An Identification Method of Data-Specific GO Terms from a Microarray Data Set

Yoichi YAMADA; Ken-ichi HIROTANI; Kenji SATOU; Ken-ichiro MURAMOTO

doi:10.1587/transinf.E92.D.1093

An Identification Method of Data-Specific GO Terms from a Microarray Data Set

Yoichi YAMADA, Ken-ichi HIROTANI, Kenji SATOU, Ken-ichiro MURAMOTO

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Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.

Publication: IEICE TRANSACTIONS on Information Vol.E92-D No.5 pp.1093-1102

Publication Date: 2009/05/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E92.D.1093

Type of Manuscript: PAPER

Category: Data Mining

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Yoichi YAMADA, Ken-ichi HIROTANI, Kenji SATOU, Ken-ichiro MURAMOTO, "An Identification Method of Data-Specific GO Terms from a Microarray Data Set" in IEICE TRANSACTIONS on Information, vol. E92-D, no. 5, pp. 1093-1102, May 2009, doi: 10.1587/transinf.E92.D.1093.
Abstract: Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1093/_p

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@ARTICLE{e92-d_5_1093,
author={Yoichi YAMADA, Ken-ichi HIROTANI, Kenji SATOU, Ken-ichiro MURAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={An Identification Method of Data-Specific GO Terms from a Microarray Data Set},
year={2009},
volume={E92-D},
number={5},
pages={1093-1102},
abstract={Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.},
keywords={},
doi={10.1587/transinf.E92.D.1093},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - An Identification Method of Data-Specific GO Terms from a Microarray Data Set
T2 - IEICE TRANSACTIONS on Information
SP - 1093
EP - 1102
AU - Yoichi YAMADA
AU - Ken-ichi HIROTANI
AU - Kenji SATOU
AU - Ken-ichiro MURAMOTO
PY - 2009
DO - 10.1587/transinf.E92.D.1093
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2009
AB - Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.
ER -