• KOGO NEWS
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• v.6 no.1
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• pp.29-33
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• 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.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.419-430
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• 2004

Since cDNA microarray experiments can monitor expression levels for thousands of genes simultaneously, the experimental designs and their analyzing methods are very important for successful analysis of microarray data. The loop design is discussed for selecting differentially expressed genes among several treatments and the analysis of variance method is introduced to normalize microarray data and provide estimates of the interesting quantities. MA-ANOVA is used to illustrate this method on a recently collected loop designed microarray data at Cancer Metastasis Research Center, Yonsei University.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 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.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.89-106
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• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.544-555
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• 2003

Since the result data from DNA microarray experiments contain a lot of gene expression information, adequate analysis methods are required. Hierarchical clustering is widely used for analysis of gene expression profiles. In this paper, we study leaf-ordering, which is a post-processing for the dendrograms output by hierarchical clusterings to improve the efficiency of DNA microarray data analysis. At first, we analyze existing leaf-ordering algorithms and then present new approaches for leaf-ordering. And we introduce a software HCLO(Hierarchical Clustering ＆ Leaf-Ordering Tool) that is our implementation of hierarchical clustering, some of existing leaf-ordering algorithms and those presented in this paper.

• Journal of Embryo Transfer
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• v.30 no.1
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• pp.17-21
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• 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
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• 2006.04a
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• pp.121-129
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• 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.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.355-369
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• 2005

Genome sequencing and microarray technology produce ever-increasing amounts of complex data that needs statistical analysis. Visualization is an effective analytic technique that exploits the ability of the human brain to process large amounts of data. In this study, biplot approach applied to microarray data to see the relationship between genes and samples. The supplementary data method to classify new sample to known category is suggested. The methods are validated by applying it to well known microarray data such as Golub et al.(1999), Alizadeh et al.(2000), Ross et al.(2000). The results are compared to the results of several clustering methods. Modified graph which combine partitioning method and biplot is also suggested.

• Journal of fish pathology
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• v.36 no.2
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• pp.191-211
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• 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
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• v.3 no.1
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• pp.30-34
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• 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.