[논문]Integrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional Code

Kim, Chang-Sik; Choi, Ji-Won; Yoon, Suk-Joon

doi:10.5808/gi.2009.7.2.122

Integrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional Code 원문보기

Genomics & informatics, v.7 no.2, 2009년, pp.122 - 130

Kim, Chang-Sik (Department of Biological Sciences, Sookmyung Women's University) , Choi, Ji-Won (Department of Biological Sciences, Sookmyung Women's University) , Yoon, Suk-Joon (Department of Biological Sciences, Sookmyung Women's University)

Abstract ▼ AI-Helper

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

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제안 방법

Many progressive diseases such as cancer are caused by combinatorial effect of multiple genes rather than a single gene. In this study, we integrated the microarray data with GO terms to find the perturbed molecular functions in breast cancer tissues using GO codes in molecular function. To this end, we identify several perturbed molecular functions that are implicated to play roles in the progression of breast cancer.

대상 데이터

, invasive lobular and ductal carcinomas. This dataset includes a total of 30 samples that consist of 10 samples from normal ductal cells, 10 samples from normal lobular cells, 5 samples from invasive ductal carcinoma cells, and 5 samples from invasive lobular carcinoma cells, which were microdissected from cryosections of 10 mastectomy specimens from postmenopausal patients.

이론/모형

’s (2004) study for more detail of GO code. In this study, we have focused on the GO code for the GO term of molecular function, which is defined as Gene ontology Functional Code (GFC). Note that for the probe set of Affymetrix GeneChip Human Genome U133 Plus 2.

성능/효과

First, the GFCs were divided into 152 subsets, in which each subset consists of GFCs with same or similar sizes. Second, the average value of log2-fold change in each subset was standardized such that its mean and standard deviation are 0 and 1, respectively. After z-transformation, it is therefore more viable to discriminate the perturbed GFCs of which size are relatively large (Fig.

후속연구

Moreover, it is believed that our strategy provides efficient visual interpretability of microarray data integrated with GFC to understand the perturbed molecular functions in complex progressive diseases such as breast cancer. Therefore, the selected perturbed molecular functions in this study could be further investigated to understand the basic molecular functions, which discriminate different breast cancer types.

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