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Hierarchical tree snipping: clustering guided by prior knowledge

BIOINFORMATICS 2007年第24期23卷 3335-3342页

作者： Dotan-Cohen, Dikla Melkman, Avraham A. Kasif, Simon Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Childrens Hosp Dept Biomed Engn Boston MA 02115 USA Childrens Hosp Ctr Adv Genom Technol Boston MA 02115 USA Childrens Hosp Bioinformat Program Boston MA 02115 USA Childrens Hosp Harvard MIT Program Hlth Sci & Technol Boston MA 02115 USA

Motivation: Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence. Results: To improve the biological significance of the clustering, we develop a new framework of partitioning by snipping-cutting selected edges at variable levels. The snipped edges are selected to induce clusters that are maximally consistent with partially available background knowledge such as functional classifications. Algorithms for two key applications are presented: functional prediction of genes, and discovery of functionally enriched clusters of co-expressed genes. Simulation results and cross-validation tests indicate that the algorithms perform well even when the actual number of clusters differs considerably from the requested number. Performance is improved compared with a previously proposed algorithm.

关键词： GENE-EXPRESSION data microarray data SACCHAROMYCES-CEREVISIAE DROSOPHILA-MELANOGASTER ANNOTATION NUMBER CYCLE

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A new hybrid method for gene selection

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PATTERN ANALYSIS AND APPLICATIONS 2011年第1期14卷 1-8页

作者： Cai, Ruichu Hao, Zhifeng Yang, Xiaowei Huang, Han Guangdong Univ Technol Fac Comp Sci Guangzhou 510006 Guangdong Peoples R China S China Univ Technol Coll Math Sci Guangzhou 510640 Peoples R China S China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China City Univ Hong Kong Coll Business Dept Management Sci Hong Kong Hong Kong Peoples R China

Gene selection is a significant preprocessing of the discriminant analysis of microarray data. The classical gene selection methods can be classified into three categories: the filters, the wrappers and the embedded methods. In this paper, a novel hybrid gene selection method (HGSM) is proposed by exploring both the mutual information criterion (filters) and leave-one-out-error criterion (wrappers) under the framework of an improved ant algorithm. Extensive experiments are conducted on three benchmark datasets and the results confirm the effectiveness and efficiency of HGSM.

关键词： microarray data Gene selection Hybrid method Ant algorithm

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Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

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International Journal of Ophthalmology(English edition) 2015年第2期8卷 269-274页

作者： Kai Shi Zhi-Tong Bing Gui-Qun Cao Ling Guo Ya-Na Cao Hai-Ou Jiang Mei-Xia Zhang Department of Ophthalmology West China HospitalSichuan University Molecular Medicine Research Center West China HospitalSichuan University Institute of Modern Physics Chinese Academy of Sciences College of Electrical Engineering Northwest University for Nationalities

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this ***: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of *** function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal ***: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

关键词： weighted gene co-expression network analysis microarray data gene ontology

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An effective measure corresponding to biological significance

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2014年第1期3卷 1-15页

作者： Goyal, Ankita Ahmed, Hasin A. Bhattacharyya, Dhruba K. Tezpur Univ Dept CSE Tezpur 784028 Assam India

Shifting and scaling correlations are correspondent of biological significance in gene expression data analysis. Recent works have mentioned about the significance of negative correlation as well. In this paper, we distinguish and define the negative form of shifting and scaling correlations as negatively shifted correlation and negatively scaled correlation, respectively. Another issue in gene expression data analysis is simultaneous detection of both negative and positive correlation (shifting and scaling) over two disjoint subsets of features in a pair of gene expressions while most existing measures can detect only either of the correlations at a time. Here we propose a measure that can detect positive and negative forms of shifting and scaling correlation simultaneously between a pair of gene expressions over two disjoint subsets of features.

关键词： microarray data Gene expression data analysis Shifting correlation Scaling correlation Negative correlation

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Significance analysis of functional categories in gene expression studies: a structured permutation approach

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BIOINFORMATICS 2005年第9期21卷 1943-1949页

作者： Barry, WT Nobel, AB Wright, FA Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA Univ N Carolina Dept Stat Chapel Hill NC 27599 USA

Motivation: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists. Method: We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates. Results: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times.

