Author’s response to reviews Title: A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis Authors:

Reviewer #1: In a typical transcriptomic dataset, the sample size is small. This makes detection of Differentially Expressed (DE) genes very difficult. One way of addressing this problem is to integrate multiple datasets of the same disease. This does have some improvement, but in many cases, the power is still limited. For two similar diseases, there could be overlap between the sets of DE genes. Under this assumption, the authors proposed a statistically principled framework to boost the detection power, where multiple datasets from similar diseases are analyzed jointly, and disease-shared and -specific DE genes are identified. By extensive simulations and two real data applications, the authors demonstrated the superiority of the new method over a single dataset method and a popular joint analysis method.