I analyze transcriptomic data that most of the time come from bacteria or cell lineages. In that case, biological variance between independent cultures is quite low, because all the cultures originate from a single clone. In contrast, the different treatments applied to these cells produce important changes in gene expression. This low within-group variability associated with a high between-condition difference situation results in very large lists of genes that show a significant expression difference between the treatments. But it is doubtful that all these genes be relevant with regard to the biological question associated with the experiment. As DESeq2, edgeR, shrinkSeq, ... have been developed to handle true biological variance, I assume that this biological variance is very much under-estimated in the case of bacteria. Applying more stringent significance thresholds is probably not the appropriate solution to this problem.
Anybody has an idea on how to proceed with bacterial data ? Is there a need for a specific dispersion estimate ?
Thank you very much for your help and advice.
Anybody has an idea on how to proceed with bacterial data ? Is there a need for a specific dispersion estimate ?
Thank you very much for your help and advice.
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