Error while running run_magic_imputation

[aditisk]

Hello,

I am trying to run Palantir on my dataset to be able to eventually use Cellrank2. I am getting an error message (pasted below). I have loaded the scipy module and imported the necessary features as mentioned in the tutorial. How can I fix this? Thanks.

`---------------------------------------------------------------------------
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 616, in wait_result_broken_or_wakeup
result_item = result_reader.recv()
File "/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/multiprocessing/connection.py", line 251, in recv
return _ForkingPickler.loads(buf.getbuffer())
ModuleNotFoundError: No module named 'scipy.sparse._csr'
"""

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

BrokenProcessPool Traceback (most recent call last)
in
----> 1 imputed_X = palantir.utils.run_magic_imputation(adata)

~/.local/lib/python3.8/site-packages/palantir/utils.py in run_magic_imputation(data, dm_res, n_steps, sim_key, expression_key, imputation_key, n_jobs)
570
571 # Run the dot product in parallel on chunks
--> 572 res = Parallel(n_jobs=n_jobs)(
573 delayed(_dot_helper_func)(T_steps, X[:, chunks[i - 1] : chunks[i]])
574 for i in range(1, len(chunks))

/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/site-packages/joblib/parallel.py in call(self, iterable)
1052
1053 with self._backend.retrieval_context():
-> 1054 self.retrieve()
1055 # Make sure that we get a last message telling us we are done
1056 elapsed_time = time.time() - self._start_time

/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/site-packages/joblib/parallel.py in retrieve(self)
931 try:
932 if getattr(self._backend, 'supports_timeout', False):
--> 933 self._output.extend(job.get(timeout=self.timeout))
934 else:
935 self._output.extend(job.get())

/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/site-packages/joblib/_parallel_backends.py in wrap_future_result(future, timeout)
540 AsyncResults.get from multiprocessing."""
541 try:
--> 542 return future.result(timeout=timeout)
543 except CfTimeoutError as e:
544 raise TimeoutError from e

/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
437 raise CancelledError()
438 elif self._state == FINISHED:
--> 439 return self.__get_result()
440 else:
441 raise TimeoutError()

/ihome/crc/install/python/ondemand-jupyter-python3.8/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
386 def __get_result(self):
387 if self._exception:
--> 388 raise self._exception
389 else:
390 return self._result

BrokenProcessPool: A result has failed to un-serialize. Please ensure that the objects returned by the function are always picklable.`

[ManuSetty]

This appears to be an issue with versioning of different python modules. Can you please try again with a new conda environment? We also suggest using a python version > 3.8

[aditisk]

Thanks for the suggestions. I tried using v3.9 and v3.10 in a virtual environment but now I am unable to run PCA (and all steps after that) on my adata object.

`---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[10], line 2
1 # Note in the manuscript, we did not use highly variable genes but scanpy by default uses only highly variable genes
----> 2 sc.tl.pca(adata)

File ~/.local/lib/python3.10/site-packages/scanpy/preprocessing/_pca.py:200, in pca(data, n_comps, zero_center, svd_solver, random_state, return_info, use_highly_variable, dtype, copy, chunked, chunk_size)
194 if svd_solver not in {'lobpcg', 'arpack'}:
195 raise ValueError(
196 'svd_solver: {svd_solver} can not be used with sparse input.\n'
197 'Use "arpack" (the default) or "lobpcg" instead.'
198 )
--> 200 output = _pca_with_sparse(
201 X, n_comps, solver=svd_solver, random_state=random_state
202 )
203 # this is just a wrapper for the results
204 X_pca = output['X_pca']

File ~/.local/lib/python3.10/site-packages/scanpy/preprocessing/_pca.py:303, in _pca_with_sparse(X, npcs, solver, mu, random_state)
292 return XHmat(x) - mhmat(ones(x))
294 XL = LinearOperator(
295 matvec=matvec,
296 dtype=X.dtype,
(...)
300 rmatmat=rmatmat,
301 )
--> 303 u, s, v = svds(XL, solver=solver, k=npcs, v0=random_init)
304 u, v = svd_flip(u, v)
305 idx = np.argsort(-s)

File ~/.local/lib/python3.10/site-packages/scipy/sparse/linalg/_eigen/_svds.py:443, in svds(A, k, ncv, tol, which, v0, maxiter, return_singular_vectors, solver, random_state, options)
104 def svds(A, k=6, ncv=None, tol=0, which='LM', v0=None,
105 maxiter=None, return_singular_vectors=True,
106 solver='arpack', random_state=None, options=None):
107 """
108 Partial singular value decomposition of a sparse matrix.
109
(...)
441
442 """
--> 443 args = _iv(A, k, ncv, tol, which, v0, maxiter, return_singular_vectors,
444 solver, random_state)
445 (A, k, ncv, tol, which, v0, maxiter,
446 return_singular_vectors, solver, random_state) = args
448 largest = (which == 'LM')

File ~/.local/lib/python3.10/site-packages/scipy/sparse/linalg/_eigen/_svds.py:40, in _iv(A, k, ncv, tol, which, v0, maxiter, return_singular, solver, random_state)
37 if not (np.issubdtype(A.dtype, np.complexfloating)
38 or np.issubdtype(A.dtype, np.floating)):
39 message = "A must be of floating or complex floating data type."
---> 40 raise ValueError(message)
41 if np.prod(A.shape) == 0:
42 message = "A must not be empty."

ValueError: A must be of floating or complex floating data type.`