Error for ov.utils.cal_paga and ov.single.pyVIA
Closed this issue · 6 comments
Thank you for developing OmicVerse. I am currently experiencing some issues:
When I run: Trajectory Inference with PAGA or Palantir this part of the ov.utils.cal_paga code, ov.utils.cal_paga(adata,use_time_prior=‘dpt_pseudotime’,vkey=‘paga’,
groups=‘clusters’) and ov.utils.cal_paga (adata,use_time_prior=‘palantir_pseudotime’,vkey=‘paga’,
groups=‘clusters’) and ov.utils.cal_paga(adata,use_time_prior=‘slingshot_pseudotime’,vkey=‘paga’,
groups=‘clusters’) and ov.single.pyVIA (adata=adata,adata_key='X_pca',adata_ncomps=80, basis='X_umap',
clusters='clusters',knn=30,random_seed=4,root_user=[4823],)
ValueError Traceback (most recent call last)
Cell In[16], line 1
----> 1 ov.utils.cal_paga(adata,use_time_prior='dpt_pseudotime',vkey='paga',
2 groups='clusters')
File ~/miniconda3/envs/omicverse/lib/python3.10/site-packages/omicverse/utils/_paga.py:293, in cal_paga(adata, groups, vkey, use_time_prior, root_key, end_key, threshold_root_end_prior, minimum_spanning_tree, copy)
290 adata.uns["paga"]["connectivities_tree"] = paga.connectivities_tree
291 adata.uns[f"{groups}_sizes"] = np.array(paga.ns)
--> 293 paga.compute_transitions()
294 adata.uns["paga"]["transitions_confidence"] = paga.transitions_confidence
295 adata.uns["paga"]["threshold"] = paga.threshold
File ~/miniconda3/envs/omicverse/lib/python3.10/site-packages/omicverse/utils/_paga.py:83, in PAGA_tree.compute_transitions(self)
81 vc = igraph.VertexClustering(g, membership=membership)
82 cg_full = vc.cluster_graph(combine_edges="sum")
---> 83 transitions = get_sparse_from_igraph(cg_full, weight_attr="weight")
84 transitions = transitions - transitions.T
85 transitions_conf = transitions.copy()
File ~/miniconda3/envs/omicverse/lib/python3.10/site-packages/omicverse/utils/_paga.py:189, in get_sparse_from_igraph(graph, weight_attr)
187 shape = (shape, shape)
188 if len(edges) > 0:
--> 189 return csr_matrix((weights, zip(*edges)), shape=shape)
190 else:
191 return csr_matrix(shape)
File ~/miniconda3/envs/omicverse/lib/python3.10/site-packages/scipy/sparse/_compressed.py:55, in _cs_matrix.init(self, arg1, shape, dtype, copy)
52 else:
53 if len(arg1) == 2:
54 # (data, ij) format
---> 55 coo = self._coo_container(arg1, shape=shape, dtype=dtype)
56 arrays = coo._coo_to_compressed(self._swap)
57 self.indptr, self.indices, self.data, self._shape = arrays
File ~/miniconda3/envs/omicverse/lib/python3.10/site-packages/scipy/sparse/_coo.py:99, in _coo_base.init(self, arg1, shape, dtype, copy)
96 if dtype is not None:
97 self.data = self.data.astype(dtype, copy=False)
---> 99 self._check()
File ~/miniconda3/envs/omicverse/lib/python3.10/site-packages/scipy/sparse/_coo.py:188, in _coo_base._check(self)
186 """ Checks data structure for consistency """
187 if self.ndim != len(self.coords):
--> 188 raise ValueError('mismatching number of index arrays for shape; '
189 f'got {len(self.coords)}, expected {self.ndim}')
191 # index arrays should have integer data types
192 for i, idx in enumerate(self.coords):
ValueError: mismatching number of index arrays for shape; got 0, expected 2
Can you provide more detailed information about the version? Including scipy version, omicverse version, and data types for adata.X
Thank you for your response. I am using sample data.
