• Journal of the Korean Data and Information Science Society
• /
• 제23권1호
• /
• pp.1-11
• /
• 2012

환자의 생존시간과 함께 유전자 마이크로어레이 자료가 주어진 경우 생존에 유의한 영향을 미치는 대사경로를 찾는 방법을 연구하였다. 기존의 방법인 유전자 집합 농축도 분석, 글로벌 검정과 왈드 형태 검정을 비교 분석하였고, 치환을 통하여 p값을 구하는 단점을 개선한 수정된 왈드 형태 검정을 제안하였다. 모의실험과 실제자료 분석을 이용하여 새로운 방법의 적용 가능성을 보였다.

• Journal of the Korean Data and Information Science Society
• /
• 제25권5호
• /
• pp.1151-1160
• /
• 2014

In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.

• 한국생물정보학회:학술대회논문집
• /
• 한국생물정보시스템생물학회 2006년도 Principles and Practice of Microarray for Biomedical Researchers
• /
• pp.98-107
• /
• 2006

Chromosomal copy number changes (aneuploidies) are common in human populations. The extra chromosome can affect gene expression by whole-genome level. By gene expression microarray analysis, we want to find aberrant gene expression due to aneuploidies in Klinefelter (+X) and Down syndrome (+21). We have analyzed the inactivation status of X-linked genes in Klinefelter Syndrome (KS) by using X-linked cDNA microarray and cSNP analysis. We analyzed the expression of 190 X-linked genes by cDNA microarray from the lymphocytes of five KS patients and five females (XX) with normal males (XY) controls. cDNA microarray experiments and cSNP analysis showed the differentially expressed genes were similar between KS and XX cases. To analyze the differential gene expressions in Down Syndrome (DS), Amniotic Fluid (AF)cells were collected from 12 pregnancies at $16{\sim}18$ weeks of gestation in DS (n=6) and normal (n=6) subjects. We also analysis AF cells for a DNA microarray system and compared the chip data with two dimensional protein gel analysis of amniotic fluid. Our data may provide the basis for a more systematic identification of biological markers of fetal DS, thus leading to an improved understanding of pathogenesis for fetal DS.

• Genomics & Informatics
• /
• 제9권1호
• /
• pp.19-27
• /
• 2011

Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/.

• Genomics & Informatics
• /
• 제4권3호
• /
• pp.110-117
• /
• 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.

• Asian Pacific Journal of Cancer Prevention
• /
• 제17권1호
• /
• pp.95-100
• /
• 2016

Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high-risk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

• Journal of Genetic Medicine
• /
• 제7권2호
• /
• pp.111-118
• /
• 2010

염색체 microarray 검사는 유전체 전체를 한번에 검색하여 초현미경적인 염색체 이상을 매우 정밀하고 정확하게 검출할 수 있다. 외국에서는 현재 자주 활용되는 임상 진단 검사로 자리잡았고, 염색체 검사 또는 표적 부위를 검출하는 FISH 검사나 PCR 기반의 분자유전학적 방법을 대체하고 있다. 최근 발표된 consensus 들은 염색체 microarray 검사를 비특이적인 다발성 기형, 발달지연 또는 정신지체, 자폐증상질환의 환자에서는 염색체 검사보다 먼저 시행할 수 있는 검사로 제안하였다. 염색체 microarray 검사는 핵형 분석에서 검출된 염색체 불균형을 검증하기 위해 염색체 검사에 보조적으로 활용할 수 있고, 염색체 이상에 대한 보다 정확하고 종합적인 분석이 가능하다. 그러나 염색체 microarray 검사는 균형재배열의 염색체 이상과 low-level 모자이시즘을 검출하기 어렵고, 임상적 중요성이 불명확한 CNV에 대한 해석과 검사비용이 고가라는 한계점이 있다. 이러한 이유로 인해 현재로서는 염색체 microarray 검사가 산전 진단 목적으로는 고식적인 염색체 검사를 대신할 수는 없다는 의견이다. 임상검사실에서 염색체 microarray 검사 시행 시, 유전학적 및 세포유전학적 지식과 경험이 결과 분석과 해석 과정에서 요구되며, 적절한 검증 과정 단계와 유전상담이 동반되어야 한다.

• Asian Pacific Journal of Cancer Prevention
• /
• 제17권5호
• /
• pp.2519-2525
• /
• 2016

There are several existing reports of microarray chip use for assessment of altered gene expression in different diseases. In fact, there have been over 1.5 million assays of this kind performed over the last twenty years, which have influenced clinical and translational research studies. The most commonly used DNA microarray platforms are Affymetrix GeneChip and Quality Control Software along with their GeneChip Probe Arrays. These chips are created using several quality controls to confirm the success of each assay, but their actual impact on gene expression profiles had not been previously analyzed until the appearance of several bioinformatics tools for this purpose. We here performed a data mining analysis, in this case specifically focused on ovarian cancer, as well as healthy ovarian tissue and ovarian cell lines, in order to confirm quality control results and associated variation in gene expression profiles. The microarray data used in our research were downloaded from ArrayExpress and Gene Expression Omnibus (GEO) and analyzed with Expression Console Software using RMA, MAS5 and Plier algorithms. The gene expression profiles were obtained using Partek Genomics Suite v6.6 and data were visualized using principal component analysis, heat map, and Venn diagrams. Microarray quality control analysis showed that roughly 40% of the microarray files were false negative, demonstrating over- and under-estimation of expressed genes. Additionally, we confirmed the results performing second analysis using independent samples. About 70% of the significant expressed genes were correlated in both analyses. These results demonstrate the importance of appropriate microarray processing to obtain a reliable gene expression profile.

• 한국생물정보학회:학술대회논문집
• /
• 한국생물정보시스템생물학회 2004년도 The 3rd Annual Conference for The Korean Society for Bioinformatics Association of Asian Societies for Bioinformatics 2004 Symposium
• /
• pp.107-116
• /
• 2004

The advent of microarray technologies gives an opportunity to moni tor the expression of ten thousands of genes, simultaneously. Such microarray data can be deteriorated by experimental errors and image artifacts, which generate non-negligible outliers that are estimated by 15% of typical microarray data. Thus, it is an important issue to detect and correct the se faulty probes prior to high-level data analysis such as classification or clustering. In this paper, we propose a systematic procedure for the detection of faulty probes and its proper correction in Genechip array based on multivariate statistical approaches. Principal component analysis (PCA), one of the most widely used multivariate statistical approaches, has been applied to construct a statistical correlation model with 20 pairs of probes for each gene. And, the faulty probes are identified by inspecting the squared prediction error (SPE) of each probe from the PCA model. Then, the outlying probes are reconstructed by the iterative optimization approach minimizing SPE. We used the public data presented from the gene chip project of human fibroblast cell. Through the application study, the proposed approach showed good performance for probe correction without removing faulty probes, which may be desirable in the viewpoint of the maximum use of data information.

• Communications for Statistical Applications and Methods
• /
• 제13권3호
• /
• pp.567-576
• /
• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.