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Gene-set approach for expression pattern analysis (vol 9, pg 189, 2008)

BRIEFINGS IN BIOINFORMATICS 2008年第5期9卷 450-450页

作者： Nam, Dougu Kim, Seon-Young KRIBB Funct Genom Res Ctr Taejon 305806 South Korea

Recently developed gene set analysis methods evaluate differential expression patterns of gene groups instead of those of individual genes. This approach especially targets gene groups whose constituents show subtle but coordinated expression changes, which might not be detected by the usual individual gene analysis. The approach has been quite successful in deriving new information from expression data, and a number of methods and tools have been developed intensively in recent years. We review those methods and currently available tools, classify them according to the statistical methods employed, and discuss their pros and cons. We also discuss several interesting extensions to the methods.

关键词： gene set analysis DNA microarray differential expression of genes microarray data FUNCTIONAL INTERPRETATION TESTING ASSOCIATION ENRICHMENT ANALYSIS PATHWAY ONTOLOGY ANNOTATION SIGNATURES PROFILES SYSTEMS

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Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis

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BIOINFORMATICS 2008年第8期24卷 1063-1069页

作者： Futschik, Matthias E. Herzel, Hanspeter Humboldt Univ Inst Theoret Biol D-10115 Berlin Germany

Motivation: Periodic processes play fundamental roles in organisms. Prominent examples are the cell cycle and the circadian clock. microarray array technology has enabled us to screen complete sets of transcripts for possible association with such fundamental periodic processes on a system-wide level. Frequently, quite large numbers of genes have been detected as periodically expressed. However, the small overlap between genes identified in different studies has cast some doubts on the reliability of the periodic expression detected. Results: In this study, comparative analysis suggests that the lacking agreement between different cell-cycle studies might be due to inadequate background models for the determination of significance. We demonstrate that the choice of background model has considerable impact on the statistical significance of periodic expression. For illustration, we reanalyzed two microarray studies of the yeast cell cycle. Our evaluation strongly indicates that the results of previous analyses might have been overoptimistic and that the use of more suitable background model promises to give more realistic results.

关键词： microarray data SACCHAROMYCES-CEREVISIAE REGULATED GENES EXPRESSION IDENTIFICATION SYNCHRONIZATION NORMALIZATION SELECTION BIAS

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MMG: a probabilistic tool to identify submodules of metabolic pathways

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BIOINFORMATICS 2008年第8期24卷 1078-1084页

作者： Sanguinetti, Guido Noirel, Josselin Wright, Phillip C. Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Sheffield Dept Chem Proc Engn Biol & Environm Syst Grp Sheffield S1 3JD S Yorkshire England

Motivation: A fundamental task in systems biology is the identification of groups of genes that are involved in the cellular response to particular signals. At its simplest level, this often reduces to identifying biological quantities (mRNA abundance, enzyme concentrations, etc.) which are differentially expressed in two different conditions. Popular approaches involve using t-test statistics, based on modelling the data as arising from a mixture distribution. A common assumption of these approaches is that the data are independent and identically distributed;however, biological quantities are usually related through a complex (weighted) network of interactions, and often the more pertinent question is which subnetworks are differentially expressed, rather than which genes. Furthermore, in many interesting cases (such as high-throughput proteomics and metabolomics), only very partial observations are available, resulting in the need for efficient imputation techniques. Results: We introduce Mixture Model on Graphs (MMG), a novel probabilistic model to identify differentially expressed submodules of biological networks and pathways. The method can easily incorporate information about weights in the network, is robust against missing data and can be easily generalized to directed networks. We propose an efficient sampling strategy to infer posterior probabilities of differential expression, as well as posterior probabilities over the model parameters. We assess our method on artificial data demonstrating significant improvements over standard mixture model clustering. Analysis of our model results on quantitative high-throughput proteomic data leads to the identification of biologically significant subnetworks, as well as the prediction of the expression level of a number of enzymes, some of which are then verified experimentally.

关键词： microarray data GENOMIC data NETWORKS EXPRESSION MODEL

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Genome-wide co-expression based prediction of differential expressions

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BIOINFORMATICS 2008年第5期24卷 666-673页

作者： Lai, Yinglei George Washington Univ Dept Stat Washington DC 20052 USA George Washington Univ Ctr Biostat Washington DC 20052 USA

Motivation: microarrays have been widely used for medical studies to detect novel disease-related genes. They enable us to study differential gene expressions at a genomic level. They also provide us with informative genome-wide co-expressions. Although many statistical methods have been proposed for identifying differentially expressed genes, genome-wide co-expressions have not been well considered for this issue. Incorporating genome-wide co-expression information in the differential expression analysis may improve the detection of disease-related genes. Results: In this study, we proposed a statistical method for predicting differential expressions through the local regression between differential expression and co-expression measures. The smoother span parameter was determined by optimizing the rank correlation between the observed and predicted differential expression measures. A mixture normal quantile-based method was used to transform data. We used the gene-specific permutation procedure to evaluate the significance of a prediction. Two published microarray data sets were analyzed for applications. For the data set collected for a prostate cancer study, the proposed method identified many genes with weak differential expressions. Several of these genes have been shown in literature to be associated with the disease. For the data set collected for a type 2 diabetes study, no significant genes could be identified by the traditional methods. However, the proposed method identified many genes with significantly low false discovery rates.