AnnData object with n_obs × n_vars = 2930 × 3000
obs: 'clusters', 'age(days)', 'clusters_enlarged', 'dpt_pseudotime'
var: 'n_cells', 'percent_cells', 'robust', 'mean', 'var', 'residual_variances', 'highly_variable_rank', 'highly_variable_features'
uns: 'clusters_colors', 'log1p', 'hvg', 'scaled|original|pca_var_ratios', 'scaled|original|cum_sum_eigenvalues', 'neighbors', 'diffmap_evals', 'draw_graph', 'iroot'
obsm: 'X_umap', 'scaled|original|X_pca', 'X_diffmap', 'X_draw_graph_fa'
varm: 'scaled|original|pca_loadings'
layers: 'ambiguous', 'spliced', 'unspliced', 'counts', 'scaled', 'lognorm'
obsp: 'distances', 'connectivities'
Package Version
absl-py 2.1.0
adjustText 1.1.1
aiohttp 3.9.5
aiosignal 1.2.0
anndata 0.10.7
annotated-types 0.7.0
anyio 4.3.0
array_api_compat 1.7.1
arrow 1.3.0
ase 3.23.0
astor 0.8.1
asttokens 2.4.1
async-timeout 4.0.3
attrs 23.1.0
autograd 1.6.2
autograd-gamma 0.5.0
backoff 2.2.1
beautifulsoup4 4.12.3
blessed 1.20.0
blinker 1.8.2
boltons 24.0.0
boto3 1.34.117
botocore 1.34.117
Brotli 1.0.9
build 1.2.1
CacheControl 0.14.0
cached-property 1.5.2
captum 0.6.0
certifi 2024.2.2
cffi 1.16.0
charset-normalizer 2.0.4
chex 0.1.86
cleo 2.1.0
click 8.1.7
cloudpickle 3.0.0
colorama 0.4.6
colorcet 3.1.0
comm 0.2.2
contextlib2 21.6.0
contourpy 1.2.1
crashtest 0.4.1
croniter 1.4.1
cryptography 42.0.7
cycler 0.12.1
Cython 3.0.10
cytoolz 0.12.3
dask 2024.5.2
datashader 0.16.2
dateutils 0.6.12
debugpy 1.6.7
decorator 5.1.1
deepdiff 7.0.1
dill 0.3.8
distlib 0.3.8
dm-tree 0.1.8
dnspython 2.6.1
docrep 0.3.2
dulwich 0.21.7
einops 0.8.0
email_validator 2.1.1
et-xmlfile 1.1.0
exceptiongroup 1.2.0
executing 2.0.1
fa2 0.3.5
fa2_modified 0.3.10
fastapi 0.111.0
fastapi-cli 0.0.4
fastjsonschema 2.19.1
filelock 3.13.1
Flask 3.0.3
flax 0.6.1
fonttools 4.53.0
formulaic 1.0.1
frozenlist 1.4.0
fsspec 2024.3.1
future 1.0.0
gdown 5.2.0
get-annotations 0.1.2
gmpy2 2.1.2
graphlib-backport 1.0.3
grpcio 1.56.2
h11 0.14.0
h2 4.1.0
h5py 3.11.0
hnswlib 0.7.0
hpack 4.0.0
httpcore 1.0.5
httpx 0.27.0
hyperframe 6.0.1
idna 3.7
igraph 0.11.5
imagecodecs-lite 2019.12.3
imageio 2.34.1
importlib_metadata 7.1.0
inquirer 3.1.4
installer 0.7.0
interface-meta 1.3.0
ipykernel 6.28.0
ipython 8.25.0
ipywidgets 8.1.3
isodate 0.6.1
itsdangerous 2.2.0
jaraco.classes 3.4.0
jax 0.4.28
jaxlib 0.4.28
jaxopt 0.8.3
jedi 0.19.1
jeepney 0.8.0
Jinja2 3.1.4
jmespath 1.0.1
joblib 1.4.0
jupyter_client 8.6.0
jupyter_core 5.5.0
jupyterlab_widgets 3.0.11
keyring 24.3.1
kiwisolver 1.4.5
lazy_loader 0.4
legacy-api-wrap 1.4
leidenalg 0.10.2
lifelines 0.27.8
lightning 2.0.9.post0
lightning_cloud 0.5.69
lightning-utilities 0.11.2
llvmlite 0.42.0
locket 1.0.0
loompy 3.0.7
Markdown 3.6
markdown-it-py 3.0.0
MarkupSafe 2.1.3
matplotlib 3.6.2
matplotlib-inline 0.1.7
mdurl 0.1.2
mellon 1.4.2
mkl-fft 1.3.8
mkl-random 1.2.4
mkl-service 2.4.0
ml-collections 0.1.1
ml-dtypes 0.4.0
more-itertools 10.2.0
mpmath 1.3.0
msgpack 1.0.8
mudata 0.2.3
multidict 6.0.4
multipledispatch 0.6.0
multiprocess 0.70.16
munkres 1.1.4
natsort 8.4.0
nest-asyncio 1.6.0
networkx 3.1
numba 0.59.1
numpy 1.26.4
numpy-groupies 0.11.1
numpyro 0.13.2
omicverse 1.5.9
openpyxl 3.1.2
opt-einsum 3.3.0
optax 0.2.2
ordered-set 4.1.0
orjson 3.10.3
packaging 24.0
pandas 1.5.3
param 2.1.0
parso 0.8.4
partd 1.4.2
patsy 0.5.6
pexpect 4.9.0
pickleshare 0.7.5
Pillow 9.4.0
pip 24.0
pkginfo 1.10.0
platformdirs 4.2.2
ply 3.11
poetry 1.8.3
poetry-core 1.9.0
poetry-plugin-export 1.8.0
progressbar2 4.4.2
prompt-toolkit 3.0.43
protobuf 4.23.3
psutil 5.9.0
ptyprocess 0.7.0
pure-eval 0.2.2
pycparser 2.22
pyct 0.5.0
pydantic 2.1.1
pydantic_core 2.4.0
pygam 0.9.1
Pygments 2.18.0
PyJWT 2.8.0
