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Accounting for Unobserved Confounding to Reduce False Discoveries in Microbiome Research

Abstract Number:

2719 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Chih-Ting Yang (1), Eric Koplin (1), Dong Wang (2), Tina Hartert (3), Suman Das (1), Yu Shyr (4), Chris McKennan (5), Siyuan Ma (4)

Institutions:

(1) Vanderbilt University, N/A, (2) Harvard T.H Chan School of Public Health, N/A, (3) Vanderbilt University School of Medicine, N/A, (4) Vanderbilt University Medical Center, N/A, (5) The University of Chicago, N/A

Co-Author(s):

Eric Koplin  
Vanderbilt University
Dong Wang  
Harvard T.H Chan School of Public Health
Tina Hartert  
Vanderbilt University School of Medicine
Suman Das  
Vanderbilt University
Yu Shyr  
Vanderbilt University Medical Center
Chris McKennan  
The University of Chicago
Siyuan Ma  
Vanderbilt University Medical Center

First Author:

Chih-Ting Yang  
Vanderbilt University

Presenting Author:

Chih-Ting Yang  
Vanderbilt University

Abstract Text:

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.

Keywords:

False discovery rate|Unobserved confounding|Microbiome|Differential abundance|Latent factor models|

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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