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gene expression microarray studies in polygenic psychiatric disorders: Applications and data analysis

BRAIN RESEARCH REVIEWS 2005年第1期50卷 142-155页

作者： Konradi, C Harvard Univ Sch Med Dept Psychiat Boston MA 02115 USA

gene expression microarrays have become a mainstream technology that can provide valuable insight into psychiatric disorders. gene expression studies in post mortem brain samples of schizophrenia and bipolar disorder have the potential to yield novel clues about the pathophysiology of these complex trait disorders. In the present review, a short introduction of the genetic and molecular background of schizophrenia and bipolar disorder is followed by a discussion of the basic concept and limits of gene expression microarray technology, and the complexities surrounding the analysis of thousands of gene transcripts. Although this review is intended for use in most platforms, it has a particular focus on the commercially available Affymetrix system. Various computer programs and their principal features are discussed, and it is shown how these programs can be applied to reveal a biological context of microarray findings. I will demonstrate how the programs can help to judge the results rather than focus on their statistical principles. The strength of gene array experiments is their emphasis on broad, biological themes, rather than on specific genes, and proper biostatistical approaches are important to ensure reproducibility of the findings. All results should be verified by independent means. This review is intended to help brain researchers who want to apply gene expression microarray technology to conceptualize research strategies and sample analysis. (c) 2005 Elsevier B.V. All rights reserved.

关键词： gene expression microarray schizophrenia bipolar disorder RMA GC-RMA dChip PLIER bioconductor GenMAPP

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Computerized system for recognition of autism on the basis of gene expression microarray data

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COMPUTERS IN BIOLOGY AND MEDICINE 2015年 56卷 82-88页

作者： Latkowski, Tomasz Osowski, Stanislaw Warsaw Univ Technol Inst Elect Syst Warsaw Poland Warsaw Univ Technol Inst Theory Elect Engn Measurement & Informat Sys Warsaw Poland

The aim of this paper is to provide a means to recognize a case of autism using gene expression microarrays. The crucial task is to discover the most important genes which are strictly associated with autism. The paper presents an application of different methods of gene selection, to select the most representative input attributes for an ensemble of classifiers. The set of classifiers is responsible for distinguishing autism data from the reference class. Simultaneous application of a few gene selection methods enables analysis of the ill-conditioned gene expression matrix from different points of view. The results of selection combined with a genetic algorithm and SVM classifier have shown increased accuracy of autism recognition. Early recognition of autism is extremely important for treatment of children and increases the probability of their recovery and return to normal social communication. The results of this research can find practical application in early recognition of autism on the basis of gene expression microarray analysis. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： gene expression microarray gene selection Ensemble of classifiers SVM Random forest

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Effective cancer subtyping by employing density peaks clustering by using gene expression microarray

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PERSONAL AND UBIQUITOUS COMPUTING 2018年第3期22卷 615-619页

作者： Mehmood, Rashid El-Ashram, Saeed Bie, Rongfang Sun, Yunchuan Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Kafr El Sheikh Univ Fac Sci Kafr Al Sheikh Egypt Beijing Normal Univ Sch Business Beijing 100875 Peoples R China

Discovering the similar groups is a popular primary step in analysis of biomedical data, which cannot be identified manually. Many supervised and unsupervised machine learning and statistical approaches have been developed to solve this problem. Clustering is an unsupervised learning approach, which organizes the data into similar groups, and is used to discover the intrinsic hidden structure of data. In this paper, we used clustering by fast search and find of density peaks (CDP) approach for cancer subtyping and identification of normal tissues from tumor tissues. In additional, we also address the preprocessing and underlying distance matrix's impact on finalized groups. We have performed extensive experiments on real-world and synthetic cancer gene expression microarray data sets and compared obtained results with state-of-the-art clustering approaches.