pynndescent 0.5.12
pyparsing 3.0.9
pyproject_hooks 1.1.0
PyQt5 5.15.10
PyQt5-sip 12.13.0
pyro-api 0.1.2
pyro-ppl 1.9.0+f02dfb9
PySocks 1.7.1
python-dateutil 2.9.0.post0
python-editor 1.0.4
python-multipart 0.0.9
python-utils 3.8.2
pytorch-lightning 2.2.2
pytz 2024.1
pywavelets 1.5.0
PyYAML 6.0.1
pyzmq 25.1.2
rapidfuzz 3.9.3
rdflib 7.0.0
readchar 4.1.0.dev3
requests 2.32.2
requests-toolbelt 1.0.0
rich 13.7.1
s3transfer 0.10.1
scanpy 1.10.1
scikit-image 0.20.0
scikit-learn 1.4.2
scikit-misc 0.1.4
scipy 1.13.0
scvelo 0.3.2
scvi-tools 1.1.2
seaborn 0.13.2
SecretStorage 3.3.3
session-info 1.0.0
setuptools 69.5.1
shellingham 1.5.4
sip 6.7.12
six 1.16.0
sniffio 1.3.1
soupsieve 2.5
sparse 0.15.4
stack-data 0.6.2
starlette 0.37.2
starsessions 1.3.0
statsmodels 0.14.2
stdlib-list 0.10.0
sympy 1.12
tensorboard 2.16.2
tensorboard-data-server 0.7.0
termcolor 2.4.0
texttable 1.7.0
threadpoolctl 3.5.0
tifffile 2020.6.3
tomli 2.0.1
tomlkit 0.12.5
toolz 0.12.1
torch 2.2.2
torch_geometric 2.5.2
torchaudio 2.2.2
torchmetrics 1.4.0.post0
torchvision 0.15.2a0
tornado 6.3.3
tqdm 4.66.4
traitlets 5.14.3
trimesh 4.4.0
trove-classifiers 2024.5.22
typer 0.12.3
typer-slim 0.12.3
types-python-dateutil 2.9.0.20240316
typing_extensions 4.11.0
ujson 5.10.0
umap-learn 0.5.5
unicodedata2 15.1.0
urllib3 2.2.1
uvicorn 0.30.0
virtualenv 20.26.2
wcwidth 0.2.13
websocket-client 1.8.0
websockets 12.0
Werkzeug 3.0.3
wheel 0.43.0
widgetsnbextension 4.0.11
wrapt 1.16.0
xarray 2024.3.0
xlrd 1.2.0
yarl 1.9.3
zipp 3.17.0
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When installing dependencies, omicverse requires ‘scipy>=1.8, <1.12’ in pyproject.toml. How did you manage to install to scipy==1.13, is there any function of the package that scipy must be at 1.13 to run? If so can you conveniently let me know?
In my environment, scipy is version 1.10, I hope this helps. As for what 1.13 changed, I'll go further and test it in future releases
When installing dependencies, omicverse requires ‘scipy>=1.8, <1.12’ in
pyproject.toml. How did you manage to install to scipy==1.13, is there any function of the package that scipy must be at 1.13 to run? If so can you conveniently let me know?
Hello, I followed the tutorial to reinstall:
conda create -n omicverse python=3.10
conda activate omicverse
(CPU only)
conda install pytorch torchvision torchaudio cpuonly -c pytorch
The issue arises at this step: conda install pyg -c pyg
It installs scipy==1.13.
Then I continued to install: conda install omicverse -c conda-forge
The installation of omicverse was successful.
Running import omicverse as ov was successful.
After I installed pip install scipy==1.10, an error occurred.
I proceeded to manually install as per the version requirements.
Then I continued installing pip install -U scvelo within the omicverse environment, which installed scvelo (version 0.3.2). However, upon importing scvelo, I encountered an AttributeError: module 'chex' has no attribute 'warn_deprecated_function'.
I had to repeatedly install various versions of optax, jax, and jaxlib until I eventually succeeded.
My versions are as follows: scipy 1.10.0, jax 0.4.27, jaxlib 0.4.27, and optax 0.1.2.
Following up: ov.utils.cal_paga and ov.single.pyVIA are functioning normally, but there is an issue with v0.plot_trajectory_gams due to incompatibility with numpy-1.23.5 and chex 0.1.86. I am currently trying to resolve this problem, anyway, I greatly appreciate the development of OmicVerse; it has allowed me to successfully obtain usable Trajectory Inference.
@looppy95 , You can use pip uninstall omicverse -y; pip install -U omicverse to install all dependency.
Zehua