RRuntimeError: Error in colSums(x) : 'x' must be numeric

[ahmed-agami]

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Hi Lukas,

I have followed the steps exactly here: https://pertpy.readthedocs.io/en/latest/tutorials/notebooks/milo.html under the section ''Milo - KNN based differential abundance analysis'' using my AnnData object and I had the above error when I tried to run the Differential abundance testing with GLM.

---

RRuntimeError Traceback (most recent call last)
Cell In[114], line 1
----> 1 milo.da_nhoods(mdata, design="~condition", model_contrasts="conditionGAPDH-conditioncontrol")

File ~/yes/envs/pertpy/lib/python3.10/site-packages/pertpy/tools/_milo.py:371, in Milo.da_nhoods(self, mdata, design, model_contrasts, subset_samples, add_intercept, feature_key, solver)
369 # Fit NB-GLM
370 dge = edgeR.DGEList(counts=count_mat[keep_nhoods, :][:, keep_smp], lib_size=lib_size[keep_smp])
--> 371 dge = edgeR.calcNormFactors(dge, method="TMM")
372 dge = edgeR.estimateDisp(dge, model)
373 fit = edgeR.glmQLFit(dge, model, robust=True)

File ~/yes/envs/pertpy/lib/python3.10/site-packages/rpy2/robjects/functions.py:208, in SignatureTranslatedFunction.call(self, *args, **kwargs)
206 v = kwargs.pop(k)
207 kwargs[r_k] = v
--> 208 return (super(SignatureTranslatedFunction, self)
209 .call(*args, **kwargs))

File ~/yes/envs/pertpy/lib/python3.10/site-packages/rpy2/robjects/functions.py:131, in Function.call(self, *args, **kwargs)
129 else:
130 new_kwargs[k] = cv.py2rpy(v)
--> 131 res = super(Function, self).call(*new_args, **new_kwargs)
132 res = cv.rpy2py(res)
133 return res

File ~/yes/envs/pertpy/lib/python3.10/site-packages/rpy2/rinterface_lib/conversion.py:45, in cdata_res_to_rinterface..(*args, **kwargs)
44 def _(*args, **kwargs):
---> 45 cdata = function(*args, **kwargs)
46 # TODO: test cdata is of the expected CType
47 return _cdata_to_rinterface(cdata)

File ~/yes/envs/pertpy/lib/python3.10/site-packages/rpy2/rinterface.py:890, in SexpClosure.call(self, *args, **kwargs)
883 res = rmemory.protect(
884 openrlib.rlib.R_tryEval(
885 call_r,
886 call_context.sexp._cdata,
887 error_occured)
888 )
889 if error_occured[0]:
--> 890 raise embedded.RRuntimeError(_rinterface._geterrmessage())
891 return res

RRuntimeError: Error in colSums(x) : 'x' must be numeric

Version information

---

anndata 0.10.8
anndata2ri 1.3.2
matplotlib 3.9.2
mudata 0.3.1
numba 0.60.0
numpy 1.26.4
pandas 2.2.3
pertpy 0.9.4
rpy2 3.5.17
scanpy 1.10.4
scipy 1.14.1
scvi 1.2.0
seaborn 0.13.2
session_info 1.0.0

PIL 11.0.0
absl NA
adjustText 1.3.0
anyio NA
arrow 1.3.0
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attr 24.2.0
attrs 24.2.0
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certifi 2024.08.30
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cycler 0.12.1
cython_runtime NA
dateutil 2.9.0.post0
debugpy 1.8.9
decorator 5.1.1
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fsspec 2024.10.0
h5py 3.12.1
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IPython 8.30.0
jupyter_client 8.6.3
jupyter_core 5.7.2
jupyterlab 4.2.6
notebook 7.2.2

Python 3.10.15 | packaged by conda-forge | (main, Oct 16 2024, 01:24:24) [GCC 13.3.0]
Linux-5.15.0-124-generic-x86_64-with-glibc2.31

Session information updated at 2024-11-29 19:32

[emdann]

Hi @ahmed-agami, replying here for emdann/milopy#52 (although the examples are different)

Can you check that you have no NA or inf in mdata['milo'].X? Or in mdata['rna'].identifier? What are the levels of condition? Do you get the same error if you use only da_nhoods(mdata, design="~condition") without specifying contrasts?

[ramadatta]

Hi @emdann ,

Many thanks for your reply. We have been stuck on this for a while.

replying on behalf of @ahmed-agami , as I am analysing the same data. I have simplified the dataset by subsetting it.

Please see below:

Additionally I checked if mdata['rna'].X has any issue, seems everything is okay with the input:

The error stems with or without specifying contrasts.

Best Regards
Datta

[emdann]

I am not sure whether this is what's causing the issue here, but it seems like you are trying to test for differential abundance on a condition for which you don't have replicates. There are only 3 observations in mdata['milo']. The model needs to estimate variance in cell abundance for multiple samples from each condition. In the first example posted you had 9 observations in mdata['milo']. How are you defining sample_col in milo.count_nhoods? And how does sample_col relate to condition? It would be helpful for debugging if you kept using and reporting the same example code.