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This dataset heterogeneity made it difficult, if not impossible, to objectively compare methods, and impeded the progress of the field.
As expected, we clearly demonstrate that mechanism-level overlap trumps gene-level overlap despite dataset heterogeneity, and this for both types of mechanisms: curated and curation-free ones.
In other words, despite dataset heterogeneity, FAIME-derived pathway mechanisms increased three- to four-fold the overlap in all the pairwise dataset comparisons.
This dataset heterogeneity has also been reported as a fundamental challenge in reproducibility of gene signatures across solid tumor datasets and the FAIME methodology was nonetheless sensitive enough to significantly detect them.
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In the absence of any between-dataset heterogeneity, fixed and random effects estimates coincide.
Meta-analysis showed large between-dataset heterogeneity that was driven by different effects in prostate cancer and other cancers.
Meta-analysis may be used to improve the power and examine the between-dataset heterogeneity of genome-wide association studies.
Between-dataset heterogeneity was quantified using the I2 metric for inconsistency [15] and its statistical significance was tested with the chi-square distributed Q statistic [16].
In our empirical evaluation, the SNPs that were proposed by each strategy typically had no measurable or minimal between-dataset heterogeneity.
Three SNPs (rs1000291 on chromosome 3, rs2241743 on chromosome 4 and rs3018626 on chromosome 11) were selected by all three strategies; the first two had absolutely no between-dataset heterogeneity (I2 = 0) and the third had minimal between-dataset heterogeneity (I2 = 15%).
However, some may warrant further consideration: 6 SNPs were identified with at least 2 of the 3 strategies and 3 SNPs [rs1000291 on chromosome 3, rs2241743 on chromosome 4 and rs3018626 on chromosome 11] were identified with all 3 strategies and had no or minimal between-dataset heterogeneity (I2 = 0, 0 and 15%, respectively).
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