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

ABSTRACT:

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 Em . We prove that Em is non-negative and Emτ^m2 increases with the increase of τ^m2 , satisfying the general conditions of the between-study variance es

SUBMITTER: Wang N 

PROVIDER: S-EPMC11884006 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature