Abstract
Microbiome data, like other high-throughput omics data, are susceptible to technical artifacts, including batch effects, measurement biases, and latent sources of heterogeneity. These challenges present major barriers to large-scale, multi-site, and integrative microbiome studies, where existing methods often rely on restrictive assumptions and may yield unreliable inference under realistic community-level variation. In this presentation, I will highlight recent methodological advances from our group to address these challenges, including ConQuR, a method for correcting known batch effects; QuanT, a framework for detecting latent or unknown sources of heterogeneity; and CAFT, a statistically principled approach for mitigating bias in differential abundance analysis. These methods are built on flexible nonparametric statistical models that accommodate the irregular, heavy-tailed, and zero-inflated characteristics of microbiome data, enabling more robust and reliable inference across diverse study settings.
Speaker Bio
Dr. Ni Zhao received her PhD from the University of North Carolina at Chapel Hill and is currently an Associate Professor and PhD Program Director in the Department of Biostatistics at Johns Hopkins University. Her primary research interests lie in statistical genetics and genomics, with a particular focus on developing statistical methods for microbiome studies, including both bulk and spatial microbiome profiling. In recent years, her lab has made significant contributions to understanding and addressing batch effects, biases, and other sources of technical variation in microbiome studies. She has also been actively involved in large-scale epidemiologic studies, where microbiome and multi-omics data integration are central components. Over the past decade, Dr. Zhao has published more than 40 peer-reviewed papers in leading journals across statistics, epidemiology, and biomedical and clinical research.