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MAID : An effect size based model for microarray data integration across laboratories and platforms

BMC BIOINFORMATICS 2008年第1期9卷 305-305页

作者： Borozan, Ivan Chen, Limin Paeper, Bryan Heathcote, Jenny E. Edwards, Aled M. Katze, Michael Zhang, Zhaolei McGilvray, Ian D. Univ Toronto Banting & Best Dept Med Res Toronto ON M5G 1L6 Canada Univ Toronto Dept Med Genet & Microbiol Toronto ON M5S 1A8 Canada Univ Toronto Dept Med Biophys Toronto ON M5G 2M9 Canada Toronto Gen Res Inst Toronto ON M5G 2M9 Canada Univ Toronto Fac Med Dept Surg Toronto ON M5S 1A8 Canada Toronto Western Hosp Toronto ON M5T 2S8 Canada Univ Washington Dept Microbiol Seattle WA 98195 USA

Background: Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions. Results: Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model ( MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to th

关键词： Effect Size Model Indirect Design Entrez Gene Identifier Maid Model microarray data integration

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Direct integration of microarrays for selecting informative genes and phenotype classification

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INFORMATION SCIENCES 2008年第1期178卷 88-105页

作者： Yoon, Youngrai Lee, Jongchan Park, Sanghyun Bien, Sangjay Chung, Hyun Cheol Rha, Sun Young Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Yonsei Univ Coll Med Canc Metastasis Res Ctr Dept Internal Med Seoul 120749 South Korea Gachon Univ Med & Sci Dept Informat Technol Inchon South Korea

The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways;we increased the number of samples by integrating independently generated microarrays that had been designed with the same biological objectives, and reduced the number of genes involved in the classification by selecting a small set of informative genes. We were able to maximally use the abundant microarray data that is being stockpiled by thousands of different research groups while improving classification accuracy. Our goal is to implement a feature (gene) selection method that can be applicable to integrated microarrays as well as to build a highly accurate classifier that permits straightforward biological interpretation. In this paper, we propose a two-stage approach. Firstly, we performed a direct integration of individual microarrays by transforming an expression value into a rank value within a sample and identified informative genes by calculating the number of swaps to reach a perfectly split sequence. Secondly, we built a classifier which is a parameter-free ensemble method using only the pre-selected informative genes. By using our classifier that was derived from large, integrated microarray sample datasets, we achieved high accuracy, sensitivity, and specificity in the classification of an independent test dataset. (C) 2007 Elsevier Inc. All rights reserved.

关键词： data mining microarray data analysis microarray data integration microarray data classification Informative gene selection

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A distributed sparse logistic regression with L1/2 regularization for microarray biomarker discovery in cancer classification

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SOFT COMPUTING 2023年第5期27卷 2537-2552页

作者： Ai, Ning Yang, Ziyi Yuan, Haoliang Ouyang, Dong Miao, Rui Ji, Yuhan Liang, Yong Macau Univ Sci & Technol Sch Comp Sci & Engn Fac Innovat Engn Ave Wai Long Taipa Macao Peoples R China Tencent Quantum Lab Shenzhen 518057 Guangdong Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518005 Peoples R China

microarray is a high-throughput sequencing technology, which can be used to classify cancer types and select the highly relevant cancer biomarkers (i.e., genes). To improve the availability of ever-increasing microarray data, data-integrative analysis becomes a hot research direction. However, the complexity of gene expression data still brings many challenges to the data integration methods: (1) the relevant biomarker selection in multiple high-dimensional datasets;(2) the batch effects between datasets;(3) the high noise in features and samples;(4) the large-scale data analysis with high computational cost. To overcome these challenges, we propose a novel Distribute-based Biological data-Integrative Analysis model-DBIA. DBIA is based on the L-1/2 regularized logistic regression (L-1/2 LR) model and the alternating direction multiplication algorithm (ADMM) for data integration. The regularization model is an effective method for selecting latent cancer-relevant genes and improving the accuracy of cancer classification. Moreover, we adopt the L-1/2 LR model to reduce the noise and dimensionality of the data. ADMM is employed to reduce the batch effects between datasets, analyze multiple datasets in parallel, and save the computational cost of large-scale data analysis. Experimental results on the simulation and real-world datasets demonstrate that DBIA achieves the good prediction performance with a shorter time, lower hardware requirements, and strong robustness. The genes selected by DBIA have a certain biological significance.

