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Unified LASSO estimation by least squares approximation

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2007年第479期102卷 1039-1048页

作者： Wang, Hansheng Leng, Chenlei Peking Univ Grad Sch Management Beijing 100871 Peoples R China Natl Univ Singapore Dept Stat & Appl Probabil Singapore 117548 Singapore

We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least squares problems. Thereafter, the standard asymptotic theory can be established and the LARS algorithm can be applied. In particular, if the adaptive LASSO penalty and a Bayes information criterion-type tuning parameter selector are used, the resulting LSA estimator can be as efficient as the oracle. Extensive numerical studies confirm our theory.

关键词： adaptive LASSO Bayes information criterion LASSO least angle regression least squares approximation microarray data oracle property solution path

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Sample size calculation with dependence a ustment for FDR-control in microarray studies

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STATISTICS IN MEDICINE 2007年第23期26卷 4219-4237页

作者： Shao, Yongzhao Tseng, Chi-Hong NYU Sch Med Div Biostat New York NY 10016 USA

DNA microarrays have been widely used for the purpose of simultaneously monitoring a large number of gene expression levels to identify differentially expressed genes. Statistical methods for the adjustment of multiple testing have been discussed extensively in the literature. An important further challenge is the existence of dependence among test statistics due to reasons such as gene co-regulation. To plan large-scale genomic studies, sample size determination with appropriate adjustment for both multiple testing and potential dependency among test statistics is crucial to avoid an abundance of false-positive results and/or serious lack of power. We introduce a general approach for calculating sample sizes for two-way multiple comparisons in the presence of dependence among test statistics to ensure adequate overall power when the false discovery rates are controlled. The usefulness of the proposed method is demonstrated via numerical studies using both simulated data and real data from a well-known study of leukaemia. Copyright (c) 2007 John Wiley & Sons, Ltd.

关键词： dependence false discovery rates power sample size microarray data

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A graph-based approach to systematically reconstruct human transcriptional regulatory modules

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BIOINFORMATICS 2007年第13期23卷 I577-I586页

作者： Yan, Xifeng Mehan, Michael R. Huang, Yu Waterman, Michael S. Yu, Philip S. Zhou, Xianghong Jasmine Univ So Calif Program Mol & Computat Biol Los Angeles CA 90089 USA IBM Corp Thomas J Watson Res Ctr Hawthorne NY USA

Motivation: A major challenge in studying gene regulation is to systematically reconstruct transcription regulatory modules, which are defined as sets of genes that are regulated by a common set of transcription factors. A commonly used approach for transcription module reconstruction is to derive coexpression clusters from a microarray dataset. However, such results often contain false positives because genes from many transcription modules may be simultaneously perturbed upon a given type of conditions. In this study, we propose and validate that genes, which form a coexpression cluster in multiple microarray datasets across diverse conditions, are more likely to form a transcription module. However, identifying genes coexpressed in a subset of many microarray datasets is not a trivial computational problem. Results: We propose a graph-based data-mining approach to efficiently and systematically identify frequent coexpression clusters. Given m microarray datasets, we model each microarray dataset as a coexpression graph, and search for vertex sets which are frequently densely connected across [theta m] datasets (0 <= theta <= 1). For this novel graph-mining problem, we designed two techniques to narrow down the search space: ( 1) partition the input graphs into ( overlapping) groups sharing common properties;( 2) summarize the vertex neighbor information from the partitioned datasets onto the 'Neighbor Association Summary Graph's for effective mining. We applied our method to 105 human microarray datasets, and identified a large number of potential transcription modules, activated under different subsets of conditions. Validation by ChIP-chip data demonstrated that the likelihood of a coexpression cluster being a transcription module increases significantly with its recurrence. Our method opens a new way to exploit the vast amount of existing microarray data accumulation for gene regulation study. Furthermore, the algorithm is applicable to other biological networ

关键词： GENE-EXPRESSION data FUNCTIONAL ANNOTATION microarray data NETWORKS PATTERNS

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Weighted Local Least Squares Imputation Method for Missing Value Estimation

Weighted Local Least Squares Imputation Method for Missing V...

