• Genomics & Informatics
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• v.4 no.3
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• pp.110-117
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• 2006

In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.147-154
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• 2003

Microarray information system is a complex system to manage, analyze and interpretate microarray gene expression data. Establishment of well-defined development process is very essential for understanding the complexity and organization of the system. We performed object-oriented analysis using Unified Modeling Language (UML) in specifying, visualizing and documenting microarray information system. The object-oriented analysis consists of three major steps: (i) use case modeling to describe various functionalities from the user's perspective (ii) dynamic modeling to illustrate behavioral aspects of the system (iii) object modeling to represent structural aspects of the system. As a result of our modeling activities we provide the UML diagrams showing various views of the microarray information system. We believe that the object-oriented analysis ensures effective documentations and communication of information system requirements. Another useful feature of object-oriented technique is structural continuity to standard microarray data model MAGE-OM (Microarray Gene Expression Object Model). The proposed modeling e(forts can be applicable for integration of biomedical information system.

• Microbiology and Biotechnology Letters
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• v.38 no.1
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• pp.70-76
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• 2010

The single-stranded large circular (LC)-sense DNA were utilized as probes for DNA chip experiments. The microarray experiment using LC-sense DNA probes found differentially expressed genes in A549 cells as compared to WI38VA13 cells, and microarray data were well-correlated with data acquired from quantitative real-time RT-PCR. A 5K LC-sense DNA microarray was prepared, and the repeated experiments and dye swap test showed consistent expression patterns. Subsequent functional analysis using LC-antisense library of overexpressed genes identified several genes involved in A549 cell growth. These experiments demonstrated proper feature of LC-sense molecules as probe DNA for microarray and the potential utility of the combination of LC-sense microarray and antisense libraries for an effective functional validation of genes.

• Molecules and Cells
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• v.22 no.3
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• pp.254-261
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• 2006

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.

• Genomics & Informatics
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• v.6 no.4
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• pp.202-209
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• 2008

Biological pathways are known as collections of knowledge of certain biological processes. Although knowledge about a pathway is quite significant to further analysis, it covers only tiny portion of genes that exists. In this paper, we suggest a model to extend each individual pathway using a microarray expression data based on the known knowledge about the pathway. We take the Rosetta compendium dataset to extend pathways of Saccharomyces cerevisiae obtained from KEGG (Kyoto Encyclopedia of genes and genomes) database. Before applying our model, we verify the underlying assumption that microarray data reflect the interactive knowledge from pathway, and we evaluate our scoring system by introducing performance function. In the last step, we validate proposed candidates with the help of another type of biological information. We introduced a pathway extending model using its intrinsic structure and microarray expression data. The model provides the suitable candidate genes for each single biological pathway to extend it.

• 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 the Korean Data and Information Science Society
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• v.23 no.1
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• pp.39-51
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• 2012

Cluster analysis has proven to be a useful tool for investigating the association structure among genes and samples in a microarray data set. We applied several cluster validation measures to evaluate the performance of clustering algorithms for analyzing microarray gene expression data, including hierarchical clustering, K-means, PAM, SOM and model-based clustering. The available validation measures fall into the three general categories of internal, stability and biological. The performance of clustering algorithms is evaluated using simulated and SRBCT microarray data. Our results from simulated data show that nearly every methods have good results with same result as the number of classes in the original data. For the SRBCT data the best choice for the number of clusters is less clear than the simulated data. It appeared that PAM, SOM, model-based method showed similar results to simulated data under Silhouette with of internal measure as well as PAM and model-based method under biological measure, while model-based clustering has the best value of stability measure.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.161-164
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• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.149-152
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• 2007

본 논문은 바이오 인포메틱스의 분야를 간단히 소개하고 기능유전체학에서 microarray 실험에 대한 통계적 방법론을 살펴보고자 한다. 또한 DNA chip 설계와 생물학적 특정에 대해 살펴보고 각 분야에서 적용되는 통계적 방법을 연구분석 해보고자 한다.