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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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analysis of DNA microarray expression data

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BEST PRACTICE & RESEARCH CLINICAL HAEMATOLOGY 2009年第2期22卷 271-282页

作者： Simon, Richard NCI Biometr Res Branch Div Canc Treatment & Diag Bethesda MD 20892 USA

DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. microarray based studies require clear objectives for selecting cases and appropriate analysis methods. Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed. Recent literature reviews indicate that serious problems of analysis exist a substantial proportion of publications. This manuscript attempts to provide a non-technical summary of the key principles of statistical design and analysis for studies that utilize microarray expression profiling. Published by Elsevier Ltd.

关键词： bioinformatics biomarkers gene expression signatures microarray data analysis

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Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data

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BMC BIOINFORMATICS 2005年第1期6卷 1-9页

作者： Jain, N Cho, HJ O'Connell, M Lee, JK Univ Virginia Sch Med Dept Hlth Evaluat Sci Div Biostat & Epidemiol Charlottesville VA 22908 USA Insightful Corp Durham NC 27713 USA

Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustment methods for multiple comparisons such as family-wise error rate (FWER) have been found to be too conservative in analyzing large-screening microarray data, and the False Discovery Rate (FDR), the expected proportion of false positives among all positives, has been recently suggested as an alternative for controlling false positives. Several statistical approaches have been used to estimate and control FDR, but these may not provide reliable FDR estimation when applied to microarray data sets with a small number of replicates. Results: We propose a rank-invariant resampling (RIR) based approach to FDR evaluation. Our proposed method generates a biologically relevant null distribution, which maintains similar variability to observed microarray data. We compare the performance of our RIR-based FDR estimation with that of four other popular methods. Our approach outperforms the other methods both in simulated and real microarray data. Conclusion: We found that the SAM's random shuffling and SPLOSH approaches were liberal and the other two theoretical methods were too conservative while our RIR approach provided more accurate FDR estimation than the other approaches.

关键词： False Discovery Rate microarray data Null Distribution microarray data analysis Null data

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Tilescope: online analysis pipeline for high-density tiling microarray data

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GENOME BIOLOGY 2007年第5期8卷 R81-R81页

作者： Zhang, Zhengdong D. Rozowsky, Joel Lam, Hugo Y. K. Du, Jiang Snyder, Michael Gerstein, Mark Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Yale Univ Interdept Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Yale Univ Dept Mol Cellular & Dev Biol New Haven CT 06520 USA

We developed Tilescope, a fully integrated data processing pipeline for analyzing high-density tiling-array data (http://***). In a completely automated fashion, Tilescope will normalize signals between channels and across arrays, combine replicate experiments, score each array element, and identify genomic features. The program is designed with a modular, three-tiered architecture, facilitating parallelism, and a graphic user-friendly interface, presenting results in an organized web page, downloadable for further analysis.

关键词： high-density tiling microarray high-density oligonucleotide microarray microarray data analysis

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Block principal component analysis with application to gene microarray data classification

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STATISTICS IN MEDICINE 2002年第22期21卷 3465-3474页

作者： Liu, AY Zhang, Y Gehan, E Clarke, R Georgetown Univ Med Ctr Lombardi Canc Ctr S112 Biostat Unit Washington DC 20007 USA Georgetown Univ Med Ctr Dept Oncol Lambardi Canc Ctr Washington DC 20007 USA

We propose a block principal component analysis method for extracting information from a database with a large number of variables and a relatively small number of subjects, such as a microarray gene expression database. This new procedure has the advantage of computational simplicity, and theory and numerical results demonstrate it to be as efficient as the ordinary principal component analysis when used for dimension reduction, variable selection and data visualization and classification. The method is illustrated with the well-known National Cancer Institute database of 60 human cancer cell lines data (NC160) of gene microarray expressions, in the context of classification of cancer cell lines. Copyright (C) 2002 John Wiley Sons, Ltd.