关键词： gene expression microarray Data mining Clustering Density peaks

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Transcriptomic responses to wounding: meta-analysis of gene expression microarray data

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BMC GENOMICS 2017年第1期18卷 1-12页

作者： Sass, Piotr Andrzej Dabrowski, Michal Charzynska, Agata Sachadyn, Pawel Gdansk Univ Technol Dept Mol Biotechnol & Microbiol Gdansk Poland Polish Acad Sci Neurobiol Ctr Lab Bioinformat Nencki Inst Expt Biol Warsaw Poland

Background: A vast amount of microarray data on transcriptomic response to injury has been collected so far. We designed the analysis in order to identify the genes displaying significant changes in expression after wounding in different organisms and tissues. This meta-analysis is the first study to compare gene expression profiles in response to wounding in as different tissues as heart, liver, skin, bones, and spinal cord, and species, including rat, mouse and human. Results: We collected available microarray transcriptomic profiles obtained from different tissue injury experiments and selected the genes showing a minimum twofold change in expression in response to wounding in prevailing number of experiments for each of five wound healing stages we distinguished: haemostasis & early inflammation, inflammation, early repair, late repair and remodelling. During the initial phases after wounding, haemostasis & early inflammation and inflammation, the transcriptomic responses showed little consistency between different tissues and experiments. For the later phases, wound repair and remodelling, we identified a number of genes displaying similar transcriptional responses in all examined tissues. As revealed by ontological analyses, activation of certain pathways was rather specific for selected phases of wound healing, such as e.g. responses to vitamin D pronounced during inflammation. Conversely, we observed induction of genes encoding inflammatory agents and extracellular matrix proteins in all wound healing phases. Further, we selected several genes differentially upregulated throughout different stages of wound response, including established factors of wound healing in addition to those previously unreported in this context such as PTPRC and AQP4. Conclusions: We found that transcriptomic responses to wounding showed similar traits in a diverse selection of tissues including skin, muscles, internal organs and nervous system. Notably, we distinguished transcripti

关键词： Wound healing Wound repair Tissue injury regeneration gene expression microarray Transcriptomics

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A general framework for optimization of probes for gene expression microarray and its application to the fungus Podospora anserina

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BMC Research Notes 2010年第1期3卷 1-7页

作者： Bidard, Frédérique Imbeaud, Sandrine Reymond, Nancie Lespinet, Olivier Silar, Philippe Clavé, Corinne Delacroix, Hervé Berteaux-Lecellier, Véronique Debuchy, Robert Univ Paris-Sud 11 Institut de Génétique et Microbiologie UMR8621 F- 91405 Orsay France CNRS Institut de Génétique et Microbiologie UMR8621 F-91404 Orsay France Univ Paris-Sud 11 F- 91405 Orsay France CNRS Centre de Génétique Moléculaire FRE3144 GODMAP F-91190 Gif sur Yvette France UFR des Sciences du Vivant Université de Paris 7 - Denis Diderot F-75205 Paris CEDEX 13 France Université Victor Segalen Institut de Biochimie et Génétique Cellulaires UMR 5095 F-33077 Bordeaux cedex 1 rue Camille Saint Sans France INSERM Génomique Fonctionnelle des Tumeurs Solides UMR U-674 IUH Université Paris-Descartes Paris F-75010 France USR 3278 CNRS-EPHE CRIOBE Université Perpignan Papetoai Moorea 98729 Polynésie BP 1013 France

Background. The development of new microarray technologies makes custom long oligonucleotide arrays affordable for many experimental applications, notably gene expression analyses. Reliable results depend on probe design quality and selection. Probe design strategy should cope with the limited accuracy of de novo gene prediction programs, and annotation up-dating. We present a novel in silico procedure which addresses these issues and includes experimental screening, as an empirical approach is the best strategy to identify optimal probes in the in silico outcome. Findings. We used four criteria for in silico probe selection: cross-hybridization, hairpin stability, probe location relative to coding sequence end and intron position. This latter criterion is critical when exon-intron gene structure predictions for intron-rich genes are inaccurate. For each coding sequence (CDS), we selected a sub-set of four probes. These probes were included in a test microarray, which was used to evaluate the hybridization behavior of each probe. The best probe for each CDS was selected according to three experimental criteria: signal-to-noise ratio, signal reproducibility, and representative signal intensities. This procedure was applied for the development of a gene expression Agilent platform for the filamentous fungus Podospora anserina and the selection of a single 60-mer probe for each of the 10,556 P. anserina CDS. Conclusions. A reliable gene expression microarray version based on the Agilent 44K platform was developed with four spot replicates of each probe to increase statistical significance of analysis. © 2010 Debuchy et al;licensee BioMed Central Ltd.

