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Related Concept Videos

Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

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, and disease...
Sample Size Calculation01:19

Sample Size Calculation

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...
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...
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...
Sampling Plans01:23

Sampling Plans

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...

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Updated: Jun 18, 2026

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

Power and sample-size estimation in human microbiome research.

Qianyi Zhou1, Yingzhou Lu2, Luman Wang3

  • 1Department of Periodontics, Shanghai Stomatological Hospital, Center on Phenomics and Precision Medicine, Intelligent Medicine Institute, Fudan University, Shanghai 200433, China.

Med (New York, N.Y.)
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

Designing human microbiome studies requires careful consideration of unique data challenges. This review offers practical guidance on study design and sample-size estimation for microbiome research to improve cohort analysis.

Keywords:
human microbiomepowersample sizestatistical analysis

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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 Genetics

Background:

  • Human microbiome research is crucial for understanding complex diseases like diabetes, IBD, and cancer.
  • High-throughput metagenomic sequencing is commonly used for comparing microbial communities in health and disease.
  • Microbiome data presents unique challenges due to its compositional, sparse, and zero-inflated nature.

Purpose of the Study:

  • To synthesize current approaches for study design and sample-size estimation in microbiome research.
  • To provide practical guidance for clinicians and researchers navigating statistical complexities.
  • To address the difficulties in robust cohort design and power-based sample-size estimation for microbiome data.

Main Methods:

  • Review of existing literature on microbiome study design.
  • Synthesis of statistical methodologies for microbiome data analysis.
  • Exploration of analytical frameworks from diversity indices to causal inference.

Main Results:

  • Microbiome data's properties (compositional, sparse, zero-inflated) complicate statistical modeling.
  • These complexities often lead to inflated sample-size requirements.
  • Diverse analytical frameworks have different statistical assumptions and are optimized for specific hypotheses.

Conclusions:

  • Effective study design and sample-size estimation are critical for valid microbiome research.
  • Understanding the statistical nuances of microbiome data is essential for accurate interpretation.
  • This review aims to equip researchers with the knowledge to overcome common statistical hurdles in microbiome studies.