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Empirical Bayes analysis of unreplicated microarray data

COMPUTATIONAL STATISTICS 2009年第3期24卷 393-408页

作者： Cho, HyungJun Kang, Jaewoo Lee, Jae K. Korea Univ Dept Stat Seoul South Korea Korea Univ Dept Biostat Seoul South Korea Korea Univ Dept Comp Sci & Engn Seoul South Korea Univ Virginia Dept Publ Hlth Sci Charlottesville VA USA

Because of the high costs of microarray experiments and the availability of only limited biological materials, microarray experiments are often performed with a small number of replicates. Investigators, therefore, often have to perform their experiments with low replication or without replication. However, the heterogeneous error variability observed in microarray experiments increases the difficulty in analyzing microarray data without replication. No current analysis techniques are practically applicable to such microarray data analysis. We here introduce a statistical method, the so-called unreplicated heterogeneous error model (UHEM) for the microarray data analysis without replication. This method is possible by utilizing many adjacent-intensity genes for estimating local error variance after nonparametric elimination of differentially expressed genes between different biological conditions. We compared the performance of UHEM with three empirical Bayes prior specification methods: between-condition local pooled error, pseudo standard error, or adaptive standard error-based HEM. We found that our unreplicated HEM method is effective for the microarray data analysis when replication of an array experiment is impractical or prohibited.

关键词： microarray data Empirical Bayes Markov chain Monte Carlo No replication

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A Functional and Phylogenetic Comparison of Quorum Sensing Related Genes in Brucella melitensis 16M

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JOURNAL OF MICROBIOLOGY 2014年第8期52卷 709-715页

作者： Leticia Brambila-Tapia, Aniel Jessica Perez-Rueda, Ernesto Univ Nacl Autonoma Mexico Inst Biotecnol Dept Ingn Celular & Biocatalisis Cuernavaca 62191 Morelos Mexico Univ Nacl Autonoma Mexico Fac Ciencias Unidad Multidisciplinaria Docencia & Invest Sisal Yucatan Mexico

A quorum-sensing (QS) system is involved in Brucella melitensis survival inside the host cell. Two transcriptional regulators identified in B. melitensis, BlxR and VjbR, regulate the expression of virB, an operon required for bacterial intracellular persistence. In this work, 628 genes affected by VjbR and 124 by BlxR were analyzed to gain insights into their functional and taxonomical distributions among the Bacteria and Archaea cellular domains. In this regard, the Cluster of Orthologous Groups (COG) genes and orthologous genes in 789 nonredundant bacterial and archaeal genomes were obtained and compared against a group of randomly selected genes. From these analyses, we found 71 coaffected genes between VjbR and BlxR. In the COG comparison, VjbR activated genes associated with intracellular trafficking, secretion and vesicular transport and defense mechanisms, while BlxR affected genes related to energy production and conversion (with an equal effect) and translation, ribosomal structure and biogenesis, posttranslational modifications and carbohydrate and amino acid metabolism (with a negative effect). When the taxonomical distribution of orthologous genes was evaluated, the VjbR- and BlxR-related genes presented more orthologous genes in Crenarchaeota (Archaea), Firmicutes, and Tenericutes and fewer genes in Proteobacteria than expected by chance. These findings suggest that QS system exert a fine-tuning modulation of gene expression, by which VjbR activates genes related to infection persistence and defense, while BlxR represses general bacterial metabolism for intracellular adaptations. Finally, these affected genes present a degree of presence among Bacteria and Archaea genomes that is different from that expected by chance.

关键词： Brucella melitensis quorum sensing microarray data taxonomical distribution orthologous genomics

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An Iterative GASVM-Based Method: Gene Selection and Classification of microarray data

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Infomat Syst Dept Software Engn Skudai 81310 Malaysia

ISBN: (纸本)9783642024801

microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). In this study, we propose an iterative method based on hybrid genetic algorithms to select a near-optimal (smaller) subset of informative genes in classification of the microarray data. The experimental results show that our proposed method is capable in selecting the near-optimal subset to obtain better classification accuracies than other related previous works as well as four methods experimented in this work. Additionally, a list of informative genes in the best gene subsets is also presented for biological usage.

关键词： Gene selection genetic algorithm iterative method hybrid approach microarray data

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An Evolutionary Approach for Sample-Based Clustering on microarray data

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Glez-Pena, Daniel Diaz, Fernando Mendez, Jose R. Corchado, Juan M. Fdez-Riverola, Florentino Univ Vigo ESEI Escuela Super Ingn Informat Edificio PolitecnCampus Univ As Lagoas S-N Orense 32004 Spain Univ Valladolid Escuela Univ Informat Dept Informat E-40005 Segovia Spain Univ Salamanca Dept Informat Automat Salamanca Spain

ISBN: (纸本)9783642024801

Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coherent from a genetic point of view. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Noise genes are identified and removed from the fuzzy pattern based on their probability of appearance. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema. Experimental results over publicly available microarray data show the effectiveness of the proposed method.

