Error in "xb._combined"

[z5ouyang]

Dear authors,
Thanks for the recent updates. But I still encounter errors in "1_1_Statistics_all_samples_using_txsim":

1. AttributeError: module 'xb' has no attribute '_combined';
   I then followed "import xb.formatting as xf" from "0_0", to modified as "import xb._combined as x_c"
2. And then error:

ValueError                                Traceback (most recent call last)
Cell In[3], line 18
     16 except:
     17     print('not possible to convert to dense')
---> 18 qlm1=x_c.all_quality_metrics(adata)
     19 qlm1.columns=[fl[:-5]]
     20 qlm1.loc['area',fl[:-5]]=float(xf.cell_area(adata))

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/xb/_combined.py:19, in all_quality_metrics(adata_sp)
     17 metrics['number_of_genes']=number_of_genes(adata_sp)
     18 metrics['number_of_cells']=number_of_cells(adata_sp)
---> 19 metrics['pct5_readsxcell']=percentile_5th_reads_cells(adata_sp)
     20 metrics['mean_genesxcell']=mean_genes_cells(adata_sp)
     21 metrics['pct95_genesxcell']=percentile_95th_genes_cells(adata_sp)

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/xb/_quality_metrics.py:119, in percentile_5th_reads_cells(adata_sp, pipeline_output)
    106 def percentile_5th_reads_cells(adata_sp: AnnData,pipeline_output=True):
    107     """5th percentile of number of reads/cells in the spatial experiment
    108     Parameters
    109     ----------
   (...)
    117        Median_number_of_reads_x_cell
    118     """   
--> 119     pctile5=np.percentile(np.sum(adata_sp.layers['raw'],axis=1),5)
    120     return pctile5

File <__array_function__ internals>:180, in percentile(*args, **kwargs)

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/lib/function_base.py:4134, in percentile(a, q, axis, out, overwrite_input, method, keepdims, interpolation)
   4132 if not _quantile_is_valid(q):
   4133     raise ValueError("Percentiles must be in the range [0, 100]")
-> 4134 return _quantile_unchecked(
   4135     a, q, axis, out, overwrite_input, method, keepdims)

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/lib/function_base.py:4383, in _quantile_unchecked(a, q, axis, out, overwrite_input, method, keepdims)
   4375 def _quantile_unchecked(a,
   4376                         q,
   4377                         axis=None,
   (...)
   4380                         method="linear",
   4381                         keepdims=False):
   4382     """Assumes that q is in [0, 1], and is an ndarray"""
-> 4383     r, k = _ureduce(a,
   4384                     func=_quantile_ureduce_func,
   4385                     q=q,
   4386                     axis=axis,
   4387                     out=out,
   4388                     overwrite_input=overwrite_input,
   4389                     method=method)
   4390     if keepdims:
   4391         return r.reshape(q.shape + k)

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/lib/function_base.py:3702, in _ureduce(a, func, **kwargs)
   3699 else:
   3700     keepdim = (1,) * a.ndim
-> 3702 r = func(a, **kwargs)
   3703 return r, keepdim

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/lib/function_base.py:4552, in _quantile_ureduce_func(a, q, axis, out, overwrite_input, method)
   4550     else:
   4551         arr = a.copy()
-> 4552 result = _quantile(arr,
   4553                    quantiles=q,
   4554                    axis=axis,
   4555                    method=method,
   4556                    out=out)
   4557 return result

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/lib/function_base.py:4669, in _quantile(arr, quantiles, axis, method, out)
   4667     result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1)
   4668     gamma = gamma.reshape(result_shape)
-> 4669     result = _lerp(previous,
   4670                    next,
   4671                    gamma,
   4672                    out=out)
   4673 if np.any(slices_having_nans):
   4674     if result.ndim == 0 and out is None:
   4675         # can't write to a scalar

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/lib/function_base.py:4488, in _lerp(a, b, t, out)
   4486 diff_b_a = subtract(b, a)
   4487 # asanyarray is a stop-gap until gh-13105
-> 4488 lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out))
   4489 subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5)
   4490 if lerp_interpolation.ndim == 0 and out is None:

File ~/.conda/envs/Xenium/lib/python3.9/site-packages/numpy/matrixlib/defmatrix.py:218, in matrix.__mul__(self, other)
    215 def __mul__(self, other):
    216     if isinstance(other, (N.ndarray, list, tuple)) :
    217         # This promotes 1-D vectors to row vectors
--> 218         return N.dot(self, asmatrix(other))
    219     if isscalar(other) or not hasattr(other, '__rmul__') :
    220         return N.dot(self, other)

File <__array_function__ internals>:180, in dot(*args, **kwargs)

ValueError: shapes (1,162033) and (1,1) not aligned: 162033 (dim 1) != 1 (dim 0)

After debug, the error caused by np.percentile(np.sum(adata_sp.layers['raw'],axis=1),5) where np.sum(...,axis=1) return a matrix which is NOT what np.percentile expect. seems like this resolve the error:
np.percentile(np.squeeze(np.asarray(np.sum(adata.layers['raw'],axis=1))),5)

[sergiomarco25]

We have now corrected the calculation of the percentiles. We have double check that the modules are correctly loaded when running the notebook and modified the __init__py file accordingly. Hopefully your concerns should be addressed now.