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, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(x_1\)</EquationSource> </InlineEquation>, and other latent variables with the error term following a normal or Cauchy distribution. Among the K CpGs, 10 were associated with <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(x_1\)</EquationSource> </InlineEquation> (informative CpGs) while the remaining sites were not associated with <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(x_1\)</EquationSource> </InlineEquation> (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 <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(>0\%\)</EquationSource> </InlineEquation> 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.

Impact of influential data on screening epigenome-wide data

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2026年3月6日

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Samia Sultana,
Hongmei Zhang,
Yu Jiang,
Mohammad Nahian Ferdous Abrar,
Hasan Arshad,
Lu Xie,
Meredith Ray

摘要

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, \(x_1\) , and other latent variables with the error term following a normal or Cauchy distribution. Among the K CpGs, 10 were associated with \(x_1\) (informative CpGs) while the remaining sites were not associated with \(x_1\) (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 \(>0\%\) 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.