关键词： simulated annealing sample-based clustering discriminant fuzzy pattern microarray data

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Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

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BMC Medical Genomics 2009年第1期2卷 1-19页

作者： Yeh, Hsiang-Yuan Cheng, Shih-Wu Lin, Yu-Chun Yeh, Cheng-Yu Lin, Shih-Fang Soo, Von-Wun Department of Computer Science National Tsing Hua University HsinChu 300 Taiwan Institute of Information Systems and Applications National Tsing Hua University HsinChu 300 Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung 811 Taiwan

Background. Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford microarray database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. Conclusions. We provide a computat

关键词： microarray data Cluster Coefficient microarray dataset Dependent Gene Network Motif

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INTEGRATIVE MODEL-BASED CLUSTERING OF microarray METHYLATION AND EXPRESSION data

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ANNALS OF APPLIED STATISTICS 2012年第3期6卷 1327-1347页

作者： Kormaksson, Matthias Booth, James G. Figueroa, Maria E. Melnick, Ari Cornell Univ Dept Stat Sci Ithaca NY 14853 USA Weill Cornell Med Coll Dept Med Hematol Oncol Div New York NY 10065 USA

In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.

关键词： Integrative model-based clustering microarray data mixture models EM algorithm methylation expression AML

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Accelerating Incremental Wrapper based Gene Selection with K-Nearest-Neighbor

Accelerating Incremental Wrapper based Gene Selection with K...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Aiguo Wang Ning An Guilin Chen Lian Li Gil Alterovitz The Gerontechnology Lab School of Computer and Information Hefei University of Technology School of Computer and Information Engineering Chuzhou University Center for Biomedical Informatics Harvard Medical School

ISBN: (纸本)9781479956708

Wrapper based gene selection methods tend to obtain better classification accuracy than filter methods, while it is much more time consuming. Accelerating this process without degrading the high accuracy is of great value for researchers to better analyze gene expression profiles. In this paper, we explore to reduce the time complexity of wrapper based gene selection method with K-Nearest-Neighbor (KNN) classifier embedded. Instead of taking KNN as a black box, we incrementally construct and maintain a classifier distance matrix to speed up the gene selection process. Experiments on six publicly available microarrays were first conducted to show the effectiveness of incremental wrapper based gene selection method with KNN. Then, to demonstrate the performance gain in time cost reduction, we analyzed the time complexity and experimentally evaluated it. Both theoretical analysis and experimental results prove that the proposed approach greatly accelerates the gene selection process without degrading the classification accuracy.

关键词： microarray data Gene selection K-nearest-neighbor Wrapper Filter

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microarray-based prediction of Parkinson's disease using clinical data as additional response variables

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STATISTICS IN MEDICINE 2012年第30期31卷 4369-4381页

作者： Karlsson, Magdalena Kauczynska Lonneborg, Anders Saebo, Solve DiaGenic ASA N-0663 Oslo Norway Norwegian Univ Life Sci Dept Chem Biotechnol & Food Sci N-1432 As Norway

Recent discoveries and developments in the field of genomics have led to the commercialization of novel diagnostic devices for studying disease or estimating therapeutic outcomes in individual patients. With this emerging field, the emphasis is shifting to integration of clinical research into product development. data acquisition is primary in the initial exploratory phase of product development, and during the process of sample collection and data generation in clinical microarray studies, great amounts of additional information, such as demographic, clinical, and study design variables associated with the data, are often accumulated and made available. Including additional information in classification has been addressed in many different ways. However, in previous studies, the additional information have consistently been treated as extra predictors, which can be a problem for future prediction if such information are not available or collectable for the new samples. We instead propose to adopt a method called canonical partial least squares, which for our purpose, only uses the additional information at the model building stage to stabilize the construction of a classifier for disease status from microarray data. The canonical partial least squares method is compared with regular partial least squares for the classification of Parkinson's disease from gene expression in peripheral blood samples and also through computer simulations. The present study showed that including clinical data in the model building produces simpler and more stable models for prediction of Parkinson's disease from gene expression data. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： microarray data PLS regression additional responses Parkinson's disease prediction

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An ensemble of SVM classifiers based on gene pairs

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第6期43卷 729-737页

作者： Tong, Muchenxuan Liu, Kun-Hong Xu, Chungui Ju, Wenbin Xiamen Univ Dept Elect Engn Xiamen 361005 Fujian Peoples R China Xiamen Univ Sch Software Xiamen 361005 Fujian Peoples R China Peking Univ Peoples Hosp Dept Trauma Orthopaed Beijing 100871 Peoples R China Dalian Med Univ Affiliated Hosp 2 Dept Urol Dalian Peoples R China

In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： microarray data Top scoring pair (TSP) Ensemble learning Genetic algorithm (GA) Support vector machine (SVM)

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Gene-Expression-Based Cancer Subtypes Prediction Through Feature Selection and Transductive SVM

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第4期60卷 1111-1117页

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Chakraborty, Debasis Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Kalyani Univ Dept Comp Sci & Engn Kalyani 741235 W Bengal India Murshidabad Coll Engn & Technol Dept Elect & Commun Engn Cossimbazar 742102 Rajasthan India

With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type. Nevertheless, small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. A novel approach to combine feature (gene) selection and transductive support vector machine (TSVM) is proposed. We demonstrated that 1) potential gene markers could be identified and 2) TSVMs improved prediction accuracy as compared to the standard inductive SVMs (ISVMs). A forward greedy search algorithm based on consistency and a statistic called signal-to-noise ratio were employed to obtain the potential gene markers. The selected genes of the microarray data were then exploited to design the TSVM. Experimental results confirm the effectiveness of the proposed technique compared to the ISVM and low-density separationmethod in the area of semisupervised cancer classification as well as gene-marker identification.

关键词： Low-density separation (LDS) microarray data semisupervised classification support vector machines (SVM) transductive learning

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