Dear team,
When I run the data.plt.paga_plot, it shows: "TypeError: 'NoneType' object is not callable." What could be the possible reasons?
Thanks,
sunfei
data.plt.paga_plot(
... adjacency='connectivities_tree', # keyword to use for paga or paga tree
... threshold=0.01, # prune edges lower than threshold
... layout='fr' # the method to layout each node
... )
Traceback (most recent call last):
File "", line 1, in
File "/home/sunfei/.conda/envs/stereo_env/lib/python3.8/site-packages/stereo/plots/decorator.py", line 52, in wrapped
fig: Figure = func(*args, **kwargs)
TypeError: 'NoneType' object is not callable
stereo_env.version
'1.6.2'
The full code that I executed is shown below:
import os
import stereo as stereo_env
import warnings
warnings.filter
warnings('ignore')
base_dir = "/mnt/sdb/sunfei/stomics/Y01502PD/Mouse_Brain/feature_expression"
os.chdir(base_dir)
read data
data = stereo_env.io.read_h5ad('/mnt/sdb/sunfei/stomics/Multi_Sample/06.Time_Series_analysis/AnnData_075/Embyro_E9.5.h5ad')
preprocessing
data.tl.cal_qc()
data.tl.raw_checkpoint()
data.tl.normalize_total(target_sum=1e4)
data.tl.log1p()
hvg
data.tl.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5, res_key='highly_variable_genes', n_top_genes=None)
data.tl.scale(zero_center=False)
embedding
data.tl.pca(use_highly_genes=True, hvg_res_key='highly_variable_genes', n_pcs=20, res_key='pca', svd_solver='arpack')
data.tl.neighbors(pca_res_key='pca', n_pcs=30, res_key='neighbors', n_jobs=-1)
data.tl.umap(pca_res_key='pca', neighbors_res_key='neighbors', res_key='umap')
data.tl.paga(groups='annotation', neighbors_key='neighbors')
data.tl.result['paga']
#PAGA可视化
data.plt.paga_plot(
adjacency='connectivities_tree', # keyword to use for paga or paga tree
threshold=0.01, # prune edges lower than threshold
layout='fr' # the method to layout each node
)
Dear team,
When I run the data.plt.paga_plot, it shows: "TypeError: 'NoneType' object is not callable." What could be the possible reasons?
Thanks,
sunfei
The full code that I executed is shown below:
import os
import stereo as stereo_env
import warnings
warnings.filter
warnings('ignore')
base_dir = "/mnt/sdb/sunfei/stomics/Y01502PD/Mouse_Brain/feature_expression"
os.chdir(base_dir)
read data
data = stereo_env.io.read_h5ad('/mnt/sdb/sunfei/stomics/Multi_Sample/06.Time_Series_analysis/AnnData_075/Embyro_E9.5.h5ad')
preprocessing
data.tl.cal_qc()
data.tl.raw_checkpoint()
data.tl.normalize_total(target_sum=1e4)
data.tl.log1p()
hvg
data.tl.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5, res_key='highly_variable_genes', n_top_genes=None)
data.tl.scale(zero_center=False)
embedding
data.tl.pca(use_highly_genes=True, hvg_res_key='highly_variable_genes', n_pcs=20, res_key='pca', svd_solver='arpack')
data.tl.neighbors(pca_res_key='pca', n_pcs=30, res_key='neighbors', n_jobs=-1)
data.tl.umap(pca_res_key='pca', neighbors_res_key='neighbors', res_key='umap')
data.tl.paga(groups='annotation', neighbors_key='neighbors')
data.tl.result['paga']
#PAGA可视化
data.plt.paga_plot(
adjacency='connectivities_tree', # keyword to use for paga or paga tree
threshold=0.01, # prune edges lower than threshold
layout='fr' # the method to layout each node
)