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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...
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Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
09:45

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Published on: August 10, 2017

A multivariate growth curve model for ranking genes in replicated time course microarray data.

Jemila S Hamid1, Joseph Beyene

  • 1The Hospital for Sick Children and University of Toronto. jemila@utstat.toronto.edu

Statistical Applications in Genetics and Molecular Biology
|July 4, 2009
PubMed
Summary
This summary is machine-generated.

We developed a new model for analyzing time course microarray data to rank genes. This method accounts for correlations between time points and improves the identification of significant gene expression patterns.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Gene expression analysis in time course experiments is complex due to temporal correlations.
  • Understanding gene expression dynamics over time is crucial for biological discovery.

Purpose of the Study:

  • To propose a multivariate growth curve model for ranking genes in replicated time course microarray data.
  • To accurately estimate mean gene expression profiles while considering temporal dependencies.

Main Methods:

  • Utilized a multivariate growth curve model incorporating within-individual correlation and temporal ordering.
  • Treated time as a continuous variable, assuming polynomial profiles for time dependence.
  • Applied a moderated likelihood ratio test on transformed data for gene ranking across biological groups.

Main Results:

  • The proposed model effectively ranks genes by considering temporal patterns and correlations.
  • The multivariate framework enhances inference by leveraging information across all groups and genes.
  • Simulation studies and real data analysis demonstrate the method's performance.

Conclusions:

  • The developed model offers a robust approach for analyzing time course microarray data.
  • It accurately identifies differentially expressed genes by accounting for complex temporal dynamics.
  • This methodology provides a valuable tool for genomic research involving time-series experiments.