Identifying differential correlation in gene/pathway combinations

Braun, Rosemary; Cope, Leslie; Parmigiani, Giovanni

doi:10.1186/1471-2105-9-488

Cited by 26 publications

(23 citation statements)

References 31 publications

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“…Several groups have developed formal methods to analyze differential correlation (reviewed in (de la Fuente, 2010)). Some methods determine whether pre-specified gene sets are differently correlated in two conditions (Braun et al, 2008; Choi and Kendziorski, 2009), while others discover gene sets directly from the data (Watson, 2006). We used CARMEN to calculate differential correlation between tail and dorsal telogen tissues ( Methods ).…”

Section: Resultsmentioning

confidence: 99%

Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer

et al. 2016

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SUMMARY Inherited germline polymorphisms can cause gene expression levels in normal tissues to differ substantially between individuals. We present an analysis of the genetic architecture of normal adult skin from 470 genetically unique mice, demonstrating the effect of germline variants, skin tissue location, and perturbation by exogenous inflammation or tumorigenesis on gene signaling pathways. Gene networks related to specific cell types and signaling pathways including Sonic Hedgehog (Shh), Wnt, Lgr family stem cell markers, and keratins differed at these tissue sites, suggesting mechanisms for the differential susceptibility of dorsal and tail skin to development of skin diseases and tumorigenesis. The Pten tumor suppressor gene network is rewired in premalignant tumors compared to normal tissue, but this response to perturbation is lost during malignant progression. We present a software package for eQTL network analysis and demonstrate how network analysis of whole tissues provides insights into interactions between cell compartments and signaling molecules.

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Section: Resultsmentioning

confidence: 99%

Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer

et al. 2016

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“…This extension would, however, come at a substantial computational cost and would require a challenging reformulation of the prior over graphs

p (G)

, to penalize for model complexity and, at the same time, to favor models closer to the structure of the prior pathway

G_{0}

. Initial progress in this direction was reported by Braun, Cope and Parmigiani (2008) and, in the context of Bayesian Networks, by Mukherjee and Speed (2008) and it is the subject of active research.…”

Section: Discussionmentioning

confidence: 99%

Modeling dependent gene expression

Telesca¹,

Müller²,

Parmigiani³

et al. 2012

Ann. Appl. Stat.

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In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.

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“…Extending this idea, we proposed a method [62] in which the pathway summary values and genes not known to be on the pathway were tested for differential correlation. In the “Gene×Pathway Correlation (GPC) Score” method [62], we first computed pathway summary values based on the first PC for every pathway of interest, yielding for each pathway j a value p j,m summarizing sample m ’s expression across pathway j ’s genes.…”

Section: 3 Identifying Functional Modulesmentioning

confidence: 99%

“…In the “Gene×Pathway Correlation (GPC) Score” method [62], we first computed pathway summary values based on the first PC for every pathway of interest, yielding for each pathway j a value p j,m summarizing sample m ’s expression across pathway j ’s genes. For each gene i with expression g i,m in sample m , we compute the GPC-score as the difference in the correlations of g and p in the case and control phenotype,…”

Section: 3 Identifying Functional Modulesmentioning

confidence: 99%

“…1.4. Although this method was applied in [62] exclusively to mRNA expression data, the same technique may be applied as an integrative analysis using both mRNA other genomic or environmental measurements. For instance, one can apply it to combined miRNA/mRNA data to search for differentially correlated miRNA–pathway pairs, thereby identifying miRNAs whose expression modulates the activity of regulatory networks.…”

Section: 3 Identifying Functional Modulesmentioning

confidence: 99%

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Systems Analysis of High-Throughput Data

Braun

2014

Advances in Experimental Medicine and Biology

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Modern high–throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high–dimensional data and the development of systems–level models of cellular signaling and regulation. Specifically, we focus on multi–gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene–wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

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Identifying differential correlation in gene/pathway combinations

Cited by 26 publications

References 31 publications

Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer

Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer

Modeling dependent gene expression

Systems Analysis of High-Throughput Data

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