I am writing a post because I encountered an interesting question when using ROTS. I want to analyze RNA-seq data gained from samples of two groups: control and treatment (inhibitor of a kinase). I want to perform differential expression analysis and find out what the main role of this kinase is. I know many people would do it based on fold change. However, I noticed that some genes have very small expression counts (for example, 30-50 counts), though they have a large fold change. I am more interested in the differential counts of genes than the fold change because some genes that have large differential counts might have a huge influence on the cell even without a large fold change. Also, in cells, living actually means a lot of chemical reactions going on. Suppose we have a molecule with a very large number in a cell, its number might be prevented from continuing to grow because of the regulatory networks within the cell - or if its number dropped, say four-fold in the cell, the cell would have died, which would make it harder to achieve as large a fold change as a molecule with a very small expression level, but this molecule is important. I know ROTS needs data to be normalized first. However, if there is a fold change, this software has estimated the mean of the control group, the mean of the treatment group, and the differential expression number. With these numbers, I can further calculate the number I want for my analysis. So I am writing to ask about the possibility of reporting these numbers in ROTS. I believe it would help a lot of downstream lab work and benefit future biomedical discoveries.