Background With the rapid development of high-throughput sequencing technology, high-throughput sequencing data has grown on a massive scale, leading to the emergence of multiple public databases, such as EBI and GEO. Conducting secondary mining of high-throughput sequencing data in these databases can yield more valuable insights. Meta-analysis can quantitatively combine high-throughput sequencing data from the the same topic. It increases the sample size for data analysis, enhances statistical power, and results in more consistent and reliable conclusions. Results This study proposes a new between-study variance estimator <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11316_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="26" /> </InlineMediaObject> <EquationSource Format="TEX">$E_{m}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mi>m</mi> </msub> </math></EquationSource> </InlineEquation>. We prove that <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11316_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="26" /> </InlineMediaObject> <EquationSource Format="TEX">$E_{m}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mi>m</mi> </msub> </math></EquationSource> </InlineEquation> is non-negative and <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11316_Article_IEq3.gif" Format="GIF" Height="23" Rendition="HTML" Resolution="72" Type="Linedraw" Width="62" /> </InlineMediaObject> <EquationSource Format="TEX">$E_{m} \left( {\hat{\tau }_{m}^{2} } \right)$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi>E</mi> <mi>m</mi> </msub> <mfenced close=")" open="("> <msubsup> <mover accent="true"> <mi>τ</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>m</mi> </mrow> <mn>2</mn> </msubsup> </mfenced> </mrow> </math></EquationSource> </InlineEquation> increases with the increase of <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11316_Article_IEq4.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">$\hat{\tau }_{m}^{2}$</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>τ</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>m</mi> </mrow> <mn>2</mn> </msubsup> </math></EquationSource> </InlineEquation>, satisfying the general conditions of the between-study variance estimator. We get the DSLE2 (two-step estimation starting with the DSL estimate and the <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12864_2025_11316_Article_IEq5.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="26" /> </InlineMediaObject> <EquationSource Format="TEX">$E_{m}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mi>m</mi> </msub> </math></EquationSource> </InlineEquation> in the second step) random-effects meta-analysis model based on the between-study variance estimator Em. The accuracy and a series of evaluation metrics of the DSLE2 model are better than those of the other 6 meta-analysis models. DSLE2 model is applied to lung cancer and Parkinson’s methylation data. Significantly differentially methylated sites identified by DSLE2 model and the genes with significantly differentially methylated sites are closely related to two diseases, indicating the effectiveness of DSLE2 random-effects model. Conclusions This paper propose the DSLE2 random-effects meta-analysis model based on new between-study variance estimator Em. The DSLE2 model performs well for methylation data.

DSLE2 random-effects meta-analysis model for high-throughput methylation data

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2025年3月5日

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Nan Wang,
Yang Zhou,
Fengping Zhu,
Shuilin Jin

摘要

Background

With the rapid development of high-throughput sequencing technology, high-throughput sequencing data has grown on a massive scale, leading to the emergence of multiple public databases, such as EBI and GEO. Conducting secondary mining of high-throughput sequencing data in these databases can yield more valuable insights. Meta-analysis can quantitatively combine high-throughput sequencing data from the the same topic. It increases the sample size for data analysis, enhances statistical power, and results in more consistent and reliable conclusions.

Results

This study proposes a new between-study variance estimator $E_{m}$ $E_{m}$ . We prove that $E_{m}$ $E_{m}$ is non-negative and $E_{m} \left( {\hat{\tau }_{m}^{2} } \right)$ $E_{m} ({\hat{τ}}_{m}^{2})$ increases with the increase of $\hat{\tau }_{m}^{2}$ ${\hat{τ}}_{m}^{2}$ , satisfying the general conditions of the between-study variance estimator. We get the DSLE2 (two-step estimation starting with the DSL estimate and the $E_{m}$ $E_{m}$ in the second step) random-effects meta-analysis model based on the between-study variance estimator Em. The accuracy and a series of evaluation metrics of the DSLE2 model are better than those of the other 6 meta-analysis models. DSLE2 model is applied to lung cancer and Parkinson’s methylation data. Significantly differentially methylated sites identified by DSLE2 model and the genes with significantly differentially methylated sites are closely related to two diseases, indicating the effectiveness of DSLE2 random-effects model.

Conclusions

This paper propose the DSLE2 random-effects meta-analysis model based on new between-study variance estimator Em. The DSLE2 model performs well for methylation data.