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Related Concept Videos

Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Modern Molecular Taxonomy

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Related Experiment Video

Updated: May 13, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Published on: May 16, 2022

Comparative analysis of microbiome measurement platforms using latent variable structural equation modeling.

Xiao Wu1, Kathryn Berkow, Daniel N Frank

  • 1Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu

BMC Bioinformatics
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

A new statistical method, latent variable structural equation modeling (SEM), effectively compares, selects, and combines microbiome measurement platforms for accurate bacterial analysis.

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

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Last Updated: May 13, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Published on: May 16, 2022

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Culture-independent phylogenetic analysis using 16S ribosomal RNA (rRNA) gene sequences is crucial for bacterial profiling.
  • Multiple platforms exist for enumerating bacterial taxa, including Sanger sequencing, next-generation sequencing, and qPCR.
  • A statistical tool is needed to compare, select, and combine these diverse measurement platforms.

Purpose of the Study:

  • To introduce latent variable structural equation modeling (SEM) as a novel statistical approach for comparing microbiome measurement platforms.
  • To address the need for a unified analysis by optimally weighing data from different platforms.

Main Methods:

  • Latent variable structural equation modeling (SEM) was employed.
  • The model treats the true relative frequency of bacterial taxa as a latent variable.
  • It estimates platform reliabilities and similarities for optimal data integration.

Main Results:

  • Latent variable SEM provides a more general and realistic modeling approach than traditional methods like repeated measures ANOVA.
  • The method demonstrated superior goodness-of-fit and more reliable analysis results in a human inflammatory bowel disease microbiome study.
  • It enables optimal weighing of measurements for a unified microbiome composition analysis.

Conclusions:

  • Latent variable SEM is a powerful tool for comparing, selecting, and combining data from various microbiome measurement platforms.
  • This statistical method is applicable to diverse biological settings beyond microbiome research.
  • It addresses the growing need for robust analysis tools in light of rapidly evolving biotechnologies.