johannesostner
commented
5 months ago Hi @schroeme!
This looks similar to #224. Rephrasing the discussion from there - the sample_identifier column(s) define a partition of the cells into (statistical) samples - in each sample, all cells will be aggregated by their cell type. Like in a regression setting, each covariate defined in covariate_obs can only have one value. If there are cells with different values in the same sample, this covariate will be skipped and the warning above is displayed.
My guess would be that one of your 10x batches has some cells labeled with male and some labeled with female in it. If you want to split those cells into different samples, you can move sex to the sample_identifiers.
schroeme
Report
Good morning,
I'm running the pertpy implementation of scCODA. I'm getting the following error
Covariate sex has non-unique values! Skipping...when I run
sccoda_model = pt.tl.Sccoda() sccoda_data = sccoda_model.load( adata_small, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_type", sample_identifier="10x_batch", covariate_obs=["age","region","sex","replicate"], )where
sexhas two values,maleandfemale. I did not get this error whensexhad 3 values,male,female, andmixed. Observation names are unique and the data type forsexis categorical. What might be causing this issue? Can a covariate not have only two possible values? Please let me know if you need more information.Version information
altair 4.2.2 anndata 0.8.0 matplotlib 3.7.1 mudata 0.2.1 numpy 1.23.5 pandas 1.5.3 pertpy 0.6.0 scanpy 1.9.3 seaborn 0.11.2 session_info 1.0.0 tensorflow 2.12.0
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IPython 8.11.0 jupyter_client 8.1.0 jupyter_core 5.3.0 notebook 6.5.3
Python 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] Linux-5.11.0-25-generic-x86_64-with-glibc2.31
Session information updated at 2024-02-03 06:46