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

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.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Yano K,Imai K,Shimizu A,Hanashita T

doi

10.1093/nar/gkl058

keywords:

subject

Has Abstract

pub_date

2006-03-14 00:00:00

pages

1532-9

issue

5

eissn

0305-1048

issn

1362-4962

pii

34/5/1532

journal_volume

34

pub_type

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