One potential workaround might be to adjust for these factors using a combination of design matrices, perhaps including batch and estrus as covariates in your model. Since DESeq2 doesn’t support random effects directly, tools like limma-voom or edgeR with mixed models might offer better support for that structure. Also, make sure your sample size is adequate to model multiple covariates effectively. By the way, have you explored Snake 8 Ball Pool Safe Mod? It’s a great example of how precise tools can enhance performance just like in RNA-seq workflows.
One potential workaround might be to adjust for these factors using a combination of design matrices, perhaps including batch and estrus as covariates in your model. Since DESeq2 doesn’t support random effects directly, tools like limma-voom or edgeR with mixed models might offer better support for that structure. Also, make sure your sample size is adequate to model multiple covariates effectively. By the way, have you explored Snake 8 Ball Pool Safe Mod? It’s a great example of how precise tools can enhance performance just like in RNA-seq workflows.