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Transcription network construction for large-scale microarray datasets using a high-performance computing approach

BMC GENOMICS 2008年第Sup1期9卷 S5-S5页

作者： Zhu, Mengxia (Michelle) Wu, Qishi So Illinois Univ Dept Comp Sci Carbondale IL 62901 USA Univ Memphis Dept Comp Sci Memphis TN 38152 USA

Background: The advance in high-throughput genomic technologies including microarrays has demonstrated the potential of generating a tremendous amount of gene expression data for the entire genome. Deciphering transcriptional networks that convey information on intracluster correlations and intercluster connections of genes is a crucial analysis task in the post-sequence era. Most of the existing analysis methods for genome-wide gene expression profiles consist of several steps that often require human involvement based on experiential knowledge that is generally difficult to acquire and formalize. Moreover, large-scale datasets typically incur prohibitively expensive computation overhead and thus result in a long experiment-analysis research cycle. Results: We propose a parallel computation-based random matrix theory approach to analyze the cross correlations of gene expression data in an entirely automatic and objective manner to eliminate the ambiguities and subjectivity inherent to human decisions. We apply the proposed approach to the publicly available human liver cancer data and yeast cycle data, and generate transcriptional networks that illustrate interacting functional modules. The experimental results conform accurately to those published in previous literatures. Conclusions: The correlations calculated from experimental measurements typically contain both "genuine" and "random" components. In the proposed approach, we remove the "random" component by testing the statistics of the eigenvalues of the correlation matrix against a "null hypothesis" - a truly random correlation matrix obtained from mutually uncorrelated expression data series. Our investigation into the components of deviating eigenvectors after varimax orthogonal rotation reveals distinct functional modules. The utilization of high performance computing resources including ScaLAPACK package, supercomputer and Linux PC cluster in our implementations and experiments significantly reduces the a

关键词： Functional Module microarray dataset Transcriptional Network Granulosa Cell Tumor Hierarchical Cluster Method

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Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data

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BMC BIOINFORMATICS 2008年第1期9卷 1-18页

作者： Pelz, Carl R. Kulesz-Martin, Molly Bagby, Grover Sears, Rosalie C. Oregon Hlth & Sci Univ Dept Mol & Med Genet Portland OR 97239 USA Oregon Hlth & Sci Univ Dept Dermatol Portland OR 97239 USA Oregon Hlth & Sci Univ Dept Cell & Dev Biol Portland OR 97239 USA Oregon Hlth & Sci Univ Dept Med Portland OR 97239 USA Oregon Hlth & Sci Univ OHSU Knight Canc Inst Portland OR 97239 USA

Background: microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip (R). Results: We commonly observe intensity-dependent technical variation between samples in a single microarray experiment. This is most common when MAS 5.0 is used to process probe level data, but we also see this type of technical variation with RMA and dChip (R) processed data. datasets with unbalanced numbers of up and down regulated genes seem to be particularly susceptible to this type of intensity-dependent technical variation. Unbalanced gene regulation is common when studying cancer samples or genetically manipulated animal models and preservation of this biologically relevant information, while removing technical variation has not been well addressed in the literature. We propose a method based on using rank-invariant, endogenous transcripts as reference points for normalization (GRSN). While the use of rank-invariant transcripts has been described previously, we have added to this concept by the creation of a global rank-invariant set of transcripts used to generate a robust average reference that is used to normalize all samples within a dataset. The global rank-invariant set is selected in an iterative manner so as to preserve unbalanced gene expression. Moreover, our method works well as an overlay that can be applied to data already processed with other probe set summary methods. We demonstrate that this additional normalization step at the "probe set level" effectively corrects a specific type of technical variation that often distorts samples in datasets. Conclusion: We have developed a simple post-proce

关键词： microarray dataset Robust Multichip Average Average Fold Change False Discovery Rate Threshold Fold Change Threshold

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Dynamic genetic algorithm-based feature selection and incomplete value imputation for microarray classification

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CURRENT SCIENCE 2017年第1期112卷 126-131页

