def main(species,
window_size,
motif_metadata,
bin_sorted_hits,
group_loci=100000):
motif_metadata = pd.read_csv(motif_metadata, sep='\t', header=None)
motif_metadata.columns = ['dataset_id', 'factor', 'source']
motif_metadata = motif_metadata.set_index('dataset_id')
motif_metadata = motif_metadata.drop_duplicates()
data = DataInterface(species,
window_size=window_size,
download_if_not_exists=False,
make_new=False,
load_genes=False)
print(data.path)
raise Exception()
data.create_binding_dataset(TECHNOLOGY, motif_metadata.index.values,
**motif_metadata.to_dict('list'))
id_to_idx_map = dict(
zip(data.list_binding_datasets(TECHNOLOGY),
np.arange(len(data.list_binding_datasets(TECHNOLOGY)))))
current_pos = 0
last_added_chunk = 0
i = 0
rows, cols, scores = [], [], []
with open(bin_sorted_hits, 'r') as f:
for line in f:
motif_id, bin_num, score = line.strip().split()
bin_num = int(bin_num)
if bin_num < current_pos:
raise Exception('Input file not sorted!')
elif bin_num > current_pos and i >= group_loci:
print('Adding matrix segment ...')
matrix_form = sparse.coo_matrix((scores, (rows, cols))).tocsr()
data.append_csr(TECHNOLOGY, matrix_form)
last_added_chunk = bin_num
i = 0
rows, cols, scores = [], [], []
tf_idx = id_to_idx_map[motif_id]
rows.append(bin_num - last_added_chunk)
cols.append(tf_idx)
scores.append(int(score))
current_pos = bin_num
i += 1
if len(rows) > 0:
matrix_form = sparse.coo_matrix((scores, (rows, cols))).tocsr()
data.append_csr(TECHNOLOGY, matrix_form)
def main(args):
cistrome_metadata = pd.read_csv(args.cistrome_metadata,
sep='\t').set_index('DCid')
cistrome_metadata.index = cistrome_metadata.index.astype(str)
data = DataInterface(args.species,
window_size=args.window_size,
download_if_not_exists=False,
make_new=False,
load_genes=True)
rp_map_styles = data.get_rp_maps()
if len(rp_map_styles) == 0:
basic_rp_map, enhanced_rp_map = data.build_binned_rp_map(
'basic', 10000), data.build_binned_rp_map('enhanced', 10000)
data.add_rp_map('basic_10K', basic_rp_map)
data.add_rp_map('enhanced_10K', enhanced_rp_map)
else:
basic_rp_map = data.get_rp_map('basic_10K')
enhanced_rp_map = data.get_rp_map('enhanced_10K')
for arr_name in args.coverage_arrays:
coverage_array = np.load(arr_name)
technology, dataset_id = os.path.basename(arr_name).split('_')
dataset_id = '.'.join(dataset_id.split('.')[:-1])
metadata_headers = data.get_metadata_headers(technology)
meta_dict = cistrome_metadata.loc[dataset_id,
metadata_headers].to_dict()
data.add_profile_data(technology, dataset_id, coverage_array,
[basic_rp_map, enhanced_rp_map],
['basic_10K', 'enhanced_10K'], **meta_dict)
def main(species, window_size, cistrome_metadata, motif_metadata, index_files):
cistrome_metadata = pd.read_csv(cistrome_metadata,
sep='\t').set_index('DCid')
cistrome_metadata.index = cistrome_metadata.index.astype(str)
motif_metadata = pd.read_csv(motif_metadata, sep='\t', header=None)
motif_metadata.columns = ['dataset_id', 'factor', 'source']
motif_metadata = motif_metadata.set_index('dataset_id')
motif_metadata = motif_metadata.drop_duplicates()
data = DataInterface(species,
window_size=window_size,
download_if_not_exists=False,
make_new=False,
load_genes=False)
dataset_ids = motif_metadata.index.values
data.create_binding_dataset('Motif', dataset_ids)
def main(species, window_size, cistrome_metadata, motif_metadata, index_files):
cistrome_metadata = pd.read_csv(cistrome_metadata,
sep='\t').set_index('DCid')
cistrome_metadata.index = cistrome_metadata.index.astype(str)
motif_metadata = pd.read_csv(motif_metadata, sep='\t', header=None)
motif_metadata.columns = ['dataset_id', 'factor', 'source']
motif_metadata = motif_metadata.set_index('dataset_id')
motif_metadata = motif_metadata.drop_duplicates()
data = DataInterface(species,
window_size=window_size,
download_if_not_exists=False,
make_new=False,
load_genes=False)
for index_file in index_files:
with open(index_file, 'r') as f:
hit_bins = np.array([int(ind.strip()) for ind in f.readlines()])
technology, dataset_id = os.path.basename(index_file).split('_')
dataset_id = '.'.join(dataset_id.split('.')[:-1])
metadata_headers = data.get_metadata_headers(technology)
if technology == 'Motifs':
meta_dict = motif_metadata.loc[dataset_id,
metadata_headers].to_dict()
meta_dict['source'] = 'jaspar'
else:
meta_dict = cistrome_metadata.loc[dataset_id,
metadata_headers].to_dict()
data.add_binding_data(technology, dataset_id, hit_bins, **meta_dict)