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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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Two-Way Gene Interaction From Microarray Data Based on Correlation Methods.

Hamid Alavi Majd1, Atefeh Talebi2, Kambiz Gilany3

  • 1Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.

Iranian Red Crescent Medical Journal
|September 14, 2016
PubMed
Summary
This summary is machine-generated.

This study constructed gene co-expression networks using correlation coefficients. Pearson and Spearman correlations aligned with visualization, unlike Blomqvist's coefficient, possibly due to limited data.

Keywords:
Gene ExpressionGene OntologyGene Regulatory NetworksMolecular StructureNonparametric

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • High-throughput techniques have accelerated gene network analysis.
  • Gene networks model gene interactions, but extracting this information is challenging.
  • Gene co-expression network construction is crucial for understanding gene activity.

Purpose of the Study:

  • To build two-way gene networks using parametric and nonparametric correlation coefficients.
  • To score gene pairs and establish a threshold for network connections.
  • To compare different correlation methods for gene network construction.

Main Methods:

  • Constructed two-way gene networks using Pearson, Spearman, and Blomqvist correlation coefficients.
  • Applied methods to six venous thrombosis genes, creating interaction score matrices.
  • Validated results using Cytoscape and Gene Ontology (GO) with R and Bioconductor.

Main Results:

  • Pearson and Spearman correlations yielded consistent results, validated by Cytoscape and GO.
  • Blomqvist's coefficient results were not supported by the visual validation methods.
  • Discrepancies suggest potential limitations with certain correlation measures or data size.

Conclusions:

  • Correlation coefficient results may differ from visualization outcomes.
  • The limited dataset size could be a factor in observed discrepancies.
  • Further investigation with larger datasets is warranted to confirm findings.