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Impacts of data heterogeneity in meta-analyses. A key strength of meta-analyses is the ability to take data from multiple studies to increase sample size. A Methodological heterogeneity risks introducing confounding batch effects in pooled datasets . In this figure, data from three studies have been pooled and analysed using a uniform pipeline that revealed two distinct clusters (group A and group B). However, after accounting for methodological heterogeneity, it becomes evident that all of the data in group A were generated using a single method that was distinct from those that generated the data in group B. This type of batch effect risks incorrect clinical interpretation of the analysis outputs.

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