gao-lab / slat Goto Github PK

scSLAT can't plot the mulberry diagram described in the article, hopefully this will be fixed.

Considering that the preprocessing method might lead to inconsistent results, could the author upload the h5ad file to the cloud storage?

What a great tool for spatial slides alignment! :-)

I have met an error when I used draw_3D function to plot cell type alignment between two slides, an error occurred:

"TypeError: draw_lines() takes from 4 to 6 positional arguments but 7 were given."

Hope for your reply. Thanks!

Excellent work! I noticed that in your tutorial available at [https://slat.readthedocs.io/en/latest/tutorials/basic_usage.html], you provided two preprocessed datasets. Could you please provide additional preprocessed datasets?
Thank you for your assistance.

Hello! Where is the example of spatial transcriptome and ATAC integration? I didn't find this part in your tutorial. Thank you for your help.

Hello, I encountered the following error when running this function:
matching_list, zip_res = run_SLAT_multi([adata1,adata2,adata3,adata4],k_cutoff=10,feature = "PCA")

ValueError: Input X contains NaN.

But there are no NAN or NA in my data.How do I fix this?

Detailed information:
Traceback (most recent call last):
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 463, in _process_worker
r = call_item()
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 291, in call
return self.fn(*self.args, **self.kwargs)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 589, in call
return [func(*args, **kwargs)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 589, in
return [func(*args, **kwargs)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/scSLAT/model/utils.py", line 304, in parall_SLAT
edges, features = load_anndatas([a1, a2], feature=feature, check_order=False)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/scSLAT/model/loaddata.py", line 234, in load_anndatas
sc.tl.pca(adata_all)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/scanpy/preprocessing/pca.py", line 188, in pca
X_pca = pca.fit_transform(X)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/base.py", line 1152, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/decomposition/_pca.py", line 460, in fit_transform
U, S, Vt = self._fit(X)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/decomposition/_pca.py", line 483, in _fit
X = self._validate_data(
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/base.py", line 605, in _validate_data
out = check_array(X, input_name="X", **check_params)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/utils/validation.py", line 957, in check_array
_assert_all_finite(
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/utils/validation.py", line 122, in _assert_all_finite
_assert_all_finite_element_wise(
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/sklearn/utils/validation.py", line 171, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input X contains NaN.
PCA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "test_v1.py", line 179, in
matching_list, zip_res = run_SLAT_multi([adata1,adata2,adata3,adata4],k_cutoff=10,feature = "PCA")
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/scSLAT/model/utils.py", line 309, in run_SLAT_multi
unfiltered_zip_res = Parallel(n_jobs=n_jobs)(delayed(parall_SLAT)(a1,a2,i)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 1952, in call
return output if self.return_generator else list(output)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 1595, in _get_outputs
yield from self._retrieve()
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 1699, in _retrieve
self._raise_error_fast()
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 1734, in _raise_error_fast
error_job.get_result(self.timeout)
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 736, in get_result
return self._return_or_raise()
File "/share/home/luxj/software/miniconda3/envs/scSLAT/lib/python3.8/site-packages/joblib/parallel.py", line 754, in _return_or_raise
raise self._result
ValueError: Input X contains NaN.
PCA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

Sessioninfo:
Package Version

anndata 0.9.2
asttokens 2.4.1
attrs 23.1.0
backcall 0.2.0
Brotli 1.1.0
certifi 2023.7.22
charset-normalizer 3.3.2
colorama 0.4.6
comm 0.2.0
contourpy 1.1.1
cycler 0.12.1
debugpy 1.8.0
decorator 5.1.1
executing 2.0.1
faiss-cpu 1.7.4
fastjsonschema 2.19.0
filelock 3.13.1
fonttools 4.44.1
fsspec 2023.10.0
get-annotations 0.1.2
h5py 3.10.0
harmony-pytorch 0.1.7
idna 3.4
igraph 0.10.8
importlib-metadata 6.8.0
importlib-resources 6.1.1
ipykernel 6.26.0
ipython 8.12.3
jedi 0.19.1
Jinja2 3.1.2
joblib 1.3.2
jsonschema 4.19.2
jsonschema-specifications 2023.11.1
jupyter_client 8.6.0
jupyter_core 5.5.0
kiwisolver 1.4.5
leidenalg 0.10.1
llvmlite 0.41.1
MarkupSafe 2.1.3
matplotlib 3.6.3
matplotlib-inline 0.1.6
mkl-fft 1.3.8
mkl-random 1.2.4
mkl-service 2.4.0
mpmath 1.3.0
natsort 8.4.0
nbformat 5.9.2
nest-asyncio 1.5.8
networkx 3.1
numba 0.58.1
numpy 1.24.4
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu12 2.18.1
nvidia-nvjitlink-cu12 12.3.52
nvidia-nvtx-cu12 12.1.105
opencv-python 4.8.1.78
packaging 23.2
pandas 2.0.3
parso 0.8.3
patsy 0.5.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 10.1.0
pip 23.3.1
pkgutil_resolve_name 1.3.10
platformdirs 4.0.0
plotly 5.18.0
pooch 1.8.0
prompt-toolkit 3.0.41
psutil 5.9.5
ptyprocess 0.7.0
pure-eval 0.2.2
pyg-lib 0.3.1+pt21cpu
Pygments 2.16.1
pynndescent 0.5.10
pynvml 11.5.0
pyparsing 3.1.1
PySocks 1.7.1
python-dateutil 2.8.2
pytz 2023.3.post1
PyYAML 6.0.1
pyzmq 25.1.1
referencing 0.31.0
requests 2.31.0
rpds-py 0.12.0
scanpy 1.9.6
scikit-learn 1.3.2
scikit-misc 0.2.0
scipy 1.10.1
scslat 0.2.1
seaborn 0.12.2
session-info 1.0.0
setuptools 68.2.2
six 1.16.0
stack-data 0.6.3
statsmodels 0.14.0
stdlib-list 0.9.0
sympy 1.12
tbb 2021.11.0
tenacity 8.2.3
texttable 1.7.0
threadpoolctl 3.2.0
torch 2.1.0
torch-cluster 1.6.3+pt21cpu
torch_geometric 2.4.0
torch-scatter 2.1.2+pt21cpu
torch-sparse 0.6.18+pt21cpu
torch-spline-conv 1.2.2+pt21cpu
torchaudio 2.1.0
torchvision 0.16.0
tornado 6.3.3
tqdm 4.66.1
traitlets 5.13.0
triton 2.1.0
typing_extensions 4.8.0
tzdata 2023.3
umap-learn 0.5.4
urllib3 2.1.0
wcwidth 0.2.10
wheel 0.41.3
zipp 3.17.0