• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.59-77
• /
• 2004

Since the introduction of DNA microarray, a revolutionary high through-put biological technology, a lot of papers have been published to deal with the analyses of the gene expression data from the microarray. In this paper we review most papers relevant to the cDNA microarray data, classify them in statistical methods' point of view, and present some statistical methods deserving consideration and future study.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.101-111
• /
• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• 한국데이터정보과학회:학술대회논문집
• /
• 2006.04a
• /
• pp.39-42
• /
• 2006

We propose a Bayesian model-based approach using a mixture of Dirichlet processes model with discrete wavelet transform, for curve clustering in the microarray data with time-course gene expressions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.10
• /
• pp.2365-2370
• /
• 2010

The method of cancer classification based on microarray could contribute to being accurate cancer classification by finding differently expressing gene pattern statistically according to a cancer type. Therefore, the process to select a closely related informative gene with a particular cancer classification to classify cancer using present microarray technology with effect is essential. In this paper, the system can detect marker genes to likely express the most differentially explaining the effects of cancer using ovarian cancer microarray data. And it compare and analyze a performance of classification of the proposed system with it of established microarray system using multi-perceptron neural network layer. Microarray data set including marker gene that are selected using ANOVA method represent the highest classification accuracy of 98.61%, which show that it improve classification performance than established microarray system.

• Genomics & Informatics
• /
• v.5 no.2
• /
• pp.83-87
• /
• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform microarray data.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2004.11a
• /
• pp.272-278
• /
• 2004

Recently, Pepe et al. (2003) employed the receiver operating characteristic (ROC) approach to rank candidate genes from a microarray experiment that can be used for the biomarker development with the ultimate purpose of the population screening of a cancer, In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. This design is referred to as a reference design or an indirect design. Ideally, this experiment produces n pairs of microarray data, where each pair consists of two sets of microarray data resulting from reference versus normal tissue and reference versus tumor tissue hybridizations. However, for certain individuals either normal tissue or tumor tissue is not large enough for the experimenter to extract enough RNA for conducting the microarray experiment, hence there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ 'normal only' and $n_3$ 'tumor only' data for the microarray experiment with n patients, where n=$n_1$+$n_2$+$n_3$. We refer to this data set as a mixed data set, as it contains a mix of fully observed and partially observed pair data. This mixed data set was actually observed in the microarray experiment based on human tissues, where human tissues were obtained during the surgical operations of cancer patients. Pepe et al. (2003) provide the rationale of using ROC approach based on two independent samples for ranking candidate gene instead of using t or Mann -Whitney statistics. We first modify ROC approach of ranking genes to a paired data set and further extend it to a mixed data set by taking a weighted average of two ROC values obtained by the paired data set and two independent data sets.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.967-976
• /
• 2011

The Box-Cox transformation is a well known family of power transformations that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. Normalization (studentization) of the regressors is a common practice in analyzing microarray data. Here, we implement Box-Cox transformation in normalizing regressors in microarray data. Pridictabilty of the model can be improved using data transformation compared to studentization.

• Communications for Statistical Applications and Methods
• /
• v.14 no.3
• /
• pp.687-698
• /
• 2007

A normal mixture model with which dependence between classes is incorporated is proposed in order to detect differentially expressed genes. Gene clustering approaches suffer from the high dimensional column of microarray expression data matrix which leads to the over-fit problem. Various methods are proposed to solve the problem. In this paper, use of simple averaging data within each class is proposed to overcome the various problems due to high dimensionality when the normal mixture model is fitted. Some experiments through simulated data set and real data set show its availability in actuality.

• Genomics & Informatics
• /
• v.9 no.1
• /
• 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/.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• 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.