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Kernel hierarchical gene clustering from microarray expression data

BIOINFORMATICS 2003年第16期19卷 2097-2104页

作者： Qin, J Lewis, DP Noble, WS Columbia Univ Columbia Genome Ctr New York NY 10032 USA Columbia Univ Dept Comp Sci New York NY 10027 USA Univ Washington Dept Genome Sci Seattle WA 98195 USA

Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similiar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. Results: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality.

关键词： .pairwise microarray data feature space microarray expression kernel function hierarchical clustering algorithm

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Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data

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GENES AND IMMUNITY 2005年第4期6卷 319-331页

作者： Abbas, AR Baldwin, D Ma, Y Ouyang, W Gurney, A Martin, F Fong, S Campagne, MV Godowski, P Williams, PM Chan, AC Clark, HF Genentech Inc Dept Bioinformat San Francisco CA 94080 USA Genentech Inc Dept Immunol San Francisco CA 94080 USA Genentech Inc Dept Mol Biol San Francisco CA 94080 USA

Immune cell-specific expression is one indication of the importance of a gene's role in the immune response. We have compiled a compendium of microarray expression data for virtually all human genes from six key immune cell types and their activated and differentiated states. Immune Response In Silico ( IRIS) is a collection of genes that have been selected for specific expression in immune cells. The expression pattern of IRIS genes recapitulates the phylogeny of immune cells in terms of the lineages of their differentiation. Gene Ontology assignments for IRIS genes reveal significant involvement in inflammation and immunity. Genes encoding CD antigens, cytokines, integrins and many other gene families playing key roles in the immune response are highly represented. IRIS also includes proteins of unknown function and expressed sequence tags that may not represent genes. The predicted cellular localization of IRIS proteins is evenly distributed between cell surface and intracellular compartments, indicating that immune specificity is important at many points in the signaling pathways of the immune response. IRIS provides a resource for further investigation into the function of the immune system and immune diseases.

关键词： microarray expression specific expression immune response

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Three methods for optimization of cross-laboratory and cross-platform microarray expression data

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NUCLEIC ACIDS RESEARCH 2007年第10期35卷 e72-e72页

作者： Stafford, Phillip Brun, Marcel Arizona State Univ Biodesign Inst Ctr Innovat Med Tempe AZ 85287 USA Translat Genom Res Inst Div Computat Biol Phoenix AZ USA

microarray gene expression data becomes more valuable as our confidence in the results grows. Guaranteeing data quality becomes increasingly important as microarrays are being used to diagnose and treat patients (1-4). The MAQC Quality Control Consortium, the FDA's Critical Path Initiative, NCI's caBIG and others are implementing procedures that will broadly enhance data quality. As GEO continues to grow, its usefulness is constrained by the level of correlation across experiments and general applicability. Although RNA preparation and array platform play important roles in data accuracy, pre-processing is a user-selected factor that has an enormous effect. Normalization of expression data is necessary, but the methods have specific and pronounced effects on precision, accuracy and historical correlation. As a case study, we present a microarray calibration process using normalization as the adjustable parameter. We examine the impact of eight normalizations across both Agilent and Affymetrix expression platforms on three expression readouts: (1) sensitivity and power, (2) functional/ biological interpretation and (3) feature selection and classification error. The reader is encouraged to measure their own discordant data, whether cross-laboratory, cross-platform or across any other variance source, and to use their results to tune the adjustable parameters of their laboratory to ensure increased correlation.

关键词： .data quality Feature Selection cross-platform biological interpretation microarray expression normalizations

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Analysis of microarray expression data

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Genome Biology 2000年第1期1卷 1-4页

作者： Paul Kellam

Detailed descriptions of the design and information content of EBI's database ArrayExpress, which is currently under development but aims to be a public repository of gene expression data, are the main feature of ... 详细信息

关键词： Gene expression Data microarray expression microarray Data Analysis microarray expression Data Public Repository

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microarray analysis of long-noncoding RNAs and mRNA expression profiles in human steroid-induced avascular necrosis of the femoral head

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JOURNAL OF CELLULAR BIOCHEMISTRY 2019年第9期120卷 15800-15813页

作者： Luo, Hongbin Lan, Weibin Li, Yongfeng Lian, Xiaolan Zhang, Nanwen Lin, Xinhua Chen, Peng Fujian Med Univ Affiliated Hosp 1 Dept Joint Surg & Sports Med Fuzhou Fujian Peoples R China Fujian Med Univ Sch Pharm Fuzhou Fujian Peoples R China Fujian Med Univ Longyan Hosp 1 Dept Orthoped Longyan Peoples R China Fujian Med Univ Sch Clin Med 1 Fuzhou Fujian Peoples R China Fujian Med Univ Union Hosp Fujian Prov Key Lab Hematol Fujian Inst Hematol Fuzhou Fujian Peoples R China

