Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

6.9K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.9K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

520
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
520
Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

28
Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity,...
28
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

12
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...
12
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

15
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...
15
Sampling Plans01:23

Sampling Plans

1.2K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Modelling the role of the microbiome in antimicrobial resistance across scales.

Nature microbiology·2026
Same author

Deconfounded, quantitative microbiome profiling identifies robust multiple sclerosis markers and clinical covariate associations.

Gut microbes·2026
Same author

Large-scale analysis of temporal gene expression variation in peripheral blood.

Nature communications·2026
Same author

Exposure to currently used pesticides in Belgian children: Urinary biomonitoring and risk assessment of frequently detected chlorpyrifos and pyrethroid metabolites.

Environmental research·2026
Same author

Kidney Function Modulates Gut Microbial Metabolism.

Toxins·2026
Same author

Toward ethical human microbiome research: improving health through radical interdisciplinary and intercultural co-laboration.

Microbiome·2026

Related Experiment Video

Updated: Mar 21, 2026

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
08:14

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model

Published on: February 28, 2018

9.4K

A web application for sample size and power calculation in case-control microbiome studies.

Federico Mattiello1, Bie Verbist2, Karoline Faust3

  • 1Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Gent, 9000.

Bioinformatics (Oxford, England)
|May 7, 2016
PubMed
Summary

Calculating sample sizes for microbial composition studies is crucial. Stratification significantly boosts statistical power, reducing the number of samples needed to detect differences in microbial communities.

More Related Videos

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

31.5K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

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

Published on: August 25, 2018

13.6K

Related Experiment Videos

Last Updated: Mar 21, 2026

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
08:14

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model

Published on: February 28, 2018

9.4K
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

31.5K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

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

Published on: August 25, 2018

13.6K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Genetics

Background:

  • Designing case-control studies requires accurate sample size estimation for detecting microbial composition differences.
  • Statistical power is essential for reliable hypothesis testing in microbiome research.

Purpose of the Study:

  • To develop and present a power calculation tool for microbiome case-control studies.
  • To compare the power of different statistical tests for analyzing microbial community data.

Main Methods:

  • Utilized simulation-based power calculations employing the Dirichlet-Multinomial model.
  • Implemented power calculations for a generalized Wald test and the Wilcoxon-Mann-Whitney test.
  • Developed a web interface using R Shiny for easy parameter specification.

Main Results:

  • Statistical power increased considerably when samples were stratified.
  • Stratification allows for the detection of the same effect size with fewer samples and equivalent power.
  • The generalized Wald test and Wilcoxon-Mann-Whitney test showed varying performance based on study design.

Conclusions:

  • Stratification is a highly effective strategy for enhancing statistical power in microbiome studies.
  • The developed tool and methods aid researchers in optimizing sample size and study design.
  • Accurate power calculations are fundamental for robust microbiome research and reproducible findings.