• 유전체소식지
• /
• 제6권1호
• /
• pp.29-33
• /
• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• 응용통계연구
• /
• 제17권3호
• /
• pp.419-430
• /
• 2004

마이크로어레이 기술은 한번에 수만 개의 유전자를 동시에 분석할 수 있는 고효율, 고가의 새로운 연구 도구로 자리잡았으며 마이크로어레이 실험 자료의 올바른 분석을 위해서는 실험 목적에 맞는 실험계획법의 확립과 통계분석법의 적용이 중요하다 본 논문에서는 마이크로어레이 자료에서 여러 군 사이에서 발현의 차이를 보이는 유전자를 찾을 수 있는 되돌림 설계를 소개하고 ANOVA 모형을 이용하여 분석하는 방법을 제시한다. 연세대학교 암전이 연구센터의 되돌림 설계를 이용한 백혈병 자료를 MA-ANOVA(Wu et. al.(2003))를 이용하여 분석하였다

• Journal of the Korean Data and Information Science Society
• /
• 제18권4호
• /
• pp.1005-1021
• /
• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• 응용통계연구
• /
• 제22권1호
• /
• pp.89-106
• /
• 2009

There have been many new advances in the development of improved clustering methods for microarray data analysis, but traditional clustering methods are still often used in genomic data analysis, which maY be more due to their conceptual simplicity and their broad usability in commercial software packages than to their intrinsic merits. Thus, it is crucial to assess the performance of each existing method through a comprehensive comparative analysis so as to provide informed guidelines on choosing clustering methods. In this study, we investigated existing clustering methods applied to microarray data in various real scenarios. To this end, we focused on how the various methods differ, and why a particular method does not perform well. We applied both internal and external validation methods to the following eight clustering methods using various simulated data sets and real microarray data sets.

• 한국정보과학회논문지:시스템및이론
• /
• 제30권10호
• /
• pp.544-555
• /
• 2003

DNA 마이크로어레이 실험으로 나오는 데이타는 아주 많은 양의 유전자 발현 정보를 담고 있기 때문에 적절한 분석 방법이 필요하다. 대표적인 분석 방법은 계층적 클러스터링(hierarchical clustering) 방법이다. 본 논문에서는 계층적 클러스터링의 결과로 나오게 되는 덴드로그램(dendrogram)에 대해서 후처리(post-Processing)를 시행함으로써 DNA 마이크로어레이 데이타 분석을 더 용이하게 해주는 리프오더링(leaf-ordering)에 대해서 연구하였다. 먼저, 기존의 리프오더링 알고리즘들을 분석하였고, 리프오더링 알고리즘의 새로운 접근 방식을 제안하였다. 또한 이에 대한 성능을 실험하고 분석하기 위해서 계층적 클러스터링과 몇 가지 리프오더링 알고리즘들, 그리고 제안된 접근 방식을 직접 구현한 HCLO (Hierarchical Clustering & Leaf-Ordering Tool)에 대해서 소개하였다.

• 한국수정란이식학회지
• /
• 제30권1호
• /
• pp.17-21
• /
• 2015

Alteration in ion channel or transporter expression levels affects cell volume which is produced by movement of water and ion across the plasma membrane. In particular, aquaporin (AQP) channels among ion channels play a crucial role in movement of water across the cell membrane. This study was performed to identify whether AQP expression is changed in bovine follicular cystic follicles using microarray, RT-PCR and Western blotting analyses. In microarray data, AQP4 expression was decreased, whereas AQP7 was increased in cystic follicles. Additional experiments were focused on the AQP7 expression increased in cystic follicles. The microarray data was confirmed by semi-quantitative polymerase chain reaction (PCR) and Western blot analysis. AQP7 mRNA and protein expressions were significantly increased in the cystic follicles (p<0.05). Application of estrogen ($10{\mu}g/ml$) to bovine ovarian cells showed a trend of increase in AQP7 expression. From these results, we suggest that the increase in AQP7 expression in cystic follicles may play an important role in movement of water in bovine ovary. In addition, AQP7, a aquaglyceroporin permeating water and glycerol, could be a good target in development of methods for the cryopreservation of bovine ovary.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
• /
• pp.121-129
• /
• 2006

Two color or cDNA microarrays are extensively used to study relative expression levels of thousands of genes simultaneously. 0かy two tissue samples can be hybridized on a single microarray slide. Thus, a microarray slide necessarily forms an incomplete block design with block size two when more than two tissue samples are under study. We also need to control for variability in gene expression values due to the two dyes. Thus, red and green dyes form the second blocking factor in addition to slides. General design problem for these microarray experiments is discussed in this paper. Designs for factorial cDNA microarrays are also discussed.

