A new method for gene discovery in large-scale microarray data

Nucleic Acids Research
Kentaro YanoTakao Hanashita

Abstract

Microarrays are an effective tool for monitoring genome-wide gene expression levels. In current microarray analyses, the majority of genes on arrays are frequently eliminated for further analysis because the changes in their expression levels (ratios) are considered to be not significant. This strategy risks failure to discover whole sets of genes related to a quantitative trait of interest, which is generally controlled by several loci that make various contributions. Here, we describe a high-throughput gene discovery method based on correspondence analysis with a new index for expression ratios [arctan (1/ratio)] and three artificial marker genes. This method allows us to quickly analyze the whole microarray dataset and discover up-/down-regulated genes related to a trait of interest. We employed an example dataset to show the theoretical advantage of this method. We then used the method to identify 88 cancer-related genes from a published microarray data from patients with breast cancer. This method also allows us to predict the phenotype of a given sample from the gene expression profile. This method can be easily performed and the result is also visible in 3D viewing software that we have developed.

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Citations

Apr 19, 2012·Briefings in Functional Genomics·Abdullah A Y Gibriel
Feb 16, 2012·DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes·Alagu ManickaveluYasunari Ogihara
Dec 28, 2010·Plant & Cell Physiology·Kazuki HamadaKentaro Yano
Apr 14, 2012·International Journal of Evolutionary Biology·Hiromi NishidaKentaro Yano
Jan 17, 2012·Algorithms for Molecular Biology : AMB·Hirokazu MatsudaHiroaki Iwaisaki
Aug 13, 2013·PloS One·Tomohiro SuzukiHirokazu Kawagishi

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Methods Mentioned

BETA
PCA

Software Mentioned

multiv
Java3D
CAView
R
J2SE Kit
Cluster
GuCAL
GOTM
Treeview
BioCompass

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