region name
expression.shape = list(expression.shape) + [1]
# average expression for duplicate genes
dct = defaultdict(
list) # gene_name -> list of indices where it appears
for i, g in enumerate(gene_names):
dct[g].append(i)
new_gene_names = sorted(set(gene_names))
new_expression = np.empty(
[len(ages), len(new_gene_names),
len(region_names)])
for i, g in enumerate(new_gene_names):
idx = dct[g]
new_expression[:, i, :] = expression[:, idx, :].mean(axis=1)
gene_names = np.array(new_gene_names)
expression = new_expression
# make sure ages are sorted (for colantuoni there are 2 datapoints that aren't)
inds = np.argsort(ages)
ages = ages[inds]
genders = genders[inds] if genders is not None else None
expression = expression[inds, :, :]
exons = _get_exons(gene_names) if cfg.exon_level else None
res = OneDataset(expression=expression,
gene_names=gene_names,
region_names=region_names,
genders=genders,
ages=ages,
name=dataset,
exons=exons).restrict_pathway('all')
sorted_regions = cfg.sorted_regions.get(dataset)
if sorted_regions is not None:
res = res.restrict_regions(cfg.sorted_regions[dataset])
return res
def load(dataset):
datadir = project_dirs.data_dir()
filename = '{}_allGenes.mat'.format(dataset)
path = join(datadir,filename)
if cfg.verbosity > 0:
print 'Loading dataset {} from {}'.format(dataset,path)
mat = loadmat(path)
ages = np.array(mat['ages'].flat)
gene_names = np.array(matlab_cell_array_to_list_of_strings(mat['gene_names']))
region_names = np.array(matlab_cell_array_to_list_of_strings(mat['region_names']))
if 'genders' in mat:
genders = np.array(matlab_cell_array_to_list_of_strings(mat['genders']))
else:
genders = None
expression = mat['expression']
if expression.ndim == 2: # extend shape to represent a single region name
expression.shape = list(expression.shape)+[1]
# average expression for duplicate genes
dct = defaultdict(list) # gene_name -> list of indices where it appears
for i,g in enumerate(gene_names):
dct[g].append(i)
new_gene_names = sorted(set(gene_names))
new_expression = np.empty([len(ages),len(new_gene_names),len(region_names)])
for i,g in enumerate(new_gene_names):
idx = dct[g]
new_expression[:,i,:] = expression[:,idx,:].mean(axis=1)
gene_names = np.array(new_gene_names)
expression = new_expression
# make sure ages are sorted (for colantuoni there are 2 datapoints that aren't)
inds = np.argsort(ages)
ages = ages[inds]
genders = genders[inds] if genders is not None else None
expression = expression[inds,:,:]
res = OneDataset(
expression = expression,
gene_names = gene_names,
region_names = region_names,
genders = genders,
ages = ages,
name = dataset
).restrict_pathway('all')
sorted_regions = cfg.sorted_regions.get(dataset)
if sorted_regions is not None:
res = res.restrict_regions(cfg.sorted_regions[dataset])
return res
def load_kang_tree_distances():
filename = join(project_dirs.data_dir(), 'kangTreeDistances.mat')
mat = loadmat(filename)
distances = mat['developingTreeDistances']
regions = matlab_cell_array_to_list_of_strings(mat['regions'])
dct_regions = {r:i for i,r in enumerate(regions)}
return Bunch(
regions = regions,
dct_regions = dct_regions,
distances = distances,
)
def load_kang_tree_distances():
filename = join(project_dirs.data_dir(), 'kangTreeDistances.mat')
mat = loadmat(filename)
distances = mat['developingTreeDistances']
regions = matlab_cell_array_to_list_of_strings(mat['regions'])
dct_regions = {r: i for i, r in enumerate(regions)}
return Bunch(
regions=regions,
dct_regions=dct_regions,
distances=distances,
)
