Methods for multiple outcome meta-analysis of gene-expression data

Konstantina E Vennou,Daniele Piovani,Panagiota I Kontou,Stefanos Bonovas,Pantelis G Bagos

doi:10.1016/j.mex.2020.100834

Konstantina E Vennou, Daniele Piovani + Show 3 more

Open Access

https://doi.org/10.1016/j.mex.2020.100834

Copy DOI

Journal: MethodsX	Publication Date: Jan 1, 2020
Citations: 4	License type: cc-by

Affiliation: University of Thessaly, Humanitas University

Abstract

Meta-analysis is a valuable tool for the synthesis of evidence across a wide range study types including high-throughput experiments such as genome-wide association studies (GWAS) and gene expression studies. There are situations though, in which we have multiple outcomes or multiple treatments, in which the multivariate meta-analysis framework which performs a joint modeling of the different quantities of interest may offer important advantages, such as increasing statistical power and allowing performing global tests. In this work we adapted the multivariate meta-analysis method and applied it in gene expression data. With this method we can test for pleiotropic effects, that is, for genes that influence both outcomes or discover genes that have a change in expression not detectable in the univariate method. We tested this method on data regarding inflammatory bowel disease (IBD), with its two main forms, Crohn’s disease (CD) and Ulcerative colitis (UC), sharing many clinical manifestations, but differing in the location and extent of inflammation and in complications. The Stata code is given in the Appendix and it is available at: www.compgen.org/tools/multivariate-microarrays.•Multivariate meta-analysis method for gene expression data.•Discover genes with pleiotropic effects.•Differentially Expressed Genes (DEGs) identification in complex traits.

Highlights

After the initial pre-processing steps that may differ among methods, the identification of differentially expressed genes (DEGs1; genes that are differentially expressed, that is, up- or down-regulated, under different conditions) is a procedure usually reduced to a statistical test for the equality of means between groups (e.g. t-test)
One can estimate the covariance matrix for the parameters of interest, which could be of importance when one wants to perform a comparison for the equality of the two estimates and construct a confidence interval for the difference or some other function of the estimates [5]
We implemented the multivariate meta-analysis method for gene expression data and we applied it to data regarding inflammatory bowel disease (IBD6), with its two main forms, Crohn’s disease (CD) and Ulcerative colitis (UC), sharing many clinical manifestations, but differing in the location and extent of inflammation and in complications

Summary

Introduction

In this work we adapted the multivariate meta-analysis method and applied it in gene expression data. Multivariate meta-analysis method for gene expression data. Concerning meta-analysis of microarray and RNAseq studies there are three broad categories of statistical methods that make use of effect sizes, p-values or ranks.

Results

Conclusion