A novel estimator of between-study variance in random-effects models - Nan Wang; Jun Zhang; Li Xu; Jing Qi; Beibei Liu; Yiyang Tang; Yinan Jiang; Liang Cheng; Qinghua Jiang; Xunbo Yin; Shuilin Jin (BMC Genomics, 2020)
Abstract Background With the rapid development of high-throughput sequencing technologies, many datasets on the same biological subject are generated. A meta-analysis is an approach that combines results from different studies on the same topic. The random-effects model in a meta-analysis enables the modeling of differences between studies by incorporating the between-study variance. Results This paper proposes a moments estimator of the between-study variance that represents the across-study variation. A new random-effects method (DSLD2), which involves two-step estimation starting with the DSL estimate and the Dg2 $D_{g}^{2}$ in the second step, is presented. The DSLD2 method is compared with 6 other meta-analysis methods based on effect sizes across 8 aspects under three hypothesis settings. The results show that DSLD2 is a suitable method for identifying differentially expressed genes under the first hypothesis. The DSLD2 method is also applied to Alzheimer’s microarray datasets. The differentially expressed genes detected by the DSLD2 method are significantly enriched in neurological diseases. Conclusions The results from both simulationes and an application show that DSLD2 is a suitable method for detecting differentially expressed genes under the first hypothesis.