This study performed the first microarray analysis of long-noncoding RNA (lncRNA) and mRNA expression profiles in human steroid-induced avascular necrosis of the femoral head (SAVNFH). expression levels of lncRNAs and mRNAs in three human SAVNFH samples and three human femoral head fracture samples (controls) were detected using third-generation lncRNA microarrays (KangChen Biotech, Shanghai, China). The fold change, false discovery rate, and P value were utilized to filter genes with significant differential expression in the SAVNFH samples compared with the control samples. In total, there were 1179 upregulated and 3214 downregulated lncRNAs (P2. zerofold, P < 0.05). Meanwhile, 1092 upregulated and 565 downregulated mRNAs were found in the SAVNFH samples compared with the control samples. Then, quantitative real-time polymerase chain reaction was used to confirm the previous microarray results using 8 and 20 selected dysregulated lncRNAs and mRNAs, respectively, and the results generally confirmed the microarray findings. Finally, we used Gene Ontology (GO) and pathway analysis to investigate the functions of the altered mRNAs and their associated GO terms and biological pathways. The Immune system process term (GO:0002376) was the most significantly upregulated GO term, and the Regulation of blood coagulation term (GO:0030193) was the most significantly downregulated GO term in the biological process category for the SAVNFH samples. "Hematopoietic cell lineage - Homo sapiens (human) (Pathway ID: hsa04640)" and "Complement and coagulation cascades - Homo sapiens (human) (Pathway ID: hsa04610)" were the most significantly up- and downregulated pathways in the SAVNFH samples compared with the controls. In conclusion, the differential expression of lncRNAs and mRNAs may be correlated with the pathogenesis of SAVNFH, and these significantly dysregulated lncRNAs and mRNAs may function through networks or participate in several specific biological processes. Further res

关键词： long-noncoding RNAs microarray expression mRNAs steroid-induced avascular necrosis of the femoral head

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TCLUST: A Fast Method for Clustering Genome-Scale expression Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第3期8卷 808-818页

作者： Dost, Banu Wu, Chunlei Su, Andrew Bafna, Vineet Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Novartis Res Fdn Genom Inst San Diego CA 92121 USA

Genes with a common function are often hypothesized to have correlated expression levels in mRNA expression data, motivating the development of clustering algorithms for gene expression data sets. We observe that existing approaches do not scale well for large data sets, and indeed did not converge for the data set considered here. We present a novel clustering method TCLUST that exploits coconnectedness to efficiently cluster large, sparse expression data. We compare our approach with two existing clustering methods CAST and K-means which have been previously applied to clustering of gene-expression data with good performance results. Using a number of metrics, TCLUST is shown to be superior to or at least competitive with the other methods, while being much faster. We have applied this clustering algorithm to a genome-scale gene-expression data set and used gene set enrichment analysis to discover highly significant biological clusters. (Source code for TCLUST is downloadable at http://***/similar to bdost/tclust.)

关键词： microarray expression clustering graph algorithms coconnectedness

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Gene signatures reflect the marked heterogeneity of tissue-resident macrophages

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IMMUNOLOGY AND CELL BIOLOGY 2008年第3期86卷 246-254页

作者： Gorgani, Nick N. Ma, Yan Clark, Hilary F. Genentech Inc Dept Bioinformat San Francisco CA 94080 USA Genentech Inc Dept Immunol San Francisco CA 94080 USA

Tissue-resident macrophages play an important role in defense against pathogens and perform key functions in organ homeostasis, innate and adaptive immunity. Tissue macrophages originate from blood monocytes that infiltrate virtually every organ in the body. Macrophages in different tissues share many characteristics, including their ability to migrate, phagocytose particles, metabolize lipids and present antigens. Morphologically they are quite heterogeneous, and some distinct functions have been reported. The gene expression profile of macrophages is reflective of both their shared and distinct biological functions. Here, we show that macrophages from murine spleen, liver and peritoneum display dramatically different expression profiles. Clusters of genes were found to represent unique biological functions related to adhesion, antigen presentation, phagocytosis, lipid metabolism and signal transduction. Some gene families, such as integrins, are differentially expressed among the macrophages resident in different tissues, suggesting that the tissue of residence influences their biological function.

