Comments (1)
Hi @Lopiniatre!
We are aware of all these warnings, and they should not affect the results. A quick breakdown:
- The first two tensorflow warnings refer to GPU optimization which is not needed for scCODA.
- The warning
WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass.
has appeared in later tensorflow versions, but we checked that it does not affect results. - The warnings after finishing the MCMC sampling are also known and should be no concern. We also cleaned up the code that caused these warnings to appear in the latest version of scCODA (0.1.7).
from sccoda.
Related Issues (20)
- Error when num_burnins > num_results HOT 4
- Exporting Summary Results HOT 1
- additive effects and interaction terms HOT 3
- model comparison using LOO/WAIC HOT 2
- Tutorial for R/reticulate HOT 3
- Final Parameter and log2-fold change have different signs HOT 6
- Additive model with batch HOT 2
- Different results for same comparison, based on which group is selected as reference HOT 2
- mixed effect model HOT 1
- Use the model with multiple covariants including numeric ones? HOT 1
- Covariate has non-unique values HOT 2
- Usage without replicates ? HOT 2
- Influence of number of cells in parameterization HOT 6
- module 'pertpy.plot' has no attribute 'coda' HOT 1
- interpreting the output with the issue of compositionality. HOT 6
- How to compare multiple groups with sccoda HOT 7
- Bug in viz.boxplots function (and possibly others) HOT 3
- Using continuous variables yield weird results. HOT 3
- Multiple comparisons HOT 2
- How to plot the final result? HOT 1
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from sccoda.