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Prediction of core cancer genes using multi-task classification framework

JOURNAL OF THEORETICAL BIOLOGY 2013年 317卷 62-70页

作者： Gao, Shan Xu, Shuo Fang, Yaping Fang, Jianwen Univ Kansas Appl Bioinformat Lab Lawrence KS 66047 USA Inst Sci & Tech Informat China Beijing 100038 Peoples R China

Cancer is deemed as a highly heterogeneous disease specific to cell type and tissue origin. All cancers, however, share a common pathogenesis. Therefore, it is widely believed that cancers may share common mechanisms. In this study, we introduce a novel strategy based on multi-tasking learning methods to predict core cancer genes shared by multiple cancers in the hope of elucidating common cancer mechanisms. Our strategy uses two multi-tasking learning algorithms, one for feature selection and the other for validation of selected features. The combined use of two methods results in more robust classifiers and reliable selected features. The top 73 significant features, mapped to 72 genes, are selected as core cancer genes. The effectiveness of the 73 features is further demonstrated in a blind test conducted on an independent test data. The biological significance of these genes is evaluated using systems biology analyses. Extensive functional, pathway and network analysis confirms findings in previous studies and brings new insights into common cancer mechanisms. Our strategy can be used as a general method to find important genes from large gene expression datasets on the genomic level. The selected genes can be used to predict cancers. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multi-task learning Classification Core cancer genes Gene differential expression microarray data

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Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

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JOURNAL OF APPLIED STATISTICS 2009年第10期36卷 1067-1085页

作者： Conlon, E. M. Postier, B. L. Methe, B. A. Nevin, K. P. Lovley, D. R. Univ Massachusetts Dept Math & Stat Amherst MA 01003 USA Univ Massachusetts Dept Microbiol Amherst MA 01003 USA

The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus control. Here, we introduce a hierarchical Bayesian meta-analysis model to pool gene expression studies from different microarray platforms: spotted DNA arrays and short oligonucleotide arrays. The studies have different array design layouts, each with multiple sources of data replication, including repeated experiments, slides and probes. Our model produces the gene-specific posterior probability of differential expression, which is the basis for inference. In simulations combining two and five independent studies, our meta-analysis model outperformed separate analyses for three commonly used comparison measures;it also showed improved receiver operating characteristic curves. When combining spotted DNA and CombiMatrix short oligonucleotide array studies of Geobacter sulfurreducens, our meta-analysis model discovered more genes for fixed thresholds of posterior probability of differential expression and Bayesian false discovery than individual study analyses. We also examine an alternative model and compare models using the deviance information criterion.

关键词： Bayesian statistics meta-analysis microarray data multiple platform Markov chain Monte Carlo deviance information criterion

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Interactive and dynamic graphics in statistical consulting

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JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2004年第4期9卷 402-431页

作者： Henderson, HV AgResearch Ruakura Res Ctr Hamilton New Zealand

Twelve years ago, Unwin wrote an article titled "How Interactive Graphics Will Revolutionize Statistical Practice." Well, has it? I think it has. At that time data Desk was the prime commercially available package for interactive statistical graphics. Since then interactive and dynamic graphics have become increasingly available in many statistical and visualization packages, and some are even available, or easy to set up, in general spread-sheets like Excel. Examples of using interactive and dynamic graphics in ways that are useful in statistical consulting will be given. We'll gain insight into some old data and into some new data, including microarray data.

关键词： brushing data analysis data visualization exploratory data analysis interactive graphics linked scatterplots microarray data slicin statistical graphics

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A module map showing conditional activity of expression modules in cancer

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NATURE GENETICS 2004年第10期36卷 1090-1098页

作者： Segal, E Friedman, N Koller, D Regev, A Stanford Univ Dept Comp Sci Stanford CA 94305 USA Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel Harvard Univ Bauer Ctr Genom Res Cambridge MA 02138 USA

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor;for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.

关键词： GENE-EXPRESSION HEPATOCELLULAR-CARCINOMA DOWN-REGULATION microarray data METASTASIS KINASE TUMOR BONE ORGANIZATION PROGRESSION

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THE NEW HYBRID METHOD FOR CLASSIFICATION OF PATIENTS BY GENE EXPRESSION PROFILING

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JOURNAL OF INTEGRATED DESIGN & PROCESS SCIENCE 2010年第2期14卷 27-42页

作者： Cosgun, Erdal Karaagaoglu, Ergun Hacettepe Univ Fac Med Dept Biostat Ankara Turkey

Genetic researches have gradually become an area which is intensively studied on in recent years. The reason of that is the fact that a lot of diseases and features are transferred to the other generations by genes. These transfers are generally at the base of diseases. The evaluation of the input which is reached as the result of the researches is also accepted as a separate field. The aim of this study is to develop a model which enables the best classification of the patients by DNA microarray expression inputs. For this purpose, the classification which is based on Unsupervised Learning has mainly been used, by bringing together various methods. The Independent Components Analysis is used for dimension reduction, Kohonen Map Method is used for clustering and Random Forest Method is used for classification purposes. The model which is formed by combining these methods and very popular classification method Support Vector Machines (SVMs) has been studied and their classification performance is compared by True Classification Rate (TCR) on two real publicity data sets. The highest value that TCR can take on is one. The aim is to close this value to one. By the help of the model proposed in this study, we expect a reduction in the cost of these researches and aim to prevent wrong diagnoses as much as possible.

