The Experts below are selected from a list of 25833 Experts worldwide ranked by ideXlab platform
Pablo Tamayo - One of the best experts on this subject based on the ideXlab platform.
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a method for downstream analysis of Gene Set Enrichment results facilitates the biological interpretation of vaccine efficacy studies
bioRxiv, 2016Co-Authors: Y Tan, Pablo Tamayo, Jill P. Mesirov, Jernej Godec, William HainingAbstract:Gene Set Enrichment analysis (GSEA) is a widely employed method for analyzing Gene expression profiles. The approach uses annotated Sets of Genes, identifies those that are coordinately up- or down-regulated in a biological comparison of interest, and thereby elucidates underlying biological processes relevant to the comparison. As the number of Gene Sets available in various collections for Enrichment analysis has grown, the resulting lists of significant differentially regulated Gene Sets may also become larger, leading to the need for additional downstream analysis of GSEA results. Here we present a method that allows the rapid identification of a small number of co-regulated groups of Genes - "leading edge metaGenes" (LEMs) - from high scoring Sets in GSEA results. LEM are sub-signatures which are common to multiple Gene Sets and that "explain" their Enrichment specific to the experimental dataSet of interest. We show that LEMs contain more refined lists of context-dependent and biologically meaningful Genes than the parental Gene Sets. LEM analysis of the human vaccine response using a large database of immune signatures identified core biological processes induced by five different vaccines in dataSets from human peripheral blood mononuclear cells (PBMC). Further study of these biological processes over time following vaccination showed that at day 3 post-vaccination, vaccines derived from viruses or viral subunits exhibit patterns of biological processes that are distinct from protein conjugate vaccines; however, by day 7 these differences were less pronounced. This suggests that the immune response to diverse vaccines eventually converge to a common transcriptional response. LEM analysis can significantly reduce the dimensionality of enriched Gene Sets, improve the identification of core biological processes active in a comparison of interest, and simplify the biological interpretation of GSEA results.
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the limitations of simple Gene Set Enrichment analysis assuming Gene independence
Statistical Methods in Medical Research, 2016Co-Authors: Pablo Tamayo, Arthur Liberzon, George Steinhardt, Jill P. MesirovAbstract:Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess Enrichment and ignoring Gene-Gene correlations was proposed by Irizarry et al. 2009 as a serious contender. The argument criticizes Gene Set Enrichment Analysis’s nonparametric nature and its use of an empirical null distribution as unnecessary and hard to compute. We refute these claims by careful consideration of the assumptions of the simplified method and its results, including a comparison with Gene Set Enrichment Analysis’s on a large benchmark Set of 50 dataSets. Our results provide strong empirical evidence that Gene–Gene correlations cannot be ignored due to the significant variance inflation they produced on the Enrichment scores and should be taken into account when estimating Gene Set Enrichment significance. In addition, we discuss the challenges th...
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the molecular signatures database hallmark Gene Set collection
Cell systems, 2015Co-Authors: Arthur Liberzon, Chet Birger, Helga Thorvaldsdottir, Pablo Tamayo, Mahmoud Ghandi, Jill P. MesirovAbstract:Summary The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of Gene Sets for performing Gene Set Enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 Gene Sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heteroGeneity within, Gene Sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" Gene Sets as part of MSigDB. Each hallmark in this collection consists of a "refined" Gene Set, derived from multiple "founder" Sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder Sets and, by reducing both variation and redundancy, provide more refined and concise inputs for Gene Set Enrichment analysis.
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constellation map downstream visualization and interpretation of Gene Set Enrichment results
F1000Research, 2015Co-Authors: Pablo Tamayo, Jill P. Mesirov, Yan Tan, Nicholas W HainingAbstract:Summary: Gene Set Enrichment analysis (GSEA) approaches are widely used to identify coordinately regulated Genes associated with phenotypes of interest. Here, we present Constellation Map, a tool to visualize and interpret the results when Enrichment analyses yield a long list of significantly enriched Gene Sets. Constellation Map identifies commonalities that explain the Enrichment of multiple top-scoring Gene Sets and maps the relationships between them. Constellation Map can help investigators take full advantage of GSEA and facilitates the biological interpretation of Enrichment results. Availability: Constellation Map is freely available as a GenePattern module at http://www.Genepattern.org .
