A1430
Title: MiLC to account for unobserved confounding and reduce false discoveries in microbiome research
Authors: Siyuan Ma - Vanderbilt University Medical Center (United States) [presenting]
Abstract: Recent research has highlighted false discoveries in microbiome studies, particularly in differential abundance (DA) analyses. While data compositionality has received attention, it is demonstrated that unobserved confounding (e.g., population heterogeneity, recent antibiotic use, or seasonal dietary changes) can be an even stronger driver of false discoveries. Using real-data evidence, it is shown that unobserved confounding inflates false discoveries in microbiome DA more than data compositionality. To address this, a novel factor-modeling regression method is introduced, Microbiome Latent Confounder DA (MiLC), to estimate unobserved confounding factors and control false discoveries. MiLC can be applied to both relative abundance and read count microbiome data. Its performance is validated in controlling false discoveries, relative to existing methods, using extensive simulation- and real-data-based benchmarking. Results highlight the critical need to correct for hidden confounders, offering a more reliable framework for microbiome DA analyses and ultimately improving the robustness of microbiome research findings.
