Gene-set analysis and reduction

Dinu, Irina; Potter, John D.; Mueller, Thomas; Liu, Qi; Adewale, Adeniyi J.; Jhangri, Gian S.; Einecke, G.; Famulski, Konrad S.; Halloran, Philip F.; Yasui, Yutaka

doi:10.1093/bib/bbn042

Cited by 84 publications

(88 citation statements)

References 29 publications

(55 reference statements)

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“…GSEA is one among a family of techniques that can summarize differential expression at the level of gene sets. 12 GSEA is widely used, generates detailed information on the results, and has shown very good performance in a comparison of methods that compute enrichment at the level of gene sets. 13 Further, GSEA has been used to identify pathways involved in liver toxicity in human hepatoblastoma cells.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Genome-Wide Transcriptional Profiles of Primary Hepatocytes in Collagen Sandwich and Monolayer Cultures

Kim

Lasher

Milford

et al. 2010

Tissue Engineering Part C: Methods

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Two commonly used culture systems in hepatic tissue engineering are the collagen sandwich (CS) and monolayers of cells. In this study, genome-wide gene expression profiles of primary hepatocytes were measured over an 8-day period for each cell culture system using Affymetrix GeneChips and compared via gene set enrichment analysis to elicit biologically meaningful information at the level of gene sets. Our results demonstrate that gene expression in hepatocytes in CS cultures steadily and comprehensively diverges from that in monolayer cultures. Gene sets up-regulated in CS cultures include several associated with liver metabolic and synthesis functions, such as metabolism of lipids, amino acids, carbohydrates, and alcohol, and synthesis of bile acids. Monooxygenases such as Cytochrome-P450 enzymes do not show any change between the culture systems after 1 day, but exhibit significant up-regulation in CS cultures after 3 days in comparison to hepatocyte monolayers. These data provide insights into the up-and down-regulation of several liver-critical gene sets and their subsequent effects on liver-specific functions. These results provide a baseline for further explorations into the systems biology of engineered liver mimics.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Genome-Wide Transcriptional Profiles of Primary Hepatocytes in Collagen Sandwich and Monolayer Cultures

Kim

Lasher

Milford

et al. 2010

Tissue Engineering Part C: Methods

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“…In such cases, post-hoc analysis can be used to reduce the gene set to a core sub-set associated with the phenotype. Such an analysis has been reported in simulations and in a real example for a binary phenotype [4].…”

Section: Discussionmentioning

confidence: 93%

1147 Linear Combination Test for Gene Set Analysis of a Continuous Phenotype

Dinu¹,

Wang²,

Pyne³

2012

European Journal of Cancer

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Background: Gene set analysis (GSA) methods test the association of sets of genes with a phenotype in gene expression microarray studies. Many GSA methods have been proposed, especially methods for use with a binary phenotype. Equally, if not more importantly however, is the ability to test the enrichment of a gene signature or pathway against the continuous phenotypes which are routinely and commonly observed in, for example, clinicopathological measurements. It is not always easy or meaningful to dichotomize continuous phenotypes into two classes, and attempting to do this may lead to the inaccurate classification of samples, which would affect the downstream enrichment analysis. In the present study, we have build on recent efforts to incorporate correlation structure within gene sets and pathways into the GSA test statistic. To address the issue of continuous phenotypes directly without the need for artificial discrete classification and thus increase the power of the test while ensuring computational efficiency and rigor, new GSA methods that can incorporate a covariance matrix estimator for a continuous phenotype may present an effective approach.

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“…Some methods were developed to select subsets of genes simultaneously with the generation of gene set statistics [45][46] , utilize covariance structure in testing 47 , or incorporate regulatory network connectivity 48 . Some of the gene set analysis methods have been previously reviewed and compared [38][39][40][49][50][51][52] . Most of the gene set differential expression analysis methods use permutation tests to assess the significance of gene sets.…”

Section: Pathway/metabolite Set Testingmentioning

confidence: 99%

Analyzing LC/MS Metabolic Profiling Data in the Context of Existing Metabolic Networks

Yu¹,

Bai²

2012

CMB

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Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.

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Gene-set analysis and reduction

Cited by 84 publications

References 29 publications

A Comparative Study of Genome-Wide Transcriptional Profiles of Primary Hepatocytes in Collagen Sandwich and Monolayer Cultures

A Comparative Study of Genome-Wide Transcriptional Profiles of Primary Hepatocytes in Collagen Sandwich and Monolayer Cultures

1147 Linear Combination Test for Gene Set Analysis of a Continuous Phenotype

Analyzing LC/MS Metabolic Profiling Data in the Context of Existing Metabolic Networks

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