def silhouette(adata, group_key, metric='euclidean', scale=True):
"""
This ASW function was taken from scIB:
Title: scIB
Authors: Malte Luecken,
Maren Buettner,
Daniel Strobl,
Michaela Mueller
Date: 4th October 2020
Code version: 0.2.0
Availability: https://github.com/theislab/scib/blob/master/scIB/metrics.py
wrapper for sklearn silhouette function values range from [-1, 1] with 1 being an ideal fit, 0 indicating
overlapping clusters and -1 indicating misclassified cells
"""
adata = remove_sparsity(adata)
labels = adata.obs[group_key].values
labels_encoded = LabelEncoder().fit_transform(labels)
asw = silhouette_score(adata.X, labels_encoded, metric=metric)
if scale:
asw = (asw + 1)/2
return asw
import scanpy as sc
import os
import scarches as sca
from scarches.dataset.trvae.data_handling import remove_sparsity
import matplotlib.pyplot as plt
n_epochs_vae = 100
batch_key = "study"
cell_type_key = "cell_type"
adata_all = sc.read(
os.path.expanduser(
f'~/Documents/benchmarking_datasets/pancreas_normalized.h5ad'))
adata = adata_all.raw.to_adata()
adata = remove_sparsity(adata)
adata_conditions = adata.obs[batch_key].tolist()
trvae = sca.models.TRVAE(adata=adata,
condition_key=batch_key,
hidden_layer_sizes=[128, 128],
use_mmd=False)
trvae.train(
n_epochs=n_epochs_vae,
alpha_epoch_anneal=200,
monitor_only_val=False,
)
adata_latent = sc.AnnData(trvae.get_latent())
adata_latent.obs['celltype'] = adata.obs[cell_type_key].tolist()
adata_latent.obs['batch'] = adata.obs[batch_key].tolist()
def opt_louvain(adata,
label_key,
cluster_key,
function=None,
resolutions=None,
inplace=True,
plot=False,
verbose=True,
**kwargs):
"""
params:
label_key: name of column in adata.obs containing biological labels to be
optimised against
cluster_key: name of column to be added to adata.obs during clustering.
Will be overwritten if exists and `force=True`
function: function that computes the cost to be optimised over. Must take as
arguments (adata, group1, group2, **kwargs) and returns a number for maximising
resolutions: list if resolutions to be optimised over. If `resolutions=None`,
default resolutions of 20 values ranging between 0.1 and 2 will be used
returns:
res_max: resolution of maximum score
score_max: maximum score
score_all: `pd.DataFrame` containing all scores at resolutions. Can be used to plot the score profile.
clustering: only if `inplace=False`, return cluster assignment as `pd.Series`
plot: if `plot=True` plot the score profile over resolution
"""
adata = remove_sparsity(adata)
if resolutions is None:
n = 20
resolutions = [2 * x / n for x in range(1, n + 1)]
score_max = 0
res_max = resolutions[0]
clustering = None
score_all = []
# maren's edit - recompute neighbors if not existing
try:
adata.uns['neighbors']
except KeyError:
if verbose:
print('computing neigbours for opt_cluster')
sc.pp.neighbors(adata)
for res in resolutions:
sc.tl.louvain(adata, resolution=res, key_added=cluster_key)
score = function(adata, label_key, cluster_key, **kwargs)
score_all.append(score)
if score_max < score:
score_max = score
res_max = res
clustering = adata.obs[cluster_key]
del adata.obs[cluster_key]
if verbose:
print(f'optimised clustering against {label_key}')
print(f'optimal cluster resolution: {res_max}')
print(f'optimal score: {score_max}')
score_all = pd.DataFrame(zip(resolutions, score_all),
columns=('resolution', 'score'))
if plot:
# score vs. resolution profile
sns.lineplot(data=score_all, x='resolution',
y='score').set_title('Optimal cluster resolution profile')
plt.show()
if inplace:
adata.obs[cluster_key] = clustering
return res_max, score_max, score_all
else:
return res_max, score_max, score_all, clustering