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Related Concept Videos

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...
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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Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR
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Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Published on: January 22, 2014

A permutation-based multiple testing method for time-course microarray experiments.

Insuk Sohn1, Kouros Owzar, Stephen L George

  • 1Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. insuk.sohn@duke.edu

BMC Bioinformatics
|October 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a computationally efficient permutation method for analyzing time-course gene expression data. It accurately identifies genes with time-dependent expression profiles in single or multiple biological groups.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Time-course microarray experiments are crucial for understanding gene expression dynamics.
  • Existing methods for analyzing temporal gene expression patterns can be computationally intensive.
  • Accurate control of Type I error in multiple testing is essential for reliable results.

Purpose of the Study:

  • To develop a computationally efficient permutation-based multiple testing procedure for time-course gene expression analysis.
  • To improve the identification of genes with time-dependent expression trajectories.
  • To provide a robust method for both single-group and multi-group comparisons.

Main Methods:

  • Utilized a permutation-based multiple testing procedure building on Storey et al.'s (2005) goodness-of-fit statistic.
  • Developed an efficient computation algorithm for the proposed method.
  • Validated the procedure through extensive simulations and application to Caenorhabditis elegans dauer developmental data.

Main Results:

  • The proposed permutation-based method demonstrated robust performance in simulations.
  • The method effectively controls Type I error in the analysis of complex gene expression correlations.
  • Successfully identified time-dependent gene expression patterns in the C. elegans dataset.

Conclusions:

  • The developed method is computationally efficient and highly applicable.
  • It accurately identifies genes with time-dependent expression in single biological groups.
  • It is also effective for identifying genes with group-dependent time-profiles in multi-group settings.