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the limitations of simple Gene Set Enrichment analysis assuming Gene independence
Journal of Biomedical Informatics, 2011Co-Authors: Pablo Tamayo, Arthur Liberzon, George Steinhardt, Jill P. MesirovAbstract:Since its first publication in 2003, the Gene Set Enrichment Analysis (GSEA) method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach, using a one sample t test score to assess Enrichment and ignoring Gene-Gene correlations was proposed by Irizarry et al. 2009 as a serious contender. The argument criticizes GSEA's nonparametric nature and its use of an empirical null distribution as unnecessary and hard to compute. We refute these claims by careful consideration of the assumptions of the simplified method and its results, including a comparison with GSEA's on a large benchmark Set of 50 dataSets. Our results provide strong empirical evidence that Gene-Gene correlations cannot be ignored due to the significant variance inflation they produced on the Enrichment scores and should be taken into account when estimating Gene Set Enrichment significance. In addition, we discuss the challenges that the complex correlation structure and multi-modality of Gene Sets pose more Generally for Gene Set Enrichment methods.
Kwangsik Nho - One of the best experts on this subject based on the ideXlab platform.
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genome wide association analysis of hippocampal volume identifies Enrichment of neuroGenesis related pathways
Scientific Reports, 2019Co-Authors: Emrin Horgusluoglumoloch, Shannon L Risacher, Paul K Crane, Derrek P Hibar, Paul M Thompson, Andrew J Saykin, Kwangsik Nho, Alzheimers Disease Neuroimaging InitiativeAbstract:Adult neuroGenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neuroGenesis-related pathways are associated with hippocampal volume, we performed Gene-Set Enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer’s Disease Neuroimaging Initiative Cohort (ADNI). Gene-Set Enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neuroGenesis-related processes including neuroGenesis, Generation of new neurons, neuronal development, and neuronal migration and differentiation. For Genes highly represented in the significantly enriched neuroGenesis-related pathways, Gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of Gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neuroGenesis related pathways. Our results suggest that neuroGenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neuroGenesis.
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Genome-wide association analysis of hippocampal volume identifies Enrichment of neuroGenesis-related pathways
Scientific Reports, 2019Co-Authors: Emrin Horgusluoglu-moloch, Shannon L Risacher, Paul K Crane, Derrek P Hibar, Paul M Thompson, Andrew J Saykin, Kwangsik NhoAbstract:Adult neuroGenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neuroGenesis-related pathways are associated with hippocampal volume, we performed Gene-Set Enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer’s Disease Neuroimaging Initiative Cohort (ADNI). Gene-Set Enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p -value
Amy K Walker - One of the best experts on this subject based on the ideXlab platform.
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wormcat an online tool for annotation and visualization of caenorhabditis elegans genome scale data
Genetics, 2020Co-Authors: Amy D Holdorf, Daniel P Higgins, Anne C Hart, Peter R Boag, Gregory J Pazour, Albertha J M Walhout, Amy K WalkerAbstract:The emergence of large Gene expression dataSets has revealed the need for improved tools to identify enriched Gene categories and visualize Enrichment patterns. While Gene ontogeny (GO) provides a valuable tool for Gene Set Enrichment analysis, it has several limitations. First, it is difficult to graph multiple GO analyses for comparison. Second, Genes from some model systems are not well represented. For example, ∼30% of Caenorhabditis elegans Genes are missing from the analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans Gene Sets in different types of genome-scale data, we developed WormCat, a web-based tool that uses a near-complete annotation of the C. elegans genome to identify coexpressed Gene Sets and scaled heat map for Enrichment visualization. We tested the performance of WormCat using a variety of published transcriptomic dataSets, and show that it reproduces major categories identified by GO. Importantly, we also found previously unidentified categories that are informative for interpreting phenotypes or predicting biological function. For example, we analyzed published RNA-seq data from C. elegans treated with combinations of lifespan-extending drugs, where one combination paradoxically shortened lifespan. Using WormCat, we identified sterol metabolism as a category that was not enriched in the single or double combinations, but emerged in a triple combination along with the lifespan shortening. Thus, WormCat identified a Gene Set with potential. phenotypic relevance not found with previous GO analysis. In conclusion, WormCat provides a powerful tool for the analysis and visualization of Gene Set Enrichment in different types of C. elegans dataSets.