作者： Priya, R. Devi Sivaraj, R. Kongu Engn Coll Dept Informat Technol Erode 638052 India Velalar Coll Engn & Technol Dept Comp Sci & Engn Erode 638002 India

Large microarray datasets usually contain many features with missing values. Inferences made from such incomplete datasets may be biased. To address this issue, we propose a novel preprocessing method called dynamic genetic algorithm-based feature selection with missing value imputation. The significant features are first identified using dynamic genetic algorithm-based feature selection and then the missing values are imputed using dynamic Bayesian genetic algorithm. The resulting complete microarray data sets with reduced features are used for classification, which results in better accuracy than the existing methods in eight microarray datasets.

关键词： microarray dataset feature selection missing values genetic algorithm

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Integrative disease classification based on cross-platform microarray data

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BMC BIOINFORMATICS 2009年第Sup1期10卷 S25-S25页

作者： Liu, Chun-Chi Hu, Jianjun Kalakrishnan, Mrinal Huang, Haiyan Zhou, Xianghong Jasmine Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ So Calif Los Angeles CA 90089 USA

Background: Disease classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based disease classification. Results: In this study, we tested the feasibility of disease classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of disease classes. We design a new classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many disease classes achieving the accuracy higher than 80%. Conclusion: Our results not only demonstrated the feasibility of the integrated disease classification approach, but also showed that the classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.

关键词： Support Vector Machine Disease Classification microarray dataset Unify Medical Language System Disease Class

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Multiobjective feature selection for microarray data via distributed parallel algorithms

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 100卷 952-981页

作者： Cao, Bin Zhao, Jianwei Yang, Po Yang, Peng Liu, Xin Qi, Jun Simpson, Andrew Elhoseny, Mohamed Mehmoode, Irfan Muhammad, Khan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool Merseyside England Mansoura Univ Fac Computers & Informat Mansoura Egypt Univ Bradford Dept Media Design & Technol Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Sejong Univ Dept Software Seoul 143747 South Korea

Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency. (C) 2019 Published by Elsevier B.V.

关键词： microarray dataset High dimension Multiobjective feature selection Distributed parallelism Feature redundancy

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A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

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BMC BIOINFORMATICS 2008年第1期9卷 1-10页

作者： Statnikov, Alexander Wang, Lily Aliferis, Constantin F. Vanderbilt Univ Dept Biomed Informat Nashville TN 37203 USA Vanderbilt Univ Dept Biostat Nashville TN USA Vanderbilt Univ Dept Canc Biol Nashville TN USA Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA

Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results: In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion: We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

关键词： Support Vector Machine Random Forest Classification Performance microarray dataset microarray Gene Expression Data

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Reproducible clusters from microarray research: Whither?

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BMC BIOINFORMATICS 2005年第s2期6卷 S10-S10页

作者： Garge, NR Page, GP Sprague, AP Gorman, BS Allison, DB Univ Alabama Birmingham Dept Biostat Sect Stat Genet Birmingham AL 35294 USA Univ Alabama Birmingham Dept Comp & Informat Sci Birmingham AL 35294 USA Hofstra Univ Dept Psychol Hempstead NY 11550 USA Med Coll Georgia Augusta GA 30912 USA

Motivation: In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets. Methods: We considered four commonly used iterative partitioning algorithms ( Self Organizing Maps (SOM), K-means, Clutsering LARge Applications (CLARA), and Fuzzy C-means) and evaluated their performances on 37 microarray datasets, with sample sizes ranging from 12 to 172. We assessed reproducibility of the clustering algorithm by measuring the strength of relationship between clustering outputs of subsamples of 37 datasets. Cluster stability was quantified using Cramer's v(2) from a kXk table. Cramer's v(2) is equivalent to the squared canonical correlation coefficient between two sets of nominal variables. Potential scores range from 0 to 1, with 1 denoting perfect reproducibility. Results: All four clustering routines show increased stability with larger sample sizes. K-means and SOM showed a gradual increase in stability with increasing sample size. CLARA and Fuzzy C-means, however, yielded low stability scores until sample sizes approached 30 and then gradually increased thereafter. Average stability never exceeded 0.55 for the four clustering routines, even at a sample size of 50. These findings suggest several plausible scenarios: ( 1) microarray datasets lack natural clustering structure thereby

关键词： Cluster Algorithm Real dataset Cluster Solution Simulated dataset microarray dataset

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From co-expression to co-regulation: how many microarray experiments do we need?

