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Multivariate statistical monitoring system for microbial population dynamics
Koji Ishiya1, Sachiyo Aburatani2
1Bioproduction Research Institute, National Institute of Advance Industrial Science and Technology (AIST), Sapporo 062-8517, Japan.
Physical Biology
|November 17, 2021
Summary
Detecting dynamic microbiome changes is crucial. A new multivariate statistical process control method accurately tracks time-series shifts in microbial communities, even with many species.
Area of Science:
- Microbiome research
- Microbial ecology
- Statistical analysis
Background:
- Microbial communities (microbiomes) are dynamic and change with environmental conditions.
- Understanding these changes is vital for assessing environmental impacts on microbial populations.
- Existing methods may struggle with the complexity of large, multi-species microbiomes.
Purpose of the Study:
- To develop a novel method for detecting time-series changes in microbiomes.
- To enable robust monitoring of dynamic shifts in complex microbial communities.
- To apply and validate the method using human gut microbiome data.
Main Methods:
- Utilized multivariate statistical process control (MSPC).
- Focused on analyzing interspecies structures within the microbiome.
- Applied the MSPC approach to time-series data from a human gut microbiome study.
Main Results:
- Successfully detected time-series changes in microbiota composition.
- Accurately traced microbial shifts induced by a dietary intervention.
- Effectively monitored the microbiome's recovery process post-intervention.
Conclusions:
- The proposed MSPC method offers robust detection of microbiome dynamics.
- This approach is effective for complex, multi-species microbial communities.
- The method is valuable for monitoring environmental impacts and interventions on microbiomes.
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