关键词： microarray data integration L-1/2 regularized logistic regression ADMM algorithm Cancer classification Gene selection

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Automatic generation of causal networks linking growth factor stimuli to functional cell state changes

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FEBS JOURNAL 2012年第18期279卷 3462-3474页

作者： Guziolowski, Carito Kittas, Aristotelis Dittmann, Florian Grabe, Niels Heidelberg Univ BIOQUANT Hamamatsu Tissue Imaging & Anal TIGA Ctr D-69120 Heidelberg Germany Univ Heidelberg Hosp Inst Med Biometry & Informat Heidelberg Germany

Despite the increasing number of growth factor-related signalling networks, their lack of logical and causal connection to factual changes in cell states frequently impairs the functional interpretation of microarray data. We present a novel method enabling the automatic inference of causal multilayer networks from such data, allowing the functional interpretation of growth factor stimulation experiments using pathway databases. Our environment of evaluation was hepatocyte growth factor-stimulated cell migration and proliferation in a keratinocyte-fibroblast co-culture. The network for this system was obtained by applying the steps: (a) automatic integration of the comprehensive set of all known cellular networks from the Pathway Interaction database into a master structure;(b) retrieval of an active-network from the master structure, where the network edges that connect nodes with an absent mRNA level were excluded;and (c) reduction of the active-network complexity to a causal subnetwork from a set of seed nodes specific for the microarray experiment. The seed nodes comprised the receptors stimulated in the experiment, the consequently differentially expressed genes, and the expected cell states. The resulting network shows how well-known players, in the context of hepatocyte growth factor stimulation, are mechanistically linked in a pathway triggering functional cell state changes. Using BIOQUALI, we checked and validated the consistency of the network with respect to microarray data by computational simulation. The network has properties that can be classified into different functional layers because it not only shows signal processing down to the transcriptional level, but also the modulation of the network structure by the preceeding stimulation. The software for generating computable objects from the Pathway Interaction database database, as well as the generated networks, are freely available at: http://***/supplements/***.

关键词： discrete reasoning HGF stimulation microarray data integration network visualization pathway interaction database

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GENESHIFT: A Nonparametric Approach for Integrating microarray Gene Expression data Based on the Inner Product as a Distance Measure between the Distributions of Genes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013年第2期10卷 383-392页

作者： Lazar, Cosmin Taminau, Jonatan Meganck, Stijn Steenhoff, David Coletta, Alain Solis, David Y. Weiss Molter, Colin Duque, Robin Bersini, Hugues Nowe, Ann Vrije Univ Brussel Dept Comp Sci Computat Modeling Lab B-1050 Brussels Belgium Univ Libre Bruxelles IRIDIA B-1050 Brussels Belgium

The potential of microarray gene expression (MAGE) data is only partially explored due to the limited number of samples in individual studies. This limitation can be surmounted by merging or integrating data sets originating from independent MAGE experiments, which are designed to study the same biological problem. However, this process is hindered by batch effects that are study-dependent and result in random data distortion;therefore numerical transformations are needed to render the integration of different data sets accurate and meaningful. Our contribution in this paper is two-fold. First we propose GENESHIFT, a new nonparametric batch effect removal method based on two key elements from statistics: empirical density estimation and the inner product as a distance measure between two probability density functions;second we introduce a new validation index of batch effect removal methods based on the observation that samples from two independent studies drawn from a same population should exhibit similar probability density functions. We evaluated and compared the GENESHIFT method with four other state-of-the-art methods for batch effect removal: Batch-mean centering, empirical Bayes or COMBAT, distance-weighted discrimination, and cross-platform normalization. Several validation indices providing complementary information about the efficiency of batch effect removal methods have been employed in our validation framework. The results show that none of the methods clearly outperforms the others. More than that, most of the methods used for comparison perform very well with respect to some validation indices while performing very poor with respect to others. GENESHIFT exhibits robust performances and its average rank is the highest among the average ranks of all methods used for comparison.