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第一届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Kwai-Wa Cheng Li-Min Li Nam-Kiu Tsing Alice S. Wong Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Anderson Cancer Center The University of Texas Department of Zoology The University of Hong Kong

Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. WLLSI allows training on the weighting and therefore can take advantage of both the LLSI method and the RA method. Numerical results on both synthetic data and real microarray data are given to demonstrate the effectiveness of our proposed method. The imputation methods are then applied to a breast cancer dataset.

关键词： Missing values microarray data row average method local least squares imputation method weighted local least squares imputation method.

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Nonlinear modeling of protein expressions in protein arrays

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2006年第6期54卷 2394-2407页

作者： Tabus, Ioan Hategan, Andrea Mircean, Cristian Rissanen, Jorma Shmulevich, Ilya Zhang, Wei Astola, Jaakko Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland Univ Texas MD Anderson Canc Ctr Canc Genom Core Lab Houston TX 77030 USA

This paper addresses the problem of estimating the expressions or concentrations of proteins from measurements obtained from protein arrays and illustrates the methodology on lysate microarray data. With several families of parametric models we design a number of algorithms for the estimation of a highly nonlinear calibration curve as well as the. concentrations themselves. The model families include polynomial and sigmoidal nonlinearities for the calibration curve and homoscedastic or heteroscedastic models for the noise. The accuracy of the estimation methods is tested on simulated data and applied to real lysate array data. The results are generally very good, provided that strongly nonlinear models are used.

关键词： heteroscedastic noise maximum-likelihood estimation microarray data model order selection nonlinear estimation proteins

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Transcript analysis of early nodulation events in Medicago truncatula

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PLANT PHYSIOLOGY 2006年第1期140卷 221-234页

作者： Lohar, DP Sharopova, N Endre, G Peñuela, S Samac, D Town, C Silverstein, KAT VandenBosch, KA Univ Minnesota Dept Plant Biol St Paul MN 55108 USA Univ Minnesota Dept Plant Pathol St Paul MN 55108 USA Univ Minnesota Microbial & Plant Genom Inst St Paul MN 55108 USA Hungarian Acad Sci Inst Genet Biol Res Ctr H-6726 Szeged Hungary USDA ARS Plant Sci Res Unit St Paul MN 55108 USA Inst Genom Res Rockville MD 20850 USA

Within the first 72 h of the interaction between rhizobia and their host plants, nodule primordium induction and infection occur. We predicted that transcription profiling of early stages of the symbiosis between Medicago truncatula roots and Sinorhizobium meliloti would identify regulated plant genes that likely condition key events in nodule initiation. Therefore, using a microarray with about 6,000 cDNAs, we compared transcripts from inoculated and uninoculated roots corresponding to defined stages between 1 and 72 h post inoculation (hpi). Hundreds of genes of both known and unknown function were significantly regulated at these time points. Four stages of the interaction were recognized based on gene expression profiles, and potential marker genes for these stages were identified. Some genes that were regulated differentially during stages I (1 hpi) and II (6 - 12 hpi) of the interaction belong to families encoding proteins involved in calcium transport and binding, reactive oxygen metabolism, and cytoskeleton and cell wall functions. Genes involved in cell proliferation were found to be upregulated during stages III (24 - 48 hpi) and IV (72 hpi). Many genes that are homologs of defense response genes were upregulated during stage I but down-regulated later, likely facilitating infection thread progression into the root cortex. Additionally, genes putatively involved in signal transduction and transcriptional regulation were found to be differentially regulated in the inoculated roots at each time point. The findings shed light on the complexity of coordinated gene regulation and will be useful for continued dissection of the early steps in symbiosis.

