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Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
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Shrinkage improves estimation of microbial associations under different normalization methods.

Michelle Badri1, Zachary D Kurtz2, Richard Bonneau1

  • 1Department of Biology, New York University, New York, NY 10012, USA.

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Summary
This summary is machine-generated.

Accurate statistical association estimation in microbiome data requires careful normalization and shrinkage estimation, especially with limited sample sizes. These methods improve the quality of microbial association networks and taxonomic coherence.

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Area of Science:

  • Microbiome research
  • Statistical genomics
  • Bioinformatics

Background:

  • Estimating statistical associations in microbial genomic survey data is crucial for microbiome research.
  • Challenges include data compositionality, small sample sizes, and technical variability, necessitating data normalization.
  • Standard association measures are often obstructed by these experimental limitations.

Purpose of the Study:

  • To investigate the impact of data normalization and sample size on microbial association estimation.
  • To analyze the statistical properties of correlation and proportionality estimators using large-scale American Gut Project (AGP) data.
  • To evaluate the effectiveness of shrinkage estimation for improving taxon-taxon association estimates.

Main Methods:

  • Leveraged large-scale American Gut Project (AGP) survey data.
  • Analyzed statistical properties of correlation and proportionality estimators under various sample sizes and normalization schemes (RNA-seq, log-ratio transformations).
  • Applied shrinkage estimation and constructed microbial association networks.

Main Results:

  • Shrinkage estimation universally improves taxon-taxon association estimates for microbiome data.
  • Large-scale association patterns in AGP data fall into five normalization-dependent classes.
  • Variance-stabilizing and log-ratio normalization approaches yield the most coherent taxonomic and structural estimates in network construction.

Conclusions:

  • Data normalization and shrinkage estimation are critical for accurate microbiome association studies, particularly with small sample sizes.
  • The choice of normalization method significantly impacts downstream analyses like network construction.
  • Findings offer guidance for optimizing microbiome data analysis workflows.