• 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 KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.

• Genomics & Informatics
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• v.9 no.1
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• pp.28-36
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• 2011

Most statistical methods for finding interesting genes are focusing on the summary values with large fold-changes or large variations. Very few methods consider the probe level data. We developed a new measure to detect reliability that incorporates the probe level data. This reliability measure is useful for exploring the microarray data without ignoring the probe level data. It is easy to calculate, and it can be used for all the other statistical methods as a good guideline to find real differentially expressed genes. Instead of filtering out genes before the analysis, we use whole genes in the analysis and make decisions with new reliability measures.

• Molecules and Cells
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• v.20 no.1
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• pp.57-68
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• 2005

Murine erythroleukemia (MEL) cells are widely used to study erythroid differentiation thanks to their ability to terminally differentiate in vitro in response to chemical induction. At the molecular level, not much is known of their terminal differentiation apart from activation of adult-type globin gene expression. We examined changes in gene expression during the terminal differentiation of these cells using microarray-based technology. We identified 180 genes whose expression changed significantly during differentiation. The microarray data were analyzed by hierarchical and k-means clustering and confirmed by semi-quantitative RT-PCR. We identified several genes including H1f0, Bnip3, Mgl2, ST7L, and Cbll1 that could be useful markers for erythropoiesis. These genetic markers should be a valuable resource both as potential regulators in functional studies of erythroid differentiation, and as straightforward cell type markers.

• Restorative Dentistry and Endodontics
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• v.37 no.3
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• pp.142-148
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• 2012

Objectives: We analyzed gene-expression profiles after 14 day odontogenic induction of human dental pulp cells (DPCs) using a DNA microarray and sought candidate genes possibly associated with mineralization. Materials and Methods: Induced human dental pulp cells were obtained by culturing DPCs in odontogenic induction medium (OM) for 14 day. Cells exposed to normal culture medium were used as controls. Total RNA was extracted from cells and analyzed by microarray analysis and the key results were confirmed selectively by reverse-transcriptase polymerase chain reaction (RT-PCR). We also performed a gene set enrichment analysis (GSEA) of the microarray data. Results: Six hundred and five genes among the 47,320 probes on the BeadChip differed by a factor of more than two-fold in the induced cells. Of these, 217 genes were upregulated, and 388 were down-regulated. GSEA revealed that in the induced cells, genes implicated in Apoptosis and Signaling by wingless MMTV integration (Wnt) were significantly upregulated. Conclusions: Genes implicated in Apoptosis and Signaling by Wnt are highly connected to the differentiation of dental pulp cells into odontoblast.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.444-450
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• 2005

Epidermal growth factor (EGF) activates many intracellular effector molecules, which subsequently influence the expression levels of many genes involved in cell growth, apoptosis and signal transduction, etc. In this study, the early response of gene expressions due to EGF treatment was monitored using oligonucleotide DNA microarrays in rat schwannoma cell lines. An immunoblotting experiment showed the successful activation of EGF receptors and an effector protein, STAT5, due to EGF treatment. The microarray study showed that 35 genes were significantly induced and 2 were repressed within 60 min after the treatment. The list of induced genes included early growth response 1, suppressor of cytokine signaling 3, c-fos, interferon regulatory factor 1 and early growth response 2, etc. According to the microarray data, six of these were induced by more than 10-fold, and showed at least two different induction patterns, indicating complicated regulatory mechanisms in the EGF signal transduction.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.195-198
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• 2004

One Important problem in a cancer microarray study is to identify a set of genes from which a molecular prognostic indicator can be developed. In parallel with this problem is to validate the chosen set of genes. We develop in this note a K-fold cross validation procedure by combining a 'pre-validation' technique and a bootstrap resampling procedure in the Cox regression . The pre-validation technique predicts the microarray predictor of a case without having seen the true class level of the case. It was suggested by Tibshirani and Efron (2002) to avoid the possible over-fitting in the regression in which a microarray based predictor is employed. The bootstrap resampling procedure for the Cox regression was proposed by Sauerbrei and Schumacher (1992) as a means of overcoming the instability of a stepwise selection procedure. We apply this K-fold cross validation to the microarray data of 92 gastric cancers of which the experiment was conducted at Cancer Metastasis Research Center, Yonsei University. We also share some of our experience on the 'false positive' result due to the information leak.

• Environmental Analysis Health and Toxicology
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• v.20 no.2 s.49
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• pp.187-194
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• 2005

Octachlorostyrene (OCS) is a primarily concerning chemical in many countries because of its persistent and bioaccumulative properties in the environment. OCS is not commercially manufactured or used but it may be produced during incineration or chemical synthetic processes involving chlorinated compounds. There are several reports that OCS was found in the waters, sediments, fish, mussels, and also in human tissues. However, systematic studies on the OCS toxicities are scarce in literature. In this study, we tried to get the gene expression data using medaka DNA chip to identify biomarkers of OCS exposure. Medaka (Oryzias latipes.) was exposed to OCS 1 ppm for 2 days and 10 days, respectively. Total RNA was extracted and purified by guanidine thiocyanate method and the Cy3- and Cy5-labelled cDNAs produced by reverse trancription of the RNA were hybridized to medaka microarray. As results, eighty five genes were found to be down-or up regulated by OCS. Some of the genes were listed and confirmed by real-time PCR.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.124-130
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• 2004

Microarray expression datasets are incessantly cumulated with the aid of recent technological advances. One of the first steps for analyzing these data under various experimental conditions is determining differentially expressed genes (DEGs) in each condition. Reasonable choices of thresholds for determining differentially expressed genes are used for the next -step-analysis with suitable statistical significances. We present a 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 tying 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 network structure from microarray datasets.