Evidence-based Practice for Epi-Transcriptomic Data Harmonization

Music City Center 

The reproducibility of epi-transcriptomic data analysis hinges on effectively mitigating data artifacts that arise from variable experimental handling through data harmonization. While numerous harmonization methods – encompassing normalization and batch-effect correction – have been developed to address these artifacts, statistical investigations into their impact on downstream analyses primarily focused on differential expression analysis. To promote evidence-based practices in data harmonization, my team has developed robust benchmark datasets, novel statistical methods, and accompanying software tools, with a particular focus on microRNAs. In this talk, I will present findings from a simulation study evaluating the performance of various data harmonization approaches in the contexts of sample clustering and sample classification, each assessed using multiple analytical methods. The best-performing combinations of harmonization and downstream analysis methods were then applied to reanalyze publicly available real-world data.