关键词： principal component analysis grouping of variables similarity gene expression microarray data analysis

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A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data

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JOURNAL OF THEORETICAL BIOLOGY 2008年第2期251卷 264-274页

作者： Zhao, Hongya Liew, Alan Wee-Chung Xie, Xudong Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： microarray data analysis gene expression profiles biclustering the Hough transform

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Geometric Biclustering analysis of DNA microarray data based on Hypergraph Partitioning

Geometric Biclustering Analysis of DNA Microarray Data based...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Wang, Doris Z. Yan, Hong City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781424483044

Biclustering can perform simultaneous pattern classification in both row and column directions in a data matrix and is useful for DNA microarray data analysis. In this paper, a new biclustering method is introduced based on a geometrical method of identifying bicluster patterns. The Hough transform in column-pair space is used to find sub-biclusters and a hypergraph model is used to merge the sub-biclusters into larger ones. The hypergraph based geometric biclustering (HGBC) algorithm proposed here reduces the computing time and improves the classification accuracy considerably compared with exiting biclustering methods. Experiments on both simulated and real microarray data demonstrate that our method can identify biclusters with different noise levels and overlapped degrees.

关键词： microarray data analysis Biclustering Hough Transform Hypergraph partition

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Pattern Browsing and Query Adjustment for the Exploratory analysis and Cooperative Visualisation of microarray Time-Course data

Pattern Browsing and Query Adjustment for the Exploratory An...

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7th International Conference on Cooperative Design, Visualization, and Engineering

作者： Craig, Paul Cannon, Alan Kennedy, Jessie Kukla, Robert Edinburgh Napier Univ Ctr Informat & Software Syst Edinburgh EH10 5DT Midlothian Scotland

ISBN: (纸本)9783642160653

This paper presents work to support collaborative visualisation and data analysis in the microarray time-series explorer (MaTSE) software. We introduce a novel visualisation component called the 'pattern browser' which is used to support the annotation and adjustment of user queries. This includes an explanation of why this component is required and how it can be used with our online pattern repository by biologists collaborating in the analysis of a microarray time-course data set. To conclude we suggest which other types of collaborative visualisation would benefit from the introduction of a component with comparable functionality.

关键词： Cooperative Visualisation Combined Multiple Views Bioinformatics microarray data analysis

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analysis of Imputation Algorithms for microarray Gene Expression data 1

Analysis of Imputation Algorithms for Microarray Gene Expres...

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International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Shashirekha, H. L. Wani, Agaz Hussain Mangalore Univ Dept Comp Sci Mangalore 574199 India

ISBN: (纸本)9781467392235

microarray technology makes it possible to measure expression level of thousands of genes simultaneously in an efficient and inexpensive manner. However, due to various complexities in processing microarrays, expression information of various genes may be missing due to unreliable measurements. The occurrence of missing values in gene expression data can adversely affect downstream analyses such as clustering, dimensionality reduction etc. Different algorithms have been developed to estimate the missing values in different data sets and none of these algorithm works well with all the data sets. In this work, we explore the possible application of Mutual Nearest Neighbor (MNN) algorithm to impute the missing values, which shows comparable results with other well know imputation algorithms. We also have explored five different methods for missing value imputation namely Row Average Imputation, Mean Imputation, Median Imputation, k-Nearest Neighbor Imputation and combination of kNN based feature selection (kNNFS) and kNN -based imputation. The experiments are carried out on very high dimensional gene expression data such as Notterman Carcinoma and Notterman Adenocarcinoma data and the results are illustrated.

关键词： Imputation microarray data analysis Gene Expression data Missing Value Estimation

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Goal Driven analysis of cDNA microarray data

Goal Driven Analysis of cDNA Microarray Data

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IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Pan, Youlian Zou, Jitao Huang, Yi Liu, Ziying Phan, Sieu Famili, Fazel A. Natl Res Council Canada Inst Informat Technol Knowledge Discovery Grp 1200 Montreal Rd Ottawa ON K1A 0R6 Canada Natl Res Council Canada Inst Plant Biotechnol Saskatoon SK S7N 0W9 Canada

ISBN: (纸本)9781424427567

microarray technology has been used extensively for high throughput gene expression studies. Many bioinformatics tools are available for analysis of microarray data. In the data mining process, it is important to be goal oriented so that a set of proper tools can be assembled for the targeted knowledge discovery process. In this paper, we tackle this issue by using a microarray dataset from Brassica endosperm together with EST data to validate our process. We were most interested in which genes are highly expressed in Brassica endosperm and their variations and functions over various stages in embryo development. We also performed gene characterization based on gene ontology analysis. Our results indicate that designing a specific data mining workflow that considers both the log ratio and signal intensity enhances knowledge discovery process. Through this approach, we were able to rind the regulatory relationship between two most important transcription factors, LEC1 and WRI1 in the endosperm of Brassica napus.

关键词： microarray data analysis gene expression differentially expressed genes transcriptional regulation

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