Robust meta-analysis for large-scale genomic experiments based on an empirical approach.

ABSTRACT:

Background

Recent high-throughput technologies have opened avenues for simultaneous analyses of thousands of genes. With the availability of a multitude of public databases, one can easily access multiple genomic study results where each study comprises of significance testing results of thousands of genes. Researchers currently tend to combine this genomic information from these multiple studies in the form of a meta-analysis. As the number of genes involved is very large, the classical meta-analysis approaches need to be updated to acknowledge this large-scale aspect of the data.

Methods

In this article, we discuss how application of standard theoretical null distributional assumptions of the classical meta-analysis methods, such as Fisher's p-value combination and Stouffe

SUBMITTER: Sikdar S 

PROVIDER: S-EPMC8832678 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature