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Empirical Bayes analysis of variance component models for microarray data

JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2006年第2期11卷 197-209页

作者： Feng, S. Wolfinger, R. D. Chu, T. M. Gibson, G. C. McGraw, L. A. Duke Univ Dept Biostat & Bioinformat Durham NC 27705 USA SAS Inst Inc Cary NC 27513 USA N Carolina State Univ Dept Genet Raleigh NC 27695 USA Cornell Univ Dept Genet & Dev Ithaca NY 14853 USA

A gene-by-gene mixed model analysis is a useful statistical method for assessing significance for microarray gene differential expression. While a large amount of data on thousands of genes are collected in a microarray experiment, the sample size for each gene is usually small, which could limit the statistical power of this analysis. In this report, we introduce an empirical Bayes (EB) approach for general variance component models applied to microarray data. Within a linear mixed model framework, the restricted maximum likelihood (REML) estimates of variance components of each gene are adjusted by integrating information on variance components estimated from all genes. The approach starts with a series of single-gene analyses. The estimated variance components from each gene are transformed to the "ANOVA components." This transformation makes it possible to independently estimate the marginal distribution of each "ANOVA component." The modes of the posterior distributions are estimated and inversely transformed to compute the posterior estimates of the variance components. The EB statistic is constructed by replacing the REML variance estimates with the EB variance estimates in the usual t statistic. The EB approach is illustrated with a real data example which compares the effects of five different genotypes of male flies on post-mating gene expression in female flies. In a simulation study, the ROC curves are applied to compare the EB statistic and two other statistics. The EB statistic was found to be the most powerful of the three. Though the null distribution of the EB statistic is unknown, a t distribution may be used to provide conservative control of the false positive rate.

关键词： microarray data analysis ROC curves shrinkage estimators

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Differential prioritization in feature selection and classifier aggregation for multiclass microarray datasets

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data MINING AND KNOWLEDGE DISCOVERY 2007年第3期14卷 329-366页

作者： Ooi, Chia Huey Chetty, Madhu Teng, Shyh Wei Monash Univ Gippsland Sch Informat Technol Churchill Vic Australia

The high dimensionality of microarray datasets endows the task of multiclass tissue classification with various difficulties-the main challenge being the selection of features deemed relevant and non-redundant to form the predictor set for classifier training. The necessity of varying the emphases on relevance and redundancy, through the use of the degree of differential prioritization (DDP) during the search for the predictor set is also of no small importance. Furthermore, there are several types of decomposition technique for the feature selection (FS) problem-all-classes-at-once, one-vs.-all (OVA) or pairwise (PW). Also, in multiclass problems, there is the need to consider the type of classifier aggregation used-whether non-aggregated (a single machine), or aggregated (OVA or PW). From here, first we propose a systematic approach to combining the distinct problems of FS and classification. Then, using eight well-known multiclass microarray datasets, we empirically demonstrate the effectiveness of the DDP in various combinations of FS decomposition types and classifier aggregation methods. Aided by the variable DDP, feature selection leads to classification performance which is better than that of rank-based or equal-priorities scoring methods and accuracies higher than previously reported for benchmark datasets with large number of classes. Finally, based on several criteria, we make general recommendations on the optimal choice of the combination of FS decomposition type and classifier aggregation method for multiclass microarray datasets.

关键词： tissue classification microarray data analysis multiclass classification feature selection classifier aggregation

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Multimedia signal processing and systems in healthcare and life sciences

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IEEE MULTIMEDIA 2007年第4期14卷 20-21页

作者： Dimitrova, Nevenka Philips Research

The guest editor discusses the articles contained in this special issue and explains how this special issue reflects trends, challenges, and opportunities in the field at large.

关键词： Signal Processing IT Healthcare Life Sciences MRI CMT Tactile Navigation Haptics Physiological Monitoring Imaging Diagnostics Genomics microarray data analysis Multimedia Modeling Computer Navigated Surgery Optical Tracking Knowledge Extraction And Digitally Augmented Visuals

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ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data

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BIOSYSTEMS 2007年第2期90卷 516-528页

作者： Huang, Hui-Ling Chang, Fang-Lin Jin Wen Inst Technol Dept Informat Management Taipei Taiwan Tri Serv Gen Hosp Dept Anesthesiol Taipei Taiwan

An optimal design of support vector machine (SVM)-based classifiers for prediction aims to optimize the combination of feature selection, parameter setting of SVM, and cross-validation methods. However, SVMs do not offer the mechanism of automatic internal relevant feature detection. The appropriate setting of their control parameters is often treated as another independent problem. This paper proposes an evolutionary approach to designing an SVM-based classifier (named ESVM) by simultaneous optimization of automatic feature selection and parameter tuning using an intelligent genetic algorithm, combined with k-fold cross-validation regarded as an estimator of generalization ability. To illustrate and evaluate the efficiency of ESVM, a typical application to microarray classification using 11 multi-class datasets is adopted. By considering model uncertainty, a frequency-based technique by voting on multiple sets of potentially informative features is used to identify the most effective subset of genes. It is shown that ESVM can obtain a high accuracy of 96.88% with a small number 10.0 of selected genes using 10-fold cross-validation for the 11 datasets averagely. The merits of ESVM are three-fold: (1) automatic feature selection and parameter setting embedded into ESVM can advance prediction abilities, compared to traditional SVMs;(2) ESVM can serve not only as an accurate classifier but also as an adaptive feature extractor;(3) ESVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of ESVM for bioinformatics problems. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

