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Related Experiment Video

Updated: Aug 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Effective data filtering is prerequisite for robust microbial association network construction.

Mengqi Wang¹, Qichao Tu^1,2,3,4

¹Institute of Marine Science and Technology, Shandong University, Qingdao, China.

Frontiers in Microbiology

|October 21, 2022

Summary

This summary is machine-generated.

Zero values in microbial data significantly impact association network construction, especially for rare microbes. Excluding paired zeros is recommended over imputation for accurate microbial ecology and environmental science studies.

Keywords:

association networks correlation inference data filtering microbial community zero values

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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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Published on: August 25, 2018

Area of Science:

Microbial Ecology
Environmental Science
Bioinformatics

Background:

Microorganisms form complex assemblages crucial for ecosystem functions and stability.
Understanding microbial interactions via association networks is vital.
Zero values in microbial profiles pose significant challenges for accurate network construction.

Purpose of the Study:

To investigate the impact of zero-value treatment on microbial association network construction.
To compare different zero-value handling methods and correlation techniques.
To provide recommendations for robust microbial network analysis.

Main Methods:

Utilized the TARA Oceans microbial profile dataset.
Applied various zero-value treatment approaches.
Employed different correlation methods to assess network variations.
Evaluated the effect of zero-handling on correlation coefficients, particularly for less frequent taxa.

Main Results:

Zero-value treatment dramatically altered correlation coefficient values.
Less frequent microbial taxa correlations were most affected by zero-value handling.
Negative correlations proved particularly sensitive, often derived from low-overlap taxa.
Microbial association networks showed significant differences based on the zero-value treatment method used.

Conclusions:

Zero-value issues critically influence microbial association network outcomes.
Sequential calculation of correlation coefficients excluding paired zeros is recommended.
Imputing missing values with pseudo-values is not advised.
Researchers must carefully consider zero-value handling in microbial data analysis for reliable ecological insights.