def compute_lisi(A,
B,
combined_meta,
batch_key,
cell_type_key,
aligner_fcn,
do_B_transform=False):
A = aligner_fcn(A)
if do_B_transform:
B = aligner_fcn(B)
X = np.concatenate((A, B))
print(X.shape)
assert (X.shape[0] == combined_meta.shape[0])
return metrics.lisi2(X, combined_meta, [batch_key, cell_type_key])
# For each alignment method
for i, method in enumerate(args.methods):
print('\t{}'.format(method))
method_key = '{}_aligned'.format(method)
log_dir = join(log_dir_root, '{}_{}'.format(task.as_path(), method))
if not exists(log_dir):
makedirs(log_dir)
if method == 'None':
pass
#
plot_alignment_results(log_dir, task_adata, method, task)
lisi_scores.append(
metrics.lisi2(task_adata.obsm['PCA'],
task_adata.obs, [task.batch_key, task.ct_key],
perplexity=30))
else:
if 'ICP' in method:
runners.run_ICP_methods(datasets, task, task_adata, method,
log_dir, args)
#lisi_scores.append(metrics.lisi2(task_adata.obsm[method_key], task_adata.obs, [task.batch_key, task.ct_key], perplexity=30)
elif method == 'ScAlign':
runners.run_scAlign(datasets, task, task_adata, method,
log_dir, args)
elif method == 'MNN':
runners.run_MNN(datasets, task, task_adata, method, log_dir,
args)
elif method == 'SeuratV3':
#task_adata = datasets[task.ds_key]
runners.run_Seurat(datasets, task, task_adata, method, log_dir,
if task.leave_out_ct is not None:
task_idx = (datasets[task.ds_key].obs[task.batch_key] == task.source_batch) | ((datasets[task.ds_key].obs[task.batch_key] == task.target_batch) & (datasets[task.ds_key].obs[task.ct_key] != task.leave_out_ct))
# elif task.leave_out_source_ct is not None:
# task_idx = ((datasets[task.ds_key].obs[task.batch_key] == task.source_batch) & (datasets[task.ds_key].obs[task.ct_key] != task.leave_out_source_ct)) | (datasets[task.ds_key].obs[task.batch_key] == task.target_batch)
else:
task_idx = (datasets[task.ds_key].obs[task.batch_key] == task.source_batch) | (datasets[task.ds_key].obs[task.batch_key] == task.target_batch)
task_adata = datasets[task.ds_key][task_idx]
method_key = '{}_aligned'.format(args.method)
kbet_stats = None
if args.method == 'None':
plot_alignment_results(log_dir, task_adata, args.method, task)
if args.input_space == 'PCA':
lisi_score = metrics.lisi2(task_adata.obsm['PCA'], task_adata.obs, [task.batch_key, task.ct_key], perplexity=30)
# lisi_score_batch = metrics.lisi2(task_adata.obsm['PCA'], task_adata.obs, [task.batch_key], perplexity=30)
# batch_A_adata = task_adata[task_adata.obs[task.batch_key] == task.source_batch, :]
# print(f'batch_A_adata.obsm["PCA"].shape: {batch_A_adata.obsm["PCA"].shape}')
# lisi_score_celltype = metrics.lisi2(batch_A_adata.obsm['PCA'], batch_A_adata.obs, [task.ct_key], perplexity=30)
else:
lisi_score = metrics.lisi2(task_adata.X, task_adata.obs, [task.batch_key, task.ct_key], perplexity=30)
# lisi_score_batch = metrics.lisi2(task_adata.X, task_adata.obs, [task.batch_key], perplexity=30)
# batch_A_adata = task_adata[task_adata.obs[task.batch_key] == task.source_batch, :]
# print(f'batch_A_adata.shape: {batch_A_adata.shape}')
# lisi_score_celltype = metrics.lisi2(batch_A_adata.X, batch_A_adata.obs, [task.ct_key], perplexity=30)
if args.do_kBET_test:
try:
kbet_stats = metrics.kBET(task_adata.X, task_adata.obs, task.batch_key, args.kBET_env_path)
print(kbet_stats)
print('kBET medians:')