I would like to use the same method to normalise and analyis the differential expression for not only RNA-seq data but also for other sequencing count data such as 16S and epigenetics data.
But if I look at the high level of my omic data, it seems that there is a global modification between healthy samples and diseases
I have 2 questions.
1) Can we use the normalisation methods that we find in DESEq, edgeR and Limma, ... on a such data (globla modification between 2 groups)? because I understood that these methods are bases on the hypothesis that most genetic elements are not DE, is it right?. it seems that it is not the case in this type of omic data. If it is not possible to use them, do you have an idea to normalise data ?
2) for Epigenetics data, the regions that I used for read count are not independant between each other and not related to known genetic elements. Currently, each chromosome is splitted into 500nt bins with a overlapping of 250nt. I understood that the counting rules to use DESeq/edgeR/Limma... is that only reads mapped on unique place are kept. In this case, it doesn't work because each read is count at least in 2 different bins. Can I use DESeq/edgeR/Limma?