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LorDist: a novel method for calculating the distance based on functional data analysis with application to
Xinhe Qi1,2,3, Menghan Zhang1,4,5, Tongqing Wei2
1State Key Laboratory of Genetics and Development of Complex Phenotypes, Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Center for Evolutionary Biology, Fudan University, Shanghai, China.
Microbiology Spectrum
|July 11, 2025
Summary
We developed a new method, Longitudinal Microbial Data Distance (LorDist), to analyze how the human microbiome changes over time. LorDist effectively captures temporal patterns, improving our understanding of microbiome dynamics in health and disease.
Area of Science:
- Microbiome Research
- Computational Biology
- Data Science
Background:
- Longitudinal human microbiome data provides crucial insights into microbial dynamics over time.
- Traditional methods often fail to account for temporal relationships within subject samples.
- Challenges like data sparsity and irregular sampling hinder time-series microbiome analysis.
Purpose of the Study:
- To introduce the Longitudinal Microbial Data Distance (LorDist) method.
- To address limitations of existing methods in analyzing temporal microbiome data.
- To leverage functional data fitting for improved microbiome data analysis.
Main Methods:
- Developed the Longitudinal Microbial Data Distance (LorDist) method.
- Utilized functional data fitting to construct a distance matrix.
- Integrated temporal information from longitudinal samples within subjects.
Main Results:
- LorDist demonstrated robustness with up to 60% data sparsity.
- The method performed well across various sequencing depths and time points.
- Empirical data analysis showed LorDist excels at capturing inter-subject differences.
Conclusions:
- LorDist effectively addresses temporal autocorrelation in microbiome data.
- The method enhances the ability to distinguish between phenotypes using longitudinal data.
- LorDist shows potential for improved diagnostics and personalized therapies in microbiome science.
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