关键词： FALSE DISCOVERY RATE HUMAN LUNG-CANCER PANCREATIC-CANCER microarray data ADENOCARCINOMA PATTERNS ONTOLOGY dataBASE TARGET CELLS

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Mixture model on the variance for the differential analysis of gene expression data

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2005年第1期54卷 31-50页

作者： Delmar, P Robin, S Tronik-Le Roux, D Daudin, JJ Lab Fournier SA F-21121 Daix France Ecole Cent Paris Chatenay Malabry France INRA Paris France Commisariat Energie Atom Evry France

In microarray experiments, accurate estimation of the gene variance is a key step in the identification of differentially expressed genes. Variance models go from the too stringent homoscedastic assumption to the overparameterized model assuming a specific variance for each gene. Between these two extremes there is some room for intermediate models. We propose a method that identifies clusters of genes with equal variance. We use a mixture model on the gene variance distribution. A test statistic for ranking and detecting differentially expressed genes is proposed. The method is illustrated with publicly available complementary deoxyribonucleic acid microarray experiments, an unpublished data set and further simulation studies.

关键词： differential analysis gene expression microarray data mixture model simulated microarray data variance modelling

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Identification of promoters bound by c-Jun/ATF2 during rapid large-scale gene activation following genotoxic stress

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MOLECULAR CELL 2004年第4期16卷 521-535页

作者： Hayakawa, J Mittal, S Wang, Y Korkmaz, KS Adamson, E English, C Omichi, M McClelland, M Mercola, D Sidney Kimmel Canc Ctr San Diego CA 92121 USA Burnham Inst La Jolla CA 92037 USA Univ Calif San Diego Rebecca & John Moores Canc Ctr La Jolla CA 92093 USA Osaka Univ Sch Med Dept Obstet & Gynecol Suita Osaka 5650871 Japan

The NH2-terminal Jun kinases (JNKs) function in diverse roles through phosphorylation and activation of AP-1 components including ATF2 and c-Jun. However, the genes that mediate these processes are poorly understood. A model phenotype characterized by rapid activation of Jun kinase and enhanced DNA repair following cisplatin treatment was examined using chromatin immunoprecipitation with antibodies against ATF2 and c-Jun or their phosphorylated forms and hybridization to promoter arrays. Following genotoxic stress, we identified 269 genes whose promoters are bound upon phosphorylation of ATF2 and c-Jun. Binding did not occur following treatment with transplatin or the JNK inhibitor SP600125 or JNK-specific siRNA. Of 89 known DNA repair genes represented on the array, 23 are specifically activated by cisplatin treatment within 3-6 hr. Thus, the genotoxic stress response occurs at least partly via activation of ATF2 and c-Jun, leading to large-scale coordinate gene expression dominated by genes of DNA repair.

关键词： N-TERMINAL KINASE DNA-DAMAGING AGENTS C-JUN TRANSCRIPTION FACTORS microarray data TUMOR-CELLS EXPRESSION CISPLATIN REPAIR PROTEIN

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A review of feature selection techniques in bioinformatics

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BIOINFORMATICS 2007年第19期23卷 2507-2517页

作者： Saeys, Yvan Inza, Inaki Larranaga, Pedro VIB Dept Plant Syst Biol B-9052 Ghent Belgium Univ Ghent Dept Mol Genet Bioinformat & Evolutionary Genom Grp B-9052 Ghent Belgium Univ Basque Country Fac Comp Sci Dept Comp Sci & Artificial Intelligence San Sebastian Spain

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

关键词： GENE-EXPRESSION data MASS-SPECTROMETRY data FALSE DISCOVERY RATE OVARIAN-CANCER IDENTIFICATION BAYESIAN VARIABLE SELECTION microarray data CLASS PREDICTION SAMPLE CLASSIFICATION SNP SELECTION MULTICLASS CLASSIFICATION

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Identification of unique key genes and miRNAs in latent tuberculosis infection by network analysis

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MOLECULAR IMMUNOLOGY 2019年 112卷 103-114页

作者： Lin, Yan Zhang, Yuwei Yu, Huiyuan Tian, Ruonan Wang, Guoqing Li, Fan Jilin Univ Coll Basic Med Chinese Minist Educ Dept PathogenobiolKey Lab Zoonosis 126 Xinmin St Changchun 130021 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Bion Engn Changchun 130021 Jilin Peoples R China Jilin Univ Engn Res Ctr Med Biomat Jilin Prov Changchun 130021 Jilin Peoples R China Jilin Univ Key Lab Biomed Mat Jilin Prov Changchun 130021 Jilin Peoples R China Jilin Univ Sch Publ Hlth Changchun 130021 Jilin Peoples R China State Key Lab Pathogenesis Prevent & Treatment Hi Urumqi Xinjiang Peoples R China

