bc.st.clr_normalize() fails

[bhoellbacher]

Running standard_workflow notebooks fails when running with CITEseq data

ValueError                                Traceback (most recent call last)
Cell In[38], line 30
     28 #perform normalization (this normalization is specific to CITEseq data)
     29 if adt_norm == 'CLR':
---> 30     bc.st.clr_normalize(adata_prot, os.path.join(results_folder_citeseq, 'citeseq'))
     32 #perform batch correction if desired otherwise just perform clustering
     33 if (batch_to_correct != 'None'):
     34     #save a copy of uncorrected in case we need it for something later

File .../lib/python3.9/site-packages/besca/st/_wrapper_funcs.py:290, in clr_normalize(adata, results_folder)
    285 start = time()
    287 # normalize per cell
    288 # this also already applies log! Is not taken seperately
    289 # already normalize BEFORE saving "raw" - as recommended in the scanpy tutorial
--> 290 normalize_geometric(adata)
    291 print("clr normalization applied to adata")
    293 # keep raw copy

File .../lib/python3.9/site-packages/besca/pp/_normalization.py:188, in normalize_geometric(adata)
    185 X = np.array(X)
    187 # replacement of zero values with very small numbers without changing the overall sums
--> 188 X = multiplicative_replacement(X)
    190 # centre log ratio transformation
    191 X = clr(X)

File .../lib/python3.9/site-packages/besca/pp/_normalization.py:132, in multiplicative_replacement(mat, delta)
     87 def multiplicative_replacement(mat, delta=None):
     88     r"""Replace all zeros with small non-zero values
     89     It uses the multiplicative replacement strategy [1]_ ,
     90     replacing zeros with a small positive :math:`\delta`
   (...)
    129            [0.0625, 0.4375, 0.4375, 0.0625]])
    130     """
--> 132     mat = closure(mat)
    133     z_mat = mat == 0
    135     num_feats = mat.shape[-1]

File .../lib/python3.9/site-packages/besca/pp/_normalization.py:37, in closure(mat)
      6 """
      7 Performs closure to ensure that all elements add up to 1.
      8 
   (...)
     34        [0.4, 0.4, 0.2]])
     35 """
     36 mat = np.atleast_2d(mat)
---> 37 if np.any(mat < 0):
     38     raise ValueError("Cannot have negative proportions")
     39 if mat.ndim > 2:

File .../lib/python3.9/site-packages/scipy/sparse/_base.py:396, in _spbase.__bool__(self)
    394     return self.nnz != 0
    395 else:
--> 396     raise ValueError("The truth value of an array with more than one "
    397                      "element is ambiguous. Use a.any() or a.all().")

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().