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DNA Microarrays

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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Related Experiment Video

Updated: Jul 3, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A fast Bayesian change point analysis for the segmentation of microarray data.

Chandra Erdman¹, John W Emerson

¹Department of Statistics, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511, USA. chandra.erdman@yale.edu

Bioinformatics (Oxford, England)

|August 1, 2008

Summary

The Bayesian change point (bcp) R package now offers an O(n) implementation, significantly accelerating the detection of genetic alterations in cancer research. This enhanced version provides rapid and accurate analysis of microarray data, improving efficiency for researchers.

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Last Updated: Jul 3, 2026

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Detecting genetic alterations (gains, losses, amplifications, deletions) is crucial for cancer research.
Understanding genetic differences between cancerous and non-cancerous tissues aids in identifying cancer progression genes.
The Bayesian change point method is effective for analyzing genetic alteration data.

Purpose of the Study:

To introduce a new, computationally efficient implementation of the Bayesian change point method.
To improve the speed and memory usage of the R package bcp for analyzing high-resolution microarray data.

Main Methods:

Implemented a new O(n) version of the Bayesian change point algorithm.
Utilized Markov Chain Monte Carlo (MCMC) for Bayesian analysis.
Incorporated parallel computing support via NetWorkSpaces for further speed enhancements.

Main Results:

The new implementation (bcp 2.1) achieves O(n) speed and memory complexity, reducing analysis time from 45 minutes to 45 seconds for 10,000 observations.
Demonstrated accurate detection of genetic aberrations of varying widths and magnitudes in simulated and real microarray data.
The enhanced bcp package provides a significant speed improvement for analyzing large-scale genomic data.

Conclusions:

The O(n) implementation of the Bayesian change point method in the bcp R package dramatically improves computational efficiency.
This advancement enables faster and more accurate identification of genetic alterations in cancer research using high-resolution microarray data.
The bcp package, particularly versions 2.0 and higher, offers a valuable tool for genomic data analysis.