Accounting for Unobserved Confounding to Reduce False Discoveries in Microbiome Research

Music City Center 

Recent research has highlighted false discoveries in microbiome studies, particularly in differential abundance (DA) analyses. While data compositionality has received attention, we demonstrate 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, we show that unobserved confounding inflates false discoveries in microbiome DA, more than data compositionality. To address this, we introduce a novel factor-modeling regression method, 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. We validate its performance in controlling false discoveries, relative to existing methods, using extensive simulation- and real-data-based benchmarking. Our 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.

False discovery rate

Unobserved confounding

Microbiome

Differential abundance

Latent factor models 

Section on Statistics in Genomics and Genetics