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GENOME BIOLOGY 2004年第7期5卷 1-11页

作者： Yeung, KY Medvedovic, M Bumgarner, RE Univ Washington Dept Microbiol Seattle WA 98195 USA Univ Cincinnati Med Ctr Dept Environm Hlth Ctr Genome Informat Cincinnati OH 45267 USA

Background: Cluster analysis is often used to infer regulatory modules or biological function by associating unknown genes with other genes that have similar expression patterns and known regulatory elements or functions. However, clustering results may not have any biological relevance. Results: We applied various clustering algorithms to microarray datasets with different sizes, and we evaluated the clustering results by determining the fraction of gene pairs from the same clusters that share at least one known common transcription factor. We used both yeast transcription factor databases (SCPD, YPD) and chromatin immunoprecipitation (ChIP) data to evaluate our clustering results. We showed that the ability to identify co-regulated genes from clustering results is strongly dependent on the number of microarray experiments used in cluster analysis and the accuracy of these associations plateaus at between 50 and 100 experiments on yeast data. Moreover, the model-based clustering algorithm MCLUST consistently outperforms more traditional methods in accurately assigning co-regulated genes to the same clusters on standardized data. Conclusions: Our results are consistent with respect to independent evaluation criteria that strengthen our confidence in our results. However, when one compares ChIP data to YPD, the false-negative rate is approximately 80% using the recommended p-value of 0.001. In addition, we showed that even with large numbers of experiments, the false-positive rate may exceed the true-positive rate. In particular, even when all experiments are included, the best results produce clusters with only a 28% true-positive rate using known gene transcription factor interactions.

关键词： Cluster Algorithm microarray Experiment Additional Data File microarray dataset Gene Subset

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In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

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BMC BIOINFORMATICS 2009年第1期10卷 1-15页

作者： Nero, Damion Katari, Manpreet S. Kelfer, Jonathan Tranchina, Daniel Coruzzi, Gloria M. NYU Dept Biol Ctr Genom & Syst Biol New York NY 10003 USA NYU Courant Inst Math Sci New York NY 10003 USA

Background: Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work [1], that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF -> target relationships on a genome-wide scale. Results: We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (>= 1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor ( TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF. target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the >= 1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions. Con

关键词： microarray Data Positive Predictor Value Spearman Correlation microarray dataset Regulatory Prediction

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Inference of radio-responsive gene regulatory networks using the graphical lasso algorithm

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BMC BIOINFORMATICS 2014年第Sup7期15卷 S5-S5页

作者： Oh, Jung Hun Deasy, Joseph O. Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10021 USA

Background: Inference of gene regulatory networks (GRNs) from gene microarray expression data is of great interest and remains a challenging task in systems biology. Despite many efforts to develop efficient computational methods, the successful modeling of GRNs thus far has been quite limited. To tackle this problem, we propose a novel framework to reconstruct radio-responsive GRNs based on the graphical lasso algorithm. In our attempt to study radiosensitivity, we reviewed the literature and analyzed two publicly available gene microarray datasets. The graphical lasso algorithm was applied to expression measurements for genes commonly found to be significant in these different analyses. Results: Assuming that a protein-protein interaction network obtained from a reliable pathway database is a gold-standard network, a comparison between the networks estimated by the graphical lasso algorithm and the gold-standard network was performed. Statistically significant p-values were achieved when comparing the gold-standard network with networks estimated from one microarray dataset and when comparing the networks estimated from two microarray datasets. Conclusion: Our results show the potential to identify new interactions between genes that are not present in a curated database and GRNs using microarray datasets via the graphical lasso algorithm.

关键词： Lasso Gene Regulatory Network microarray dataset GSE23393 dataset Gaussian Markov Random Field

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