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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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A new class of mixture models for differential gene expression in DNA microarray data.

Ming-Hui Chen¹, Joseph G Ibrahim, Yueh-Yun Chi

¹Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.

Journal of Statistical Planning and Inference

|September 28, 2011

Summary

This study introduces a novel mixture model to analyze gene expression data, effectively identifying differentially expressed genes. The new method demonstrates superior performance in controlling false positives and negatives in gene selection.

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

Bioinformatics
Statistical Genetics
Computational Biology

Background:

Analyzing microarray data requires distinguishing expressed from underexpressed genes.
Gene expression levels often exhibit bimodal distributions, with one peak for expressed and another for underexpressed genes.

Purpose of the Study:

To propose a new class of mixture models for accommodating bimodality in gene expression data.
To develop a novel criterion for identifying differentially expressed genes between two subject groups.

Main Methods:

Utilized a novel mixture model with a random threshold to address bimodality.
Employed empirical Bayes methodology for prior elicitation and hyperparameter estimation.
Derived a new gene selection criterion for differential expression analysis.

Main Results:

The proposed model effectively handles bimodality in gene expression distributions.
The new gene selection criterion exhibits excellent false positive and false negative rates in simulations.
The methodology was applied to a gastric cancer dataset for illustration.

Conclusions:

The novel mixture model provides a robust approach for analyzing gene expression data.
The developed criterion enhances the accuracy of identifying differentially expressed genes.
This method offers significant improvements for microarray data analysis in biological research.