关键词： Outlier Probe gene expression microarray Normalize Signal Intensity Final Array Selection Round

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Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

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BMC BIOINFORMATICS 2007年第1期8卷 20-20页

作者： Sayyed-Ahmad, Abdallah Tuncay, Kagan Ortoleva, Peter J. Indiana Univ Dept Chem Ctr Cell & Virus Theory Bloomington IN 47405 USA Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN 55455 USA

Background: gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results: Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion: Multiplex time series data can be used for the construction of the network of

关键词： microarray Data Independent Component Analysis Transcription Factor Activity gene expression microarray gene expression microarray Data

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A HYBRID OF geneTIC ALGORITHM AND SUPPORT VECTOR MACHINE FOR FEATURES SELECTION AND CLASSIFICATION OF gene expression microarray

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2005年第1期5卷 91-107页

作者： Mohamad, Mohd Saberi Deris, Safaai Illias, Rosli Md Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Lab Artificial Intelligent & Bioinformat Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Chem & Nat Resource Engn Dept Bioprocess Engn Skudai 81310 Johor Malaysia

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.

关键词： Feature selection classification genetic algorithm support vector machine gene expression microarray

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FWCMR: A scalable and robust fuzzy weighted clustering based on MapReduce with application to microarray gene expression

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 91卷 198-210页

作者： Hosseini, Behrooz Kiani, Kourosh Semnan Univ Elect & Comp Engn Dept Semnan Iran

Data clustering is a very useful data mining technique to find groups of similar objects present in the dataset. Scalability to handle immense volumes, robustness to intrinsic outlier data and validity of clustering results are the main challenges of any data clustering approach. In order to address these challenges, a fuzzy weighted clustering approach which is comprehensibly parallel and distributed in every phase, is proposed in this research. Although the proposed method can be used for various data clustering purposes, it has been applied in gene expression clustering to reveal functional relationships of genes in a biological process. Conforming to MapReduce, the proposed method also presents a novel similarity measure which benefits from combining ordered weighted averaging and Spearman correlation coefficient. In the proposed method, density reachable genes were joined to establish subclusters. Afterwards, final cluster results were obtained by merging these subclusters. A voting system detects the best weights and consequently the most valid clusters among all possible results for each distinct dataset. The whole algorithm is implemented on a distributed processing platform and it is scalable to process any size of data stored in cloud infrastructures. Precision of resulting clusters were evaluated using some of the well-known cluster validity indexes in the literature. Also, the efficiency of the proposed method in scalability and robustness was compared with recently published similar researches. In all the mentioned comparisons, the proposed method outperformed recent works on the same datasets. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： MapReduce Distributed density based clustering gene expression microarray Fuzzy weighted clustering Decision making Big data

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microarray analysis of changes in bone cell gene expression early after cadmium gavage in mice

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TOXICOLOGY AND APPLIED PHARMACOLOGY 2003年第3期191卷 272-293页

作者： Regunathan, A Glesne, DA Wilson, AK Song, JW Nicolae, D Flores, T Bhattacharyya, MH Argonne Natl Lab Biosci Div Argonne IL 60439 USA Benedictine Univ Dept Biol Sci Lisle IL 60523 USA Univ Chicago Dept Stat Chicago IL 60637 USA

