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Related Concept Videos

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...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: May 8, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

General power and sample size calculations for high-dimensional genomic data.

Maarten van Iterson1, Mark A van de Wiel, Judith M Boer

  • 1Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.

Statistical Applications in Genetics and Molecular Biology
|August 13, 2013
PubMed
Summary
This summary is machine-generated.

Choosing the right number of biological replicates is key for gene expression experiments. This study introduces a pilot-data method for power and sample size analysis, improving accuracy for complex designs and RNA-seq data.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Related Experiment Videos

Last Updated: May 8, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Determining the optimal number of biological replicates is critical for microarray and next-generation sequencing (NGS) experiments to balance statistical power and experimental cost.
  • Insufficient replicates reduce the power to detect small effect sizes in differentially expressed genes, while excessive replicates increase costs.
  • Existing power and sample size methods are often limited to two-group comparisons and require predefined effect sizes.

Purpose of the Study:

  • To develop a pilot-data-based method for power and sample size analysis applicable to diverse experimental designs.
  • To enable accurate estimation of effect sizes directly from pilot data.
  • To extend power and sample size calculations to a broader range of statistical models, including those used for RNA-seq data analysis.

Main Methods:

  • A novel pilot-data-driven approach for power and sample size analysis.
  • Incorporation of chi-squared distributed test statistics to accommodate various models.
  • Estimation of effect size densities from pilot data for improved accuracy.

Main Results:

  • The proposed method demonstrates robust performance in power and sample size calculations for both microarray and NGS data.
  • The pilot-data based estimation of effect sizes is effective and comparable to existing methods for two-group comparisons.
  • The method successfully handles more general experimental designs and complex statistical models.

Conclusions:

  • The developed method provides a flexible and accurate tool for determining the optimal number of biological replicates in genomic experiments.
  • It enhances the reliability of power and sample size analysis, particularly for complex designs and RNA-sequencing data.
  • This approach aids researchers in optimizing experimental resource allocation and maximizing the chances of detecting biologically significant findings.