Any good experimental setup must include replicate treated or affected experimental samples and replicate untreated or unaffected control samples for comparison.
A critical factor is setting up any differential gene expression analysis is to define the groups for comparison. Any good experimental setup includes not only a set of treated or affected experimental samples or test samples, but also a set of untreated or unaffected control samples to which all experimental samples or test samples are compared.
Datasets obtained from samples using the same biological condition are therefore grouped and their normalized data across each individual gene averaged before calculating fold-change values during the secondary differential gene expression analysis. Experimental and/or biological replicates for every condition are also crucial for determining the statistical significance of those differential gene expression analysis results by calculating p-values.
Once these group definitions have been made in the RNA-seq Analysis Portal, the application takes care of the rest of the differential gene expression analysis for you.