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Sample Size Calculation

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...

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Power and sample size calculations for high-throughput sequencing-based experiments.

Chung-I Li¹, David C Samuels², Ying-Yong Zhao³

¹Department of Statistics, National Cheng Kung University in Taiwan.

Briefings in Bioinformatics

|June 13, 2017

Summary

This summary is machine-generated.

Power analysis is crucial for reliable high-throughput sequencing (HTS) studies. This review details HTS power computation methods for DNA, RNA, microbiome, and ChIP sequencing, guiding researchers to appropriate techniques.

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Power analysis is essential for designing statistically sound experiments and ensuring reliable results.
Established power computation methods exist for microarray-based studies (gene expression, GWAS).
High-throughput sequencing (HTS) offers high-resolution biomedical data but presents greater power computation complexity.

Purpose of the Study:

To describe available power computation methods for various HTS-based studies.
To review power analysis methods for different types of sequencing data.
To guide researchers in selecting appropriate power analysis techniques for HTS data.

Main Methods:

Review of existing literature on power computation methods for HTS data.
Categorization of methods based on sequencing data types (DNA, RNA, microbiome, ChIP).
Guidance on applying relevant power analysis techniques to specific HTS study designs.

Main Results:

Power computation for HTS data is more complex than for traditional array data.
Recent research offers methods for HTS power analysis, but awareness and adoption are limited.
A range of power computation methods are available for DNA, RNA, microbiome, and ChIP sequencing.

Conclusions:

Accurate power analysis is critical for the success of HTS-based biomedical research.
Understanding and applying appropriate power computation methods is necessary for robust HTS study design.
This review provides a valuable resource for researchers navigating power analysis in HTS studies.