关键词： classification gene expression intelligent genetic algorithm microarray data analysis support vector machine

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data-dependent kernel machines for microarray data classification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第4期4卷 583-595页

作者： Xiong, Huilin Zhang, Ya Chen, Xue-Wen Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

One important application of gene expression analysis is to classify tissue samples according to their gene expression levels. Gene expression data are typically characterized by high dimensionality and small sample size, which makes the classification task quite challenging. In this paper, we present a data-dependent kernel for microarray data classification. This kernel function is engineered so that the class separability of the training data is maximized. A bootstrapping-based resampling scheme is introduced to reduce the possible training bias. The effectiveness of this adaptive kernel for microarray data classification is illustrated with a k-Nearest Neighbor (KNN) classifier. Our experimental study shows that the data-dependent kernel leads to a significant improvement in the accuracy of KNN classifiers. Furthermore, this kernel-based KNN scheme has been demonstrated to be competitive to, if not better than, more sophisticated classifiers such as Support Vector Machines (SVMs) and the Uncorrelated Linear Discriminant analysis (ULDA) for classifying gene expression data.

关键词： microarray data analysis cancer classification kernel machines kernel optimization bootstrapping resampling

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Framework for identifying common aberrations in DNA copy number data

Framework for identifying common aberrations in DNA copy num...

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11th Annual International Conference on Research in Computational Molecular Biology

作者： Ben-Dor, Amir Lipson, Doron Tsalenko, Anya Reimers, Mark Baumbusch, Lars O. Barrett, Michael T. Weinstein, John N. Borresen-Dale, Anne-Lise Yakhini, Zohar Agilent Labs Santa Clara CA 95051 USA Technion Dept Comp Sci Haifa Israel NCI Bethesda MD USA Inst Canc Res Dept Genet Rikshosp Radiumhosp Med Ctr Bethesda MD USA Translat Genom Res Inst Phoenix AZ USA

ISBN: (纸本)9783540716808

High-resolution array comparative genomic hybridization (aCGH) provides exon-level mapping of DNA aberrations in cells or tissues. Such aberrations are central to carcinogenesis and, in many cases, central to targeted therapy of the cancers. Some of the aberrations are sporadic, one-of-a-kind changes in particular tumor samples;others occur frequently and reflect common themes in cancer biology that have interpretable, causal ramifications. Hence, the difficult task of identifying and mapping common, overlapping genomic aberrations (including amplifications and deletions) across a sample set is an important one-, it can provide insight for the discovery of oncogenes, tumor suppressors, and the mechanisms by which they drive cancer development. In this paper we present an efficient computational framework for identification and statistical characterization of genomic aberrations that are common to multiple cancer samples in a CGH data set. We present and compare three different algorithmic approaches within the context of that framework. Finally, we apply our methods to two datasets - a collection of 20 breast cancer samples and a panel of 60 diverse human tumor cell lines (the NCI-60). Those analyses identified both known and novel common aberrations containing cancer-related genes. The potential impact of the analytical methods is well demonstrated by new insights into the patterns of deletion of CDKN2A (p16), a tumor suppressor gene crucial for the genesis of many types of cancer.

关键词： CGH cancer microarray data analysis common aberrations breast cancer NCI-60

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Comparative analysis of spatial patterns of gene expression in Drosophila melanogaster imaginal discs

Comparative analysis of spatial patterns of gene expression ...

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11th Annual International Conference on Research in Computational Molecular Biology

作者： Harmon, Cyrus L. Ahammad, Parvez Hammonds, Ann Weiszmann, Richard Celniker, Susan E. Sastry, S. Shankar Rubin, Gerald M. Univ Calif Berkeley Dept Mol & Cell Biol Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Dept Genome Sci Berkeley CA 94720 USA

ISBN: (纸本)9783540716808

Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.

关键词： genomic imaging gene expression analysis clustering microarray data analysis imaginal discs

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From genes to functional classes in the study of biological systems

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BMC BIOINFORMATICS 2007年第1期8卷 114-114页

作者： Al-Shahrour, Fatima Arbiza, Leonardo Dopazo, Hernan Huerta-Cepas, Jaime Minguez, Pablo Montaner, David Dopazo, Joaquin Bioinformat Dept E-46013 Valencia Spain CIPF INB Funct Genom Node E-46013 Valencia Spain

Background: With the popularisation of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed. Results: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics. Conclusion: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://***.

关键词： Gene Ontology Functional Classis KEGG Pathway microarray data analysis Functional Term

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analysis of expression data: an overview

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Current protocols in bioinformatics 2007年第1期Chapter 7卷 Unit 7.1页

作者： Anoop Grewal Peter Lambert Jordan Stockton NextBio Cupertino California USA.

After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. data analysis from microarray experiments generally involves two parts: acquiring and nor... 详细信息

关键词： microarray data analysis genomic expression transcriptomics

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Significance of gene ranking for classification of microarray samples

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2006年第3期3卷 312-320页

作者： Zhang, Chaolin Lu, Xuesong Zhang, Xuegong SUNY Stony Brook Cold Spring Harbor Lab Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA Tsinghua Univ MOE Key Lab Bioinformat Dept Automat Beijing 100084 Peoples R China

Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods.

关键词： significance of gene ranking gene selection classification microarray data analysis

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