关键词： data mining random forest independent component analysis Kohonen map bootstrap classification clustering dimension reduction microarray data

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

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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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Screening and identification of the core immune-related genes and immune cell infiltration in severe burns and sepsis

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JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 2023年第11期27卷 1493-1508页

作者： Su, Wenxing Li, Wei Zhang, Yuanyuan Wang, Kuan Chen, Maolin Chen, Xiaoming Li, Dazhuang Zhang, Ping Yu, Daojiang China Natl Nucl Corp 416 Hosp Chengdu Med Coll Affiliated Hosp 2 Dept Plast & Burns Surg Chengdu Peoples R China First Affiliated Hosp Chengdu Med Coll Dept Urol Chengdu Peoples R China Xindu Dist Peoples Hosp Chengdu Dept Med Lab Chengdu Peoples R China First Affiliated Hosp Chengdu Med Coll Dept Cosmet Plast & Burns Surg Chengdu Peoples R China Yangzhou Univ Affiliated Hosp Dept Orthoped Yangzhou Peoples R China China Natl Nucl Corp 416 Hosp Chengdu Med Coll Affiliated Hosp 2 Dept Plast & Burns Surg 4 Er Huan Lu Bei Si Duan Chengdu 610051 Sichuan Peoples R China First Affiliated Hosp Chengdu Med Coll Dept Cosmet Plast & Burns Surg Chengdu Sichuan Peoples R China 278 Middle Baoguang Rd Chengdu 610599 Sichuan Peoples R China Yangzhou Univ Affiliated Hosp Dept Orthoped 45 Taizhou Rd Yangzhou 225000 Jiangsu Peoples R China

Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune-related differentially expressed genes (DEGs) involved in disease progression in both burns and sepsis. Subsequently, weighted gene coexpression network analysis (WGCNA), gene enrichment analysis, protein-protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, coexpression network analysis and clinical correlation analysis were performed. A total of 282 common DEGs associated with burns and sepsis were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the following enriched pathways in burns and sepsis: metabolic pathways;complement and coagulation cascades;legionellosis;starch and sucrose metabolism;and ferroptosis. Finally, six core DEGs were identified, namely, IL10, RETN, THBS1, FGF13, LCN2 and MMP9. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells. Of these, RETN upregulation was associated with a worse prognosis. The immune-related genes and dysregulated immune cells in severe burns and sepsis provide potential research directions for diagnosis and treatment.

关键词： burns core immune-related genes differentially expressed genes immune cell infiltration microarray data sepsis

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GO-Mapper: functional analysis of gene expression data using the expression level as a score to evaluate Gene Ontology terms

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BIOINFORMATICS 2004年第16期20卷 2618-2625页

作者： Smid, M Dorssers, LCJ Erasmus MC Josephine Nefkens Inst Dept Pathol NL-3000 DR Rotterdam Netherlands

Motivation: Retrieval of information on biological processes from large-scale expression data is still a time-consuming task. An automated analysis utilizing all expression information would greatly increase our understanding of the samples under study. Results: We describe here a novel method to obtain a functional analysis of complex gene expression data. Instead of applying a predefined expression threshold, Gene Ontology (GO) terms are weighted using the actual measured levels of expression of all associated genes. Based on this concept, the application GO-Mapper was developed to quantitatively link gene expression levels to GO-terms for multiple experiments in an automated way. The applicability of GO-Mapper was developed and validated on in house and public human microarray data and mouse SAGE data. We demonstrate that the GO-Mapper allows for interrelating relevant biological functions with the experiments under study.

关键词： SERIAL ANALYSIS microarray data RESOURCE GENOME TOOL

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Independent component analysis-based penalized discriminant method for tumor classification using gene expression data

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BIOINFORMATICS 2006年第15期22卷 1855-1862页

作者： Huang, De-Shuang Zheng, Chun-Hou Chinese Acad Sci Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Peoples R China

Motivation: microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e. g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. Results: This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

关键词： BLIND SOURCE SEPARATION MOLECULAR CLASSIFICATION NEURAL-NETWORKS microarray data BREAST-CANCER PREDICTION REGRESSION

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The effect of correlation in false discovery rate estimation

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BIOMETRIKA 2011年第1期98卷 199-214页

作者： Schwartzman, Armin Lin, Xihong Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA

The objective of this paper is to quantify the effect of correlation in false discovery rate analysis. Specifically, we derive approximations for the mean, variance, distribution and quantiles of the standard false discovery rate estimator for arbitrarily correlated data. This is achieved using a negative binomial model for the number of false discoveries, where the parameters are found empirically from the data. We show that correlation may increase the bias and variance of the estimator substantially with respect to the independent case, and that in some cases, such as an exchangeable correlation structure, the estimator fails to be consistent as the number of tests becomes large.

关键词： High-dimensional data microarray data Multiple testing Negative binomial

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