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A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
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A Primer for Microbiome Time-Series Analysis.

Ashley R Coenen1, Sarah K Hu2, Elaine Luo3

  • 1School of Physics, Georgia Institute of Technology, Atlanta, GA, United States.

Frontiers in Genetics
|May 7, 2020
PubMed
Summary
This summary is machine-generated.

Analyzing microbial community time-series requires specific statistical methods. This guide offers practical approaches for understanding microbial dynamics, interactions, and periodic signals in temporal data.

Keywords:
clusteringcode:Rcode:matlabinferencemarine microbiologymicrobial ecologyperiodicitytime-series analysis

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Area of Science:

  • Microbial Ecology
  • Computational Biology
  • Time-Series Analysis

Background:

  • Microbial communities are dynamic systems, and their temporal changes offer crucial insights into ecosystem function.
  • Traditional comparative microbiome studies often overlook the unique statistical challenges posed by time-series data.
  • Analyzing temporal microbial data requires specialized methods to address ecological questions effectively.

Purpose of the Study:

  • To provide a primer on statistical approaches for analyzing microbiome time-series data.
  • To address the unique challenges in analyzing temporal microbial data, focusing on compositionality and autocorrelation.
  • To facilitate the application of time-series methods in microbial ecology research.

Main Methods:

  • Development of hands-on modules in R and Matlab connecting theory, code, and data.
  • Focus on three key analytical areas: sample similarity, population interactions, and periodic signal detection.
  • Utilizing a marine microbial ecology case study to motivate the analytical approaches.

Main Results:

  • Demonstration of methods for characterizing community structure shifts and activity over time.
  • Identification of expression levels exhibiting diel periodic signals.
  • Methods for inferring putative interactions within complex microbial communities are presented.

Conclusions:

  • The primer highlights statistical considerations for robust microbiome time-series analysis.
  • Interactive tutorials aim to broaden the adoption of time-series analytic methods in microbial ecology.
  • The developed methods enhance the understanding of microbial community dynamics and function.