Impact of influential data on screening epigenome-wide data
Abstract
Background: ttScreening (TT) is an effective high-dimensional screening algorithm to identify important cytosine-phosphate-guanine dinucleotide (CpG) sites associated with DNA methylation. Via simulations, we aimed to examine the impact of influential outliers on TT’s performance. Methods: We simulated K = 2,000 and 10,000 CpG sites across n = 100 and 200 subjects, linearly associated with a continuous outcome,, and other latent variables with the error term following a normal or Cauchy distribution. Among the K CpGs, 10 were associated with (informative CpGs) while the remaining sites were not associated with (non-informative CpGs). We artificially created 1 to 5 influential points in one informative and one non-informative CpG site and compared TT’s accuracy to Bonferroni and false discovery rate (FDR)-based approaches. Results: TT performed as well as or better than the FDR and Bonferroni-based approaches, across all degrees of influentiality. When focusing on non-informative CpG detection, regardless of sample size, all approaches had high accuracy (above 85%, overall) at their optimal cutoff for a single influential point. Among the CpG sites with a higher number of influential points (five points) and a normal error term, TT required a minimum cutoff of 70% for accuracy compared to FDR and Bonferroni, both of which had an accuracy of 0% for n = 100 and 200. However, increasing the TT cutoff to 80% increased accuracy to 20% and 24%, respectively, and further increased to 97% and 99% for a 90% cutoff, respectively (among K = 2000). We observed the same patterns for 10,000 CpGs and informative CpG detection. When Cauchy error terms were applied, the same patterns held, but with a higher magnitude of accuracy for all approaches, and thus TT required lower cutoffs to achieve 100% accuracy. In summary, in the presence of influential data, we recommend a more conservative cutoff of 70–90% compared to the default cutoff of 50–70% suggested by Ray et al. (in Biomed Res Int 2016(1):2615348, 2016). Conclusion: TT, Bonferroni, and FDR are capable approaches for type 1 protection when screening high-dimensional data. However, in the presence of influential data, TT is likely to be the most robust approach.
Publication Title
BMC Bioinformatics
Recommended Citation
Sultana, S., Zhang, H., Jiang, Y., Abrar, M., Arshad, H., Xie, L., & Ray, M. (2026). Impact of influential data on screening epigenome-wide data. BMC Bioinformatics, 27 (1) https://doi.org/10.1186/s12859-026-06369-4