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (***). New cases are now mainly caused by the progression of latent tuberculosis infection (LTBI). Thus, methods to diagnose and treat LTBI are urgently needed to prevent the development of active TB in infected individuals and the subsequent spread of the disease. In this study, a systems biology approach was utilized to obtain numerous microarray data sets for mRNAs and microRNAs (miRNAs) expressed in the peripheral blood mononuclear cells (PBMCs) of TB patients and individuals with LTBI. Within these data sets, we identified the differentially expressed mRNAs and miRNAs and further investigated which differentially expressed genes and miRNAs were uniquely expressed during LTBI. The database for Annotation, Visualization and Integrated Discovery (DAVID) was employed to analyze the functional annotations and pathway classifications of the identified genes. To further understand the unique miRNA-gene regulatory network of LTBI, we constructed a protein-protein interaction (PPI) network for the targeted genes. The PPI network included 39 genes that were differentially and uniquely expressed in PBMCs of individuals with LTBI, and KEGG pathway enrichment analysis showed that these genes were predominantly involved in the PI3K-Akt signaling pathway, which plays an important role in chronic inflammation. DIANA TOOLs-mirPath analysis revealed that the identified miRNAs in the miRNA-gene regulatory network for LTBI were mainly associated with the Hippo signaling pathway, which functions in the development of inflammation. Quantitative real-time PCR verified the up expression of hsa-miR-212-3p and its predicted target gene MAPK1 which had low expression and was a major component of the PPI network, and MAPK1 expression was correlated with the clinicopathological characteristics of LTBI by receiver operating characteristic (ROC) curve analysis. Therefore, MAPK1 has potential to be a new i

关键词： microarray data miRNA-Gene network PPI Network Marker Latent tuberculosis infection (LTBI)

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Identification of genes associated with methotrexate resistance in methotrexate-resistant osteosarcoma cell lines

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JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH 2015年第1期10卷 1-9页

作者： Yang, Xiao-rong Xiong, Yan Duan, Hong Gong, Ren-rong Sichuan Univ West China Hosp Dept Operat Room Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Dept Orthoped Chengdu 610041 Sichuan Peoples R China

Background: This study aimed to better understand the mechanisms underlying methotrexate (MTX)-resistance in osteosarcoma. Methods: The raw transcription microarray data GSE16089 collected from three MTX-sensitive osteosarcoma (Saos-2) cell samples and three MTX-resistant osteosarcoma (Saos-2) cell samples were downloaded from Gene Expression Omnibus. After data processing, the differentially expressed genes (DEGs) were identified. Next, DEGs were submitted to DAVID for functional annotation based on the GO (Gene Ontology) database, as well as pathway enrichment analysis based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Transcription factors (TFs) and tumor-associated genes (TAGs) were identified with reference to TRANSFAC and TAG, and TSGene databases, respectively. The protein-protein interaction (PPI) network of the gene-encoded products was constructed, and the subnetwork with the highest score was also detected using Search Tool for the Retrieval of Interacting Genes and BioNet package. Results: A total of 690 up-regulated genes and down-regulated 626 genes were identified. Up-regulated DEGs (including AARS and PARS2) were associated to transfer RNA (tRNA) aminoacylation while down-regulated DEGs (including AURKA, CCNB1, CCNE2, CDK1, and CENPA) were correlated with mitotic cell cycle. Totally, 13 TFs (including HMGB2), 13 oncogenes (including CCNA2 and AURKA), and 19 tumor suppressor genes (TSGs) (including CDKN2C) were identified from the down-regulated DEGs. Ten DEGs, including nine down-regulated genes (such as AURKA, CDK1, CCNE2, and CENPA) and one up-regulated gene (GADD45A), were involved in the highest score subnetwork. Conclusion: AARS, AURKA, AURKB, CENPA, CCNB1, CCNE2, and CDK may contribute to MTX resistance via aminoacyl-tRNA biosynthesis pathway, cell cycle pathway, or p53 signaling pathway.

关键词： Osteosarcoma Methotrexate resistance microarray data

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