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wormcat an online tool for annotation and visualization of caenorhabditis elegans genome scale data
bioRxiv, 2019Co-Authors: Amy D Holdorf, Daniel P Higgins, Anne C Hart, Peter R Boag, Gregory J Pazour, Albertha J M Walhout, Amy K WalkerAbstract:The emergence of large Gene expression dataSets has revealed the need for improved tools to identify enriched Gene categories and visualize Enrichment patterns. While Gene Ontogeny (GO) provides a valuable tool for Gene Set Enrichment analysis, it has several limitations. First, it is difficult to graphically compare multiple GO analyses. Second, Genes from some model systems are not well represented. For example, around 30% of Caenorhabditis elegans Genes are missing from analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans Gene Sets in different types of genome-scale data, we developed WormCat, a web-based tool that uses a near-complete annotation of the C. elegans genome to identify co-expressed Gene Sets and scaled heat map for Enrichment visualization. We tested the performance of WormCat using a variety of published transcriptomic dataSets and show that it reproduces major categories identified by GO. Importantly, we also found previously unidentified categories that are informative for interpreting phenotypes or predicting biological function. For example, we analyzed published RNA-seq data from C. elegans treated with combinations of lifespan-extending drugs where one combination paradoxically shortened lifespan. Using WormCat, we identified sterol metabolism as a category that was not enriched in the single or double combinations but emerged in a triple combination along with the lifespan shortening. Thus, WormCat identified a Gene Set with potential phenotypic relevance that was not uncovered with previous GO analysis. In conclusion, WormCat provides a powerful tool for the analysis and visualization of Gene Set Enrichment in different types of C. elegans dataSets.
Eric S. Lander - One of the best experts on this subject based on the ideXlab platform.
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from the cover Gene Set Enrichment analysis a knowledge based approach for interpreting genome wide expression profiles
Proceedings of the National Academy of Sciences of the United States of America, 2005Co-Authors: A. Subramanian, Michael A Gillette, Todd R Golub, Pablo Tamayo, A. Paulovich, Vamsi K Mootha, S Mukherjee, Scott L Pomeroy, Benjamin L Ebert, Eric S. LanderAbstract:Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting Gene expression data. The method derives its power by focusing on Gene Sets, that is, groups of Genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data Sets, including leukemia and lung cancer. Notably, where single-Gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined Gene Sets.
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Gene Set Enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Proceedings of the National Academy of Sciences, 2005Co-Authors: A. Subramanian, Michael A Gillette, Todd R Golub, Pablo Tamayo, A. Paulovich, Vamsi K Mootha, S Mukherjee, Scott L Pomeroy, Eric S. LanderAbstract:Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting Gene expression data. The method derives its power by focusing on Gene Sets, that is, groups of Genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data Sets, including leukemia and lung cancer. Notably, where single-Gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined Gene Sets.
Alzheimers Disease Neuroimaging Initiative - One of the best experts on this subject based on the ideXlab platform.
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genome wide association analysis of hippocampal volume identifies Enrichment of neuroGenesis related pathways
Scientific Reports, 2019Co-Authors: Emrin Horgusluoglumoloch, Shannon L Risacher, Paul K Crane, Derrek P Hibar, Paul M Thompson, Andrew J Saykin, Kwangsik Nho, Alzheimers Disease Neuroimaging InitiativeAbstract:Adult neuroGenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neuroGenesis-related pathways are associated with hippocampal volume, we performed Gene-Set Enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer’s Disease Neuroimaging Initiative Cohort (ADNI). Gene-Set Enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neuroGenesis-related processes including neuroGenesis, Generation of new neurons, neuronal development, and neuronal migration and differentiation. For Genes highly represented in the significantly enriched neuroGenesis-related pathways, Gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of Gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neuroGenesis related pathways. Our results suggest that neuroGenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neuroGenesis.