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Skip labels before loop #180

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Skip labels before loop #180

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93 changes: 47 additions & 46 deletions src/scib_metrics/metrics/_kbet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,69 +138,70 @@ def kbet_per_label(
conn_graph = X.knn_graph_connectivities

# prepare call of kBET per cluster
kbet_scores = {"cluster": [], "kBET": []}
for clus in np.unique(labels):
clusters = []
clusters, counts = np.unique(labels, return_counts=True)
skipped = clusters[counts > 10]
clusters = clusters[counts <= 10]
kbet_scores = {"cluster": list(skipped), "kBET": [np.nan] * len(skipped)}
logger.info(f"{len(skipped)} clusters consist of a single batch or are too small. Skip.")

for clus in clusters:
# subset by label
mask = labels == clus
conn_graph_sub = conn_graph[mask, :][:, mask]
conn_graph_sub.sort_indices()
n_obs = conn_graph_sub.shape[0]
batches_sub = batches[mask]

# check if neighborhood size too small or only one batch in subset
if np.logical_or(n_obs < 10, len(np.unique(batches_sub)) == 1):
logger.info(f"{clus} consists of a single batch or is too small. Skip.")
score = np.nan
else:
quarter_mean = np.floor(np.mean(pd.Series(batches_sub).value_counts()) / 4).astype("int")
k0 = np.min([70, np.max([10, quarter_mean])])
# check k0 for reasonability
if k0 * n_obs >= size_max:
k0 = np.floor(size_max / n_obs).astype("int")
quarter_mean = np.floor(np.mean(pd.Series(batches_sub).value_counts()) / 4).astype("int")
k0 = np.min([70, np.max([10, quarter_mean])])
# check k0 for reasonability
if k0 * n_obs >= size_max:
k0 = np.floor(size_max / n_obs).astype("int")

n_comp, labs = scipy.sparse.csgraph.connected_components(conn_graph_sub, connection="strong")

if n_comp == 1: # a single component to compute kBET on
try:
diffusion_n_comps = np.min([diffusion_n_comps, n_obs - 1])
nn_graph_sub = diffusion_nn(conn_graph_sub, k=k0, n_comps=diffusion_n_comps)
# call kBET
score, _, _ = kbet(
nn_graph_sub,
batches=batches_sub,
alpha=alpha,
)
except ValueError:
logger.info("Diffusion distance failed. Skip.")
score = 0 # i.e. 100% rejection

n_comp, labs = scipy.sparse.csgraph.connected_components(conn_graph_sub, connection="strong")
else:
# check the number of components where kBET can be computed upon
comp_size = pd.Series(labs).value_counts()
# check which components are small
comp_size_thresh = 3 * k0
idx_nonan = np.flatnonzero(np.in1d(labs, comp_size[comp_size >= comp_size_thresh].index))

# check if 75% of all cells can be used for kBET run
if len(idx_nonan) / len(labs) >= 0.75:
# create another subset of components, assume they are not visited in a diffusion process
conn_graph_sub_sub = conn_graph_sub[idx_nonan, :][:, idx_nonan]
conn_graph_sub_sub.sort_indices()

if n_comp == 1: # a single component to compute kBET on
try:
diffusion_n_comps = np.min([diffusion_n_comps, n_obs - 1])
nn_graph_sub = diffusion_nn(conn_graph_sub, k=k0, n_comps=diffusion_n_comps)
diffusion_n_comps = np.min([diffusion_n_comps, conn_graph_sub_sub.shape[0] - 1])
nn_results_sub_sub = diffusion_nn(conn_graph_sub_sub, k=k0, n_comps=diffusion_n_comps)
# call kBET
score, _, _ = kbet(
nn_graph_sub,
batches=batches_sub,
nn_results_sub_sub,
batches=batches_sub[idx_nonan],
alpha=alpha,
)
except ValueError:
logger.info("Diffusion distance failed. Skip.")
score = 0 # i.e. 100% rejection

else:
# check the number of components where kBET can be computed upon
comp_size = pd.Series(labs).value_counts()
# check which components are small
comp_size_thresh = 3 * k0
idx_nonan = np.flatnonzero(np.in1d(labs, comp_size[comp_size >= comp_size_thresh].index))

# check if 75% of all cells can be used for kBET run
if len(idx_nonan) / len(labs) >= 0.75:
# create another subset of components, assume they are not visited in a diffusion process
conn_graph_sub_sub = conn_graph_sub[idx_nonan, :][:, idx_nonan]
conn_graph_sub_sub.sort_indices()

try:
diffusion_n_comps = np.min([diffusion_n_comps, conn_graph_sub_sub.shape[0] - 1])
nn_results_sub_sub = diffusion_nn(conn_graph_sub_sub, k=k0, n_comps=diffusion_n_comps)
# call kBET
score, _, _ = kbet(
nn_results_sub_sub,
batches=batches_sub[idx_nonan],
alpha=alpha,
)
except ValueError:
logger.info("Diffusion distance failed. Skip.")
score = 0 # i.e. 100% rejection
else: # if there are too many too small connected components, set kBET score to 0
score = 0 # i.e. 100% rejection
else: # if there are too many too small connected components, set kBET score to 0
score = 0 # i.e. 100% rejection

kbet_scores["cluster"].append(clus)
kbet_scores["kBET"].append(score)
Expand Down