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Related Concept Videos

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Weibull Distribution
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Published on: September 18, 2021

A semiparametric Bayesian approach for estimating the gene expression distribution.

Fei Zou1, Hanwen Huang, Joseph G Ibrahim

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. fzou@bios.unc.edu

Journal of Biopharmaceutical Statistics
|March 24, 2010
PubMed
Summary

Accurately estimating sample sizes for gene expression microarray studies is crucial. This research introduces a robust semiparametric model to improve sample size calculations, ensuring more reliable experimental design and analysis.

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Published on: December 10, 2012

Area of Science:

  • Genomics
  • Biostatistics
  • Computational Biology

Background:

  • Gene expression microarrays are vital for global gene expression analysis.
  • A common misconception is that few arrays suffice for meaningful results, necessitating robust sample size estimation.
  • Accurate sample size calculation is statistically essential for microarray experiments.

Purpose of the Study:

  • To develop a more robust statistical method for estimating sample sizes in gene expression microarray studies.
  • To address limitations of existing parametric models by proposing a flexible semiparametric approach.
  • To provide a framework for reliable experimental design and multiple-comparison control.

Main Methods:

  • Extension of a parametric mixture model to a semiparametric Dirichlet process mixture model.
  • Utilizing a Bayesian inference framework with Markov-chain Monte Carlo (MCMC) methods.
  • Estimation of gene expression effect size distributions without specifying parametric forms.

Main Results:

  • The proposed semiparametric model offers a robust alternative for sample size calculations.
  • Demonstrated effectiveness through simulations and a real-world murine lung gene expression study.
  • Improved accuracy in determining the necessary number of arrays for reliable findings.

Conclusions:

  • The Dirichlet process mixture model provides a flexible and robust approach to sample size estimation in gene expression studies.
  • This method enhances the statistical rigor of microarray experimental design.
  • Accurate sample size determination is key to achieving biologically meaningful and statistically valid results.