A0767
Title: Contextual evaluation of data harmonization for microRNA sequencing
Authors: Li-Xuan Qin - Memorial Sloan Kettering Cancer Center (United States) [presenting]
Abstract: The reproducibility of microRNA sequencing 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 enable contextual evaluation for data harmonization, robust benchmark datasets are developed, thorough evaluation pipelines, and accompanying software tools, with a particular focus on microRNAs. Findings are presented 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.