关键词： LINEAR-REGRESSION MODELS GENE-EXPRESSION microarray data STATISTICAL-METHOD PROSTATE-CANCER DNA microarray CELL-CYCLE DISCOVERY CLASSIFICATION IDENTIFICATION

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Analysis of the Arabidopsis histidine kinase ATHK1 reveals a connection between vegetative osmotic stress sensing and seed maturation

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PLANT CELL 2008年第4期20卷 1101-1117页

作者： Wohlbach, Dana J. Quirino, Betania F. Sussman, Michael R. Univ Wisconsin Dept Biochem Madison WI 53706 USA Univ Wisconsin Dept Genet Madison WI 53706 USA Univ Cattolica Brasilia Genom Sci & Biotechnol Program BR-70790 Brasilia DF Brazil Embrapa Agroenergia BR-70770 Brasilia DF Brazil

To cope with water stress, plants must be able to effectively sense, respond to, and adapt to changes in water availability. The Arabidopsis thaliana plasma membrane His kinase ATHK1 has been suggested to act as an osmosensor that detects water stress and initiates downstream responses. Here, we provide direct genetic evidence that ATHK1 not only is involved in the water stress response during early vegetative stages of plant growth but also plays a unique role in the regulation of desiccation processes during seed formation. To more comprehensively identify genes involved in the downstream pathways affected by the ATHK1-mediated response to water stress, we created a large-scale summary of expression data, termed the AtMegaCluster. In the AtMegaCluster, hierarchical clustering techniques were used to compare whole-genome expression levels in athk1 mutants with the expression levels reported in publicly available data sets of Arabidopsis tissues grown under a wide variety of conditions. These experiments revealed that ATHK1 is cotranscriptionally regulated with several Arabidopsis response regulators, together with two proteins containing novel sequences. Since overexpression of ATHK1 results in increased water stress tolerance, our observations suggest a new top-down route to increasing drought resistance via receptor-mediated increases in sensing water status, rather than through genetically engineered changes in downstream transcription factors or specific osmolytes.

关键词： ABSCISIC-ACID BIOSYNTHESIS PROBE LEVEL data RESPONSE REGULATORS GENE-EXPRESSION MASS-SPECTROMETRY microarray data SALT STRESS THALIANA PROTEIN PLANTS

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Merging two gene-expression studies via cross-platform normalization

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BIOINFORMATICS 2008年第9期24卷 1154-1160页

作者： Shabalin, Andrey A. Tjelmeland, Hakon Fan, Cheng Perou, Charles M. Nobel, Andrew B. Univ N Carolina Dept Stat & Operat Res Chapel Hill NC 27599 USA Norwegian Univ Sci & Technol Dept Math Sci N-7034 Trondheim Norway Univ N Carolina Lineberger Comprehens Canc Ctr Chapel Hill NC 27599 USA Univ N Carolina Dept Pathol & Lab Med Chapel Hill NC 27599 USA Univ N Carolina Dept Genet Chapel Hill NC 27599 USA

Motivation: Gene-expression microarrays are currently being applied in a variety of biomedical applications. This article considers the problem of how to merge datasets arising from different gene-expression studies of a common organism and phenotype. Of particular interest is how to merge data from different technological platforms. Results: The article makes two contributions to the problem. The first is a simple cross-study normalization method, which is based on linked gene/sample clustering of the given datasets. The second is the introduction and description of several general validation measures that can be used to assess and compare cross-study normalization methods. The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures. Availability: The supplementary materials and XPN Matlab code are publicly available at website: https://***/xpn Contact: shabalin@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： PROBE LEVEL data ART. NO. E15 microarray data BREAST-CANCER MOLECULAR CLASSIFICATION METAANALYSIS PORTRAITS SUMMARIES PROFILES TUMORS

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Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles

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BIOINFORMATICS 2008年第11期24卷 1359-1366页

作者： Bhattacharya, Anindya De, Rajat K. Indian Stat Inst Machine Intelligence Unit Kolkata India Netaji Subhash Engn Coll Dept Comp Sci & Engn Garia India

Motivation: Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we focus on several shortcomings of conventional clustering algorithms and propose a new one that is able to produce better clustering solution than that produced by some others. Results: We present the Divisive Correlation Clustering Algorithm (DCCA) that is suitable for finding a group of genes having similar pattern of variation in their expression values. To detect clusters with high correlation and biological significance, we use the correlation clustering concept introduced by Bansal et al. Our proposed algorithm DCCA produces a clustering solution without taking number of clusters to be created as an input. DCCA uses the correlation matrix in such a way that all genes in a cluster have highest average correlation with genes in that cluster. To test the performance of the DCCA, we have applied DCCA and some well-known conventional methods to an artificial dataset, and nine gene-expression datasets, and compared the performance of the algorithms. The clustering results of the DCCA are found to be more significantly relevant to the biological annotations than those of the other methods. All these facts show the superiority of the DCCA over some others for the clustering of gene-expression data.