• 응용통계연구
• /
• 제18권2호
• /
• pp.355-369
• /
• 2005

마이크로어레이 실험에서는 유전자의 기능과 상호작용의 이해를 돕기 위한 방안으로 유전자발현자료의 시각화방법이 많이 사용되고 있다. 행렬도는 유전자와 샘플들을 동시에 그려볼 수 있어서, 유전자 또는 샘플의 군집이나 유전자-샘플간 연관작용을 알아보는데 더욱 유용하게 쓰일 수 있다. 본고에서는 마이크로어레이실험에서 행렬도를 이용하여 유전자의 군집 및 연관성을 알아보는 방법을 소개하고, 추가점기법을 이용하여 새로운 샘플을 분류하는 방법을 제안하였다. Golub et al.(1999)의 백혈병 데이터와 Alizadeh et al. (2000)의 림프구데이터, Ross et al.(2000)의 NCI60 종양조직데이터를 이용하여 유용성을 살펴보았으며, 계층적 군집분석 및 k-평균 군집분석 등 다른 기법을 이용한 결과와 비교하고 이러한 기법을 행렬도와 연계하는 방안을 살펴보았다.

• 한국어병학회지
• /
• 제36권2호
• /
• pp.191-211
• /
• 2023

Rock bream iridovirus (RBIV) causes high mortality and economic losses in rock bream (Oplegnathus fasciatus) aquaculture industry in Korea. Although, the immune responses of rock bream under RBIV infection have been studied, there is not much information at the different stages of infection (initial, middle and recovery). Gene expression profiling of rock bream under different RBIV infection stages was investigated using a microarray approaches. In total, 5699 and 6557 genes were significantly up- or down-regulated over 2-fold, respectively, upon RBIV infection. These genes were grouped into categories such as innate immune responses, adaptive immune responses, complements, lectin, antibacterial molecule, stress responses, DNA/RNA binding, energy metabolism, transport and cell cycle. Interestingly, hemoglobins (α and β) appears to be important during pathogenesis; it is highly up-regulated at the initial stage and is gradually decreased when the pathogen most likely multiplying and fish begin to die at the middle or later stage. Expression levels were re-elevated at the recovery stage of infection. Among up-regulated genes, interferon-related genes were found to be responsive in most stages of RBIV infection. Moreover, X-linked inhibitor of apoptosis (XIAP)-associated factor 1 (XAF1) expression was high, whereas expression of apoptosis-relate genes were low. In addition, stress responses were highly induced in the virus infection. The cDNA microarray data were validated using quantative real-time PCR. Our results provide novel inslights into the broad immune responses triggered by RBIV at different infection stages.

• Genomics & Informatics
• /
• 제3권1호
• /
• pp.30-34
• /
• 2005

Microarray technology makes it possible to measure the expressions of tens of thousands of genes simultaneously under various experimental conditions. Identifying differentially expressed genes in each single experimental condition is one of the most common first steps in microarray gene expression data analysis. Reasonable choices of thresholds for determining differentially expressed genes are used for the next-stap-analysis with suitable statistical significances. We present a supervised model for identifying DEGs using pathway information based on the global connectivity structure. Pathway information can be regarded as a collection of biological knowledge, thus we are trying to determine the optimal threshold so that the consequential connectivity structure can be the most compatible with the existing pathway information. The significant feature of our model is that it uses established knowledge as a reference to determine the direction of analyzing microarray dataset. In the most of previous work, only intrinsic information in the miroarray is used for the identifying DEGs. We hope that our proposed method could contribute to construct biologically meaningful structure from microarray datasets.