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Related Concept Videos

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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
The Anderson-Darling Test01:16

The Anderson-Darling Test

The Anderson-Darling test is a statistical method used to determine whether a data sample is likely drawn from a specific theoretical distribution. Unlike parametric tests, it does not require assumptions about specific parameters of the distribution. Instead, it compares the sample's empirical cumulative distribution function (ECDF) with the cumulative distribution function (CDF) of the hypothesized distribution. Critical values for the test are specific to the chosen distribution rather than...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
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Published on: October 5, 2016

A case study on choosing normalization methods and test statistics for two-channel microarray data.

Yang Xie1, Kyeong S Jeong, Wei Pan

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0392, USA.

Comparative and Functional Genomics
|July 17, 2008
PubMed
Summary

This study presents a practical approach for analyzing DNA microarray data, recommending descriptive plots for normalization and the empirical Bayes B statistic for significance testing. This method improves upon traditional statistics for understanding gene regulation.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • DNA microarray technology enables simultaneous whole-genome monitoring.
  • Analyzing microarray data presents statistical challenges, particularly in normalization and significance testing.
  • Understanding transcription regulation mechanisms is crucial in biology.

Purpose of the Study:

  • To propose a practical approach for analyzing DNA microarray data.
  • To address normalization strategies and statistical significance testing in microarray analysis.
  • To improve the understanding of transcription regulation through DNA-protein binding microarray experiments.

Main Methods:

  • Utilizing descriptive plots for preliminary data assessment and normalization decisions.
  • Recommending the empirical Bayes B statistic for comparative inference.
  • Employing the false discovery rate (FDR) for method evaluation.

Main Results:

  • Descriptive plots aid in determining the necessity and method of normalization.
  • The empirical Bayes B statistic outperforms traditional methods like M statistic and Student's t statistic.
  • Comparative analysis using FDR supports the proposed approach.

Conclusions:

  • A practical, two-step approach (descriptive plots for normalization, B statistic for inference) is effective for DNA microarray data.
  • The empirical Bayes B statistic offers a robust and explainable alternative for significance testing.
  • This methodology enhances the analysis of DNA-protein binding microarray data for biological insights.