**3.6. Differential expression analysis**

There are several tools and methods developed for the differential expression analysis comparing differences in gene expression in different conditions (see section 2). Nonparamet‐ ric methods are not capable of better differential expression detection in the absence of sample replicates and hence parametric methods are preferred for differential expression analysis [129]. A study comparing various differential expression methods suggests that there is no optimized method that can serve well for all the different conditions. As compared to other tools, Cuffdiff performed poorly with large number of false-positives [130]. The accuracy of differentially expressed genes is statistically significant and makes more sense if multiple replicates are used in the analysis.

Similar to the situation as in normalization, picking up the best tool for differential analysis is a tricky job. This is because there is no consensus about the tool best-suited for all experimental setups. Soneson and Dolerenzi [106] found limma performing well under many conditions but it required at least three replicates. Furthermore, they found limma performing worse when dispersion differed between two conditions. They also observed that with large sample sizes DESeq was overly conservative, while edgeR was producing large number of false-positives.