We developed an in vivo model for cadmium-induced bone loss in which mice excrete bone mineral in feces beginning 8 h after cadmium gavage. Female mice of three strains [CF1, MTN (metallothionein-wild-type), and MT1,2KO (MT1,2-deficient)] were placed on a low-calcium diet for 2 weeks. Each mouse was gavaged with 200 mug Cd or vehicle only. Fecal calcium was monitored daily for 9 days, beginning 4 days before cadmium gavage, to document the bone response. For CF1 mice, bones were taken from four groups: +/- Cd, 2 It after Cd and +/- Cd, 4 h after Cd. MTN and MT1,2KO strains had two groups each: +/-Cd, 4 h after Cd. PolyA+ RNA preparations from marrow-free shafts of femura and tibiae of each +/- Cd pair were submitted to Incyte Genomics for microarray analysis. Fecal Ca results showed that bone calcium excreted after cadmium differed for the three mouse strains: CFI, 0.24 +/- 0.08 mg;MTN, 0.92 +/- 0.22 mg;and MT1,2KO, 1.7 +/- 0.4 mg. gene array results showed that nearly all arrayed genes were unaffected by cadmium. However, MT1 and MT2 had Cd+/Cd- expression, ratios >1 in all four groups, while all ratios for MT3 were essentially 1, showing specificity. Both probes for MAPK 14 (p38 MAPK) had expression ratios >1, while no other MAPK responded to cadmium. Vacuolar proton pump ATPase and integrin alpha v (osteoclast genes), transferrin receptor, and src-like adaptor protein genes were stimulated by Cd;other src-related genes were unaffected. genes for bone formation, stress response, growth factors, and signaling molecules showed little or no response to cadmium. Results support the hypothesis that Cd stimulates bone demineralization via a p38 MAPK pathway involving osteoclast activation. (C) 2003 Elsevier Inc. All rights reserved.

关键词： cadmium bone gene expression microarray northern analysis mouse

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Differential gene expression profiles of neurothekeomas and nerve sheath myxomas by microarray analysis

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MODERN PATHOLOGY 2011年第3期24卷 343-354页

作者： Sheth, Sachiv Li, Xinmin Binder, Scott Dry, Sarah M. Univ Calif Los Angeles David Geffen Sch Med Dept Pathol & Lab Med Los Angeles CA 90095 USA

Neurothekeomas and dermal nerve sheath myxomas have previously been considered related cutaneous neoplasms of peripheral nerve sheath origin based on light microscopic similarities. However, recent immunohistochemical and ultrastructural data indicate nerve sheath myxomas exhibit true nerve sheath differentiation, whereas no such compelling evidence exists for neurothekeomas. Although neurothekeomas lack a specific immunohistochemical profile, similar antigen expression and histopathologic patterns suggest neurothekeomas may be categorized as fibrohistiocytic tumors. To date, no known molecular studies have examined the histogenetic relationship of these tumors. We report the first microarray-based gene expression profile study of these entities on formalin-fixed paraffin-embedded tissues. Cases of dermal schwannomas, dermal nerve sheath myxomas, myxoid/mixed/cellular neurothekeomas, and cellular fibrous histiocytomas diagnosed in the past 3 years were identified in our database. Archival formalin-fixed paraffin-embedded tissue from 28 patients was selected for microarray analysis (seven schwannomas, five nerve sheath myxomas, nine myxoid/mixed/cellular neurothekeomas and seven cellular fibrous histiocytomas). Following tumor RNA isolation, amplification and labeling using commercially available kits, labeled targets were hybridized to the Affymetrix geneChip Human Genome U133 Plus 2.0 Array (Santa Clara, CA, USA). Acquisition of array images and data analyses was performed using appropriate software. Hierarchical clustering and principal component analysis demonstrated discrete groups, which correlated with histopathologically identified diagnoses. Dermal nerve sheath myxomas demonstrate very similar molecular genetic signatures to dermal schwannomas, whereas neurothekeomas of all subtypes more closely resemble cellular fibrous histiocytomas. We are the first to report distinct gene expression profiles for nerve sheath myxomas and neurothekeomas, which further subs

关键词： cellular fibrous histiocytoma formalin fixed paraffin embedded gene expression microarray nerve sheath myxoma neurothekeoma schwannoma

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