def _augmented_score_func(
a: Agglomerate3D) -> Tuple[str, str, int, float, List[float]]:
return a.linkage_cell, \
a.linkage_region, \
a.max_region_diff, \
a.region_dist_scale, \
[a.compute_tree_score(m) for m in TREE_SCORE_OPTIONS]
def _agglomerate_func(lc, lr, cta, ra, mrd, rds, ic, data):
agglomerate = Agglomerate3D(linkage_cell=lc,
linkage_region=lr,
cell_type_affinity=cta,
region_affinity=ra,
max_region_diff=mrd,
region_dist_scale=rds,
verbose=False,
pbar=False,
integrity_check=ic)
agglomerate.agglomerate(data)
return agglomerate
if __name__ == '__main__':
ct_data_loader = CTDataLoader(
'mouse',
reprocess=True,
remove_correlated='ct',
normalize=True,
gene_selection_method='elastic-net',
model_cache_dir='models',
alpha=3e-1, # np.logspace(-4, 2, num=6),
dim_reduction=None,
n_components=50)
agglomerate = Agglomerate3D(cell_type_affinity=spearmanr_connectivity,
linkage_cell='complete',
linkage_region='homolog_mnn',
max_region_diff=1,
region_dist_scale=.7,
verbose=False,
pbar=True,
integrity_check=True)
start = time.process_time()
agglomerate.agglomerate(ct_data_loader)
end = time.perf_counter()
pd.options.display.width = 0
pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.max_columns', None)
print(agglomerate.linkage_mat_readable)
print(agglomerate.compute_tree_score('BME'))
agglomerate.view_tree3d()
print(f'Total time elapsed: {(end - start) / 10}s')
def _basic_score_func(a: Agglomerate3D) -> List[float]:
return [a.compute_tree_score(m) for m in TREE_SCORE_OPTIONS]