关键词： Batch effects microarray data integration distance measures between probability density functions inner product density estimation integrative analysis of gene expression microarrays nonparametric methods

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Extraction of informative genes from integrated microarray data

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17th International Symposium on Metodologies for Intelligent Systems

作者： Hong, Dongwan Lee, Jongkeun Hong, Sangkyoon Yoon, Jeehee Park, Sanghyun Hallym Univ Div Informat & Commun Engn Chunchon 200702 South Korea Yonsei Univ Shinchon Dong Seodaemun Dept Comp Sci Seoul 120749 South Korea

ISBN: (纸本)9783540681229

We have recently proposed a rank-based approach as a new microarray data integration method. The rank-based approach, which converts the expression value of each sample into a rank value within the sample, enables us to directly integrate samples generated by different laboratories and microarray technologies. In this study, we show that a non-parametric scoring method can be efficiently employed for the rankbased data, and informative genes can be effectively extracted from the integrated rank-based data. To verify the statistical significance of the scoring results from the rank-based data, we compared the distribution of the score statistics to a set of distributions obtained from the randomly column-permuted data. We also validate our methods with experimental study using publicly available prostate microarray data. We compared the informative genes extracted from each individual data to the informative genes extracted from the integrated data. The results show that we can extract important prostate marker genes by directly integrating inter-study microarray data, which are missed in either single analysis.

关键词： informative genes selection microarray data integration prostate cancer statistical significance verification

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Building a classifier for integrated microarray datasets through two-stage approach

Building a classifier for integrated microarray datasets thr...

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6th IEEE Syposium on BioInformatics and BioEngineering (BIBE 2006)

作者： Yoon, Youngmi Lee, Jongchan Park, Sanghyun Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

ISBN: (纸本)0769527272

Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases. However, the results of analyzing several microarray datasets which were independently carried out with the same biological objectives, could turn out to be different. One of the main reasons is attributable to the limited number of samples involved in one microarray experiment. In order to increase the classification accuracy, it is desirable to augment the sample size by integrating and maximizing the use of independently-conducted microarray datasets. In this paper, we propose a two-stage approach which firstly integrates individual microarray datasets to overcome the problem caused by limited number of samples, and identifies informative genes, secondly builds a classifier using only the informative genes. The classifier from large samples by integrating independent microarray datasets achieves high accuracy, sensitivity, and specificity on independent test sample dataset.

关键词： bioinformatics microarray data analysis microarray data integration microarray classification informative gene selection

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INTEGRATING microarray data BY CONSENSUS CLUSTERING

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International Journal on Artificial Intelligence Tools 2004年第4期13卷 863-880页

作者： VLADIMIR FILKOV STEVEN SKIENA Computer Science Dept. University of California One Shields Ave. Davis CA 95616 USA Computer Science Dept. SUNY Stony Brook Stony Brook NY 11794 USA

With the exploding volume of microarray experiments comes increasing interest in mining repositories of such data. Meaningfully combining results from varied experiments on an equal basis is a challenging task. Here we propose a general method for integrating heterogeneous data sets based on the consensus clustering formalism. Our method analyzes source-specific clusterings and identifies a consensus set-partition which is as close as possible to all of them. We develop a general criterion to assess the potential benefit of integrating multiple heterogeneous data sets, i.e. whether the integrated data is more informative than the individual data sets. We apply our methods on two popular sets of microarray data yielding gene classifications of potentially greater interest than could be derived from the analysis of each individual data set.

关键词： microarray data integration consensus clustering median partition heuristics

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