关键词： macrophage gene signature microarray expression

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Genome-wide analysis of DNA methylation and their associations with long noncoding RNA/ mRNA expression in non-small-cell lung cancer

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EPIGENOMICS 2017年第2期9卷 137-153页

作者： Feng, Nannan Wang, Yu Zheng, Min Yu, Xiao Lin, Hongyan Ma, Rong-Na Shi, Oumin Zheng, Xiangqian Gao, Ming Yu, Herbert Garmire, Lana Qian, Biyun Shanghai Jiao Tong Univ Sch Med Hongqiao Int Inst Med Shanghai Tongren Hosp Shanghai 200025 Peoples R China Shanghai Jiao Tong Univ Sch Med Fac Publ Hlth Shanghai 200025 Peoples R China Tianjin Med Univ Tianjin Key Lab Canc Prevent & Therapy Canc Inst & Hosp Tianjin 300060 Peoples R China Univ Hawaii Canc Epidemiol Program Ctr Canc 701 Ilalo St Honolulu HI 96813 USA

Aim: The goal of this study is to identify differentially methylated (DM) loci associated with long noncoding RNA (lncRNA)/mRNA expression in non-small-cell lung cancer (NSCLC). Materials & methods: microarrays were used to interrogate genome-wide methylation and expression of lncRNA/mRNA in NSCLC. Results: We identified 113,644 DM loci between tumors and adjacent tissues. Among them, 26,310 DM loci were associated with 1685 differentially expressed genes, and 839 genes had significant correlations between methylation and expression, of which 26 hypermethylated loci in transcription start site 200 were correlated with low gene expression. We validated the correlations between methylation and expression in five genes (CDO1, C2orf40, SCARF1, ZFP106 and IFFO1) using pyrosequencing and quantitative polymerase chain reaction. We also found significant correlations between lncRNAs and mRNAs, and validated four of the correlations with quantitative polymerase chain reaction. Conclusion: Integrated analysis of genome-wide DNA methylation and lncRNA/mRNA expression allows us to identify new DM loci-correlated with gene expression in NSCLC.

关键词： genome-wide methylation long noncoding RNA lung cancer microarray expression

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hsa_circ_0000729, a potential prognostic biomarker in lung adenocarcinoma

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THORACIC CANCER 2018年第8期9卷 924-930页

作者： Li, Shicheng Sun, Xiao Miao, Shuncheng Lu, Tong Wang, Yuanyong Liu, Jia Jiao, Wenjie Qingdao Univ Affiliated Hosp Dept Thorac Surg 16 Jiangsu Rd Qingdao 266003 Peoples R China Qingdao Univ Sch Pharm 38 Dengzhou Rd Qingdao 266003 Peoples R China

BackgroundIncreasing evidence has demonstrated that circular RNAs (circRNAs) may play an important role in oncogenesis and tumor development;however, their role in lung adenocarcinoma (LUAD) remains unclear. We identified the differentially expressed circRNAs in LUAD and investigated the potential mechanisms for cancer progression. MethodsWe examined differentially expressed circRNAs in LUAD and paired normal tissues using downloaded circRNA microarrays from the Gene expression Omnibus. We constructed gene co-expression networks based on the degree of Pearson correlation to predict the critical circRNA in LUAD. Gene Ontology analysis was performed on the genes in the network. We observed one novel circRNA upregulated in LUAD, hsa_circ_0000792, as well as its potential sponged microRNA, miR-375. Subsequent real-time quantitative PCR was used to verify the bioinformatics analysis. ResultsSeveral circRNAs showed significantly different expression levels in LUAD tissues. Real-time quantitative PCR and further co-expression network analysis of 42 matched tissue samples showed a significant difference in expression between LUAD and normal tissues in hsa_circ_0000792 (P < 0.001). We built a network of hsa_circ_0000792-targeted miRNA gene interactions, including miR-375 and the corresponding messenger RNAs. Gene Ontology analysis revealed that hsa_circ_0000792 could participate in signal transduction and cell communication during LUAD development. Larger area under the curve by receiver operating characteristic curve analysis of hsa_circ_0000792 and miR-375 (0.815 and 0.772, respectively) in LUAD indicated greater potential as biomarkers. ConclusionsWe identified hsa_circ_0000792 as a potential LUAD biomarker;however, further studies are required to determine the mechanism of this circRNA in LUAD development.

关键词： Circular RNA lung adenocarcinoma microarray expression prognostic biomarker

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Modeling nonlinearity in dilution design microarray data

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BIOINFORMATICS 2007年第11期23卷 1339-1347页

作者： Zheng, Xiuwen Huang, Hung-Chung Li, Wenyuan Liu, Peng Li, Quan-Zhen Liu, Ying Univ Texas Dept Math Sci Richardson TX 75083 USA Univ Texas Dept Comp Sci Richardson TX 75083 USA Univ Texas Dept Mol & Cell Biol Richardson TX 75083 USA Iowa State Univ Dept Stat Ames IA 50011 USA Univ Texas Med Ctr Microarray Core Facil Dallas TX 75390 USA

Motivation: Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC. Results: We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent.

关键词： .intensities microarray data utilized dilution calibration quantified data analysis microarray expression microarray platforms linearly Modeling nonlinearity sources of nonlinearity RNA mixtures beta ratio adj-FC

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