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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
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...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
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Sample size calculations for controlling the distribution of false discovery proportion in microarray experiments.

Tomonori Oura1, Shigeyuki Matsui, Koji Kawakami

  • 1Department of Biostatistics, Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku, Kyoto 606-8501, Japan. toura-kyt@umin.ac.jp

Biostatistics (Oxford, England)
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PubMed
Summary

This study introduces a novel sample size calculation method for microarray experiments to control false discoveries and true positives, even with correlated genes. The procedure accounts for varying gene block correlations and effect sizes, improving statistical power.

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Genetics
  • Biostatistics

Background:

  • Controlling false positives in multiple testing is crucial for detecting differential genes in microarray experiments.
  • The false discovery proportion (FDP) offers a more nuanced control than the false discovery rate (FDR) for correlated data.
  • Existing methods lack robust sample size calculations for controlling FDP distributions in experimental design.

Purpose of the Study:

  • To develop a sample size calculation procedure for microarray experiments that simultaneously controls the distributions of FDP and true positives.
  • To address the challenge of correlated genes by incorporating blockwise correlation structures.
  • To enable precise experimental design by considering variable gene block sizes, correlations, and effect sizes.

Main Methods:

  • Developed a novel procedure for sample size calculation.
  • Incorporated blockwise correlation structures among genes to model dependencies.
  • Utilized gene clustering on historical data to identify gene blocks and estimate parameters.

Main Results:

  • The proposed procedure effectively controls the distributions of FDP and true positives under blockwise correlations.
  • Demonstrated the adequacy of the method using simulated microarray data.
  • Successfully applied the procedure to a real-world clinical study in lymphoma.

Conclusions:

  • The developed sample size calculation method provides a powerful tool for designing microarray experiments with correlated genes.
  • This approach enhances the reliability of detecting differential genes by simultaneously controlling false and true positive rates.
  • The method is applicable to various biological contexts, including clinical studies.