LorDist: a novel method for calculating the distance based on functional data analysis with application to longitudinal microbial data
Xinhe Qi, Menghan Zhang, Tongqing Wei, Jinran Lin, Xingming Zhao, Yin Yao, Yueqing Hu, Yan Zheng
TL;DR
LorDist is a new method for analyzing longitudinal microbiome data that better captures temporal patterns and improves detection of biological differences.
Contribution
LorDist introduces a novel distance calculation method using functional data analysis for longitudinal microbiome studies.
Findings
LorDist performs robustly even with up to 60% sparsity and varying sequencing depths.
LorDist outperforms existing methods in capturing meaningful differences in longitudinal microbiome data.
LorDist successfully analyzed real-world datasets related to inflammatory bowel disease and infant gut development.
Abstract
Longitudinal human microbial data offer insights into microbiome dynamics over time. Traditional methods usually overlook temporal relationships among samples from the same subject. Here, we presented the Longitudinal Microbial Data Distance (LorDist) method, which uses functional data fitting to construct a distance matrix integrating information from the same subject at different time points. Simulation data showed that LorDist handled well up to 60% sparseness and worked robustly with various sequencing depths and time points. Empirical data analysis demonstrated that LorDist excels in capturing differences across subjects with longitudinal microbiome data. LorDist presented the potential of longitudinal microbial data in addressing temporal autocorrelation and distinguishing phenotypes. Longitudinal analysis of the human microbiome is critical for understanding its dynamic role in…
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Taxonomy
TopicsMetabolomics and Mass Spectrometry Studies · Gene expression and cancer classification · Gut microbiota and health