关键词： microarray data

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A note on the false discovery rate and inconsistent comparisons between experiments

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BIOINFORMATICS 2008年第10期24卷 1225-1228页

作者： Higdon, Roger van Belle, Gerald Kolker, Eugene Seattle Childrens Res Inst Seattle WA 98101 USA Univ Washington Dept Biostat & Environm & Occupat Hlth Sci Seattle WA 98195 USA Univ Washington Dept Med Educ & Biomed Informat Div Biomed Informat Seattle WA 98195 USA

Motivation: The false discovery rate (FDR) has been widely adopted to address the multiple comparisons issue in high-throughput experiments such as microarray gene-expression studies. However, while the FDR is quite useful as an approach to limit false discoveries within a single experiment, like other multiple comparison corrections it may be an inappropriate way to compare results across experiments. This article uses several examples based on gene-expression data to demonstrate the potential misinterpretations that can arise from using FDR to compare across experiments. Researchers should be aware of these pitfalls and wary of using FDR to compare experimental results. FDR should be augmented with other measures such as p-values and expression ratios. It is worth including standard error and variance information for meta-analyses and, if possible, the raw data for re-analyses. This is especially important for high-throughput studies because data are often re-used for different objectives, including comparing common elements across many experiments. No single error rate or data summary may be appropriate for all of the different objectives.

关键词： MULTIPLE-COMPARISON PROCEDURES microarray data HYPOTHESIS CRITERIA

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Using repeated measurements to validate hierarchical gene clusters

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BIOINFORMATICS 2008年第5期24卷 682-688页

作者： Brehelin, Leurent Gascuel, Olivier Martin, Olivier Univ Montpellier 2 CNRS LIRMM F-34060 Montpellier 1 France INRA Unite Proteom F-34060 Montpellier France INRA Unite Biostat & Proc Spatiaux F-84914 Avignon 9 France

Motivation: Hierarchical clustering is a common approach to study protein and gene expression data. This unsupervised technique is used to find clusters of genes or proteins which are expressed in a coordinated manner across a set of conditions. Because of both the biological and technical variability, experimental repetitions are generally performed. In this work, we propose an approach to evaluate the stability of clusters derived from hierarchical clustering by taking repeated measurements into account. Results: The method is based on the bootstrap technique that is used to obtain pseudo-hierarchies of genes from resampled datasets. Based on a fast dynamic programming algorithm, we compare the original hierarchy to the pseudo-hierarchies and assess the stability of the original gene clusters. Then a shuffling procedure can be used to assess the significance of the cluster stabilities. Our approach is illustrated on simulated data and on two microarray datasets. Compared to the standard hierarchical clustering methodology, it allows to point out the dubious and stable clusters, and thus avoids misleading interpretations.

关键词： microarray data EXPRESSION PATTERNS RELIABILITY MIXTURE NETWORK

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Assessing TF regulatory relationships of divergently transcribed genes

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GENOMICS 2008年第5期92卷 316-321页

作者： Chen, Lan Cai, Lun Skogerbo, Geir Zhao, Yi Chen, Runsheng Chinese Acad Sci Inst Biophys Bioinformat Lab Beijing 100101 Peoples R China Chinese Acad Sci Inst Comp Technol Bioinformat Res Grp Beijing 100190 Peoples R China Chinese Acad Sci Grad Sch Beijing 100049 Peoples R China

Ambiguously located transcription factor (TF) binding sites may introduce a large number of potentially erroneous regulatory associations into models of transcriptional regulatory networks. We have used a two-step expression similarity strategy to distinguish between likely and unlikely regulatory associations for TFs located between divergently transcribed genes in the yeast genome. Most regulatory associations of divergently transcribed genes could be assigned to either high-confidence (HQ or low-confidence (LC) groups. In support of our result, we found that most of the previously characterized regulatory associations reported in the literature fell into the HC group rather than the LC group. Moreover, genomic distance analysis showed that TF binding sites tend to be located in relative proximity to the gene that is most likely to be regulated by this TF. Finally, removal of low-confidence (i.e., most probably erroneous) regulatory associations from the transcriptional regulatory network barely affected its basic architecture. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Divergently transcribed genes Gene regulation ChIP-chip data microarray data

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