Reviewing and assessing existing meta-analysis models and tools

Funmilayo Makinde, Milaine Tchamga, James Jafali, Segun Fatumo, Emile Rugamika Chimusa, Nicola Mulder, Gaston K. Mazandu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.
Original languageEnglish
Article numberbbab324
Number of pages12
JournalBriefings in Bioinformatics
Volume22
Issue number6
Early online date20 Aug 2021
DOIs
Publication statusPublished - 5 Nov 2021
Externally publishedYes

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