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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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

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Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
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Published on: February 21, 2014

Multiple testing and its applications to microarrays.

Yongchao Ge1, Stuart C Sealfon, Terence P Speed

  • 1Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, NY 10029, USA. yongchao.ge@mssm.edu

Statistical Methods in Medical Research
|January 6, 2010
PubMed
Summary
This summary is machine-generated.

This study addresses multiple testing in gene expression data. It reviews methods for controlling false positive errors, comparing their performance on microarray datasets.

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Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Microarray experiments generate large datasets, leading to multiple testing challenges.
  • Controlling false positive errors is crucial for reliable gene expression analysis.

Purpose of the Study:

  • To review and compare statistical methods for controlling false positive errors in large-scale multiple testing.
  • To evaluate the performance of these methods using real gene expression data.

Main Methods:

  • Description of common error control criteria: familywise error rates, false discovery rates, and false discovery proportion rates.
  • Application and comparison of various statistical methods for controlling these error rates.
  • Analysis of gene expression data from two distinct microarray studies.

Main Results:

  • Comparison of the properties and performance of different multiple testing procedures.
  • Evaluation of the advantages and disadvantages of each method in practical applications.

Conclusions:

  • Understanding and applying appropriate multiple testing methods is essential for accurate interpretation of gene expression data.
  • The choice of method depends on the specific research question and desired error control.