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 Resjournal_title
Nucleic acids researchauthors
Yano K,Imai K,Shimizu A,Hanashita Tdoi
10.1093/nar/gkl058keywords:
subject
Has Abstractpub_date
2006-03-14 00:00:00pages
1532-9issue
5eissn
0305-1048issn
1362-4962pii
34/5/1532journal_volume
34pub_type
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