关键词： RHIZOBIUM-MELILOTI GENE-EXPRESSION NOD FACTORS CALCIUM SPIKING REACTIVEOXYGEN ROOT HAIRS TRANSGENIC ALFALFA microarray data PLANT DEFENSE WHITE CLOVER

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Boosting and microarray data

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MACHINE LEARNING 2003年第1-2期52卷 31-44页

作者： Long, PM Vega, VB Genome Inst Singapore Singapore Singapore

We have found one reason why AdaBoost tends not to perform well on gene expression data, and identified simple modifications that improve its ability to find accurate class prediction rules. These modifications appear especially to be needed when there is a strong association between expression profiles and class designations. Cross-validation analysis of six microarray datasets with different characteristics suggests that, suitably modified, boosting provides competitive classification accuracy in general. Sometimes the goal in a microarray analysis is to find a class prediction rule that is not only accurate, but that depends on the level of expression of few genes. Because boosting makes an effort to find genes that are complementary sources of evidence of the correct classification of a tissue sample, it appears especially useful for such gene-efficient class prediction. This appears particularly to be true when there is a strong association between expression profiles and class designations, which is often the case for example when comparing tumor and normal samples.

关键词： supervised learning classification boosting gene expression data microarray data bioinformatics

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Avoiding model selection bias in small-sample genomic datasets

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BIOINFORMATICS 2006年第10期22卷 1245-1250页

作者： Berrar, D Bradbury, I Dubitzky, W Univ Ulster Sch Biomed Sci Coleraine BT52 1SA Londonderry North Ireland

Motivation: Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived from such studies are often optimistically biased, because the apparent differences in performance are usually not controlled in a statistically stringent framework taking into account the adopted sampling strategy. We investigate this problem by means of a comparison of various classifiers in the context of multiclass microarray data. Results: Commonly used accuracy-based performance values, with or without confidence intervals, are inadequate for comparing classifiers for small-sample data. We present a statistical methodology that avoids bias in cross-validated model selection in the context of small-sample scenarios. This methodology is valid for both k-fold cross-validation and repeated random sampling.

关键词： GENE-EXPRESSION CLASSIFICATION METHODS TUMOR CLASSIFICATION STATISTICALTESTS CANCER-DIAGNOSIS CLASS PREDICTION microarray data DISCOVERY INFERENCE

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Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

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BIOMETRICAL JOURNAL 2006年第3期48卷 435-450页

作者： Jung, Yoon-Young Oh, Man-Suk Shin, Dong Wan Kang, Seung-ho Oh, Hyun Sook Kyungwon Univ Dept Appl Stat Sung Nam 461701 South Korea Ewha Womans Univ Dept Stat Seoul 120750 South Korea

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from nondifferentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR;Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.

关键词： cluster analysis hierarchical model false discovery rate microarray data

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Correlating Expression data with Gene Function Using Gene Ontology

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Chinese Journal of Chemistry 2006年第9期24卷 1247-1254页

作者：刘琪邓勇王川石铁流李亦学 School of Life Science & Biotechnology Shanghai Jiao Tong University Shanghai 200240 China Zhejiang Police Vocational Academy Hangzhou Zhejiang 310018 China Biomformatton Center Shanghat Instttutesfor Btologtcal Sctences Chinese Academy of Sciences Shanghai 200031 China

Clustering is perhaps one of the most widely used tools for microarray data analysis. Proposed roles for genes of unknown function are inferred from clusters of genes similarity expressed across many biological conditions. However, whether function annotation by similarity metrics is reliable or not and to what extent the similarity in gene expression patterns is useful for annotation of gene functions, has not been evaluated. This paper made a comprehensive research on the correlation between the similarity of expression data and of gene functions using Gene Ontology. It has been found that although the similarity in expression patterns and the similarity in gene functions are significantly dependent on each other, this association is rather weak. In addition, among the three categories of Gene Ontology, the similarity of expression data is more useful for cellular component annotation than for biological process and molecular function. The results presented are interesting for the gene functions prediction research area.

关键词： microarray data gene ontology similarity of expression data function annotation

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