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Dinu I.,University of Alberta | Wang X.,University of Alberta | Kelemen L.E.,University of Calgary | Vatanpour S.,University of Alberta | And 2 more authors.
BMC Bioinformatics | Year: 2013

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.Results: We have designed a new method by extending the GSA approach called Linear Combination Test (LCT) from a binary to a continuous phenotype. Simulation studies and a real microarray dataset were used to compare the proposed LCT for a continuous phenotype, a modification of LCT (referred to as LCT2), and two publicly available GSA methods for continuous phenotypes.Conclusions: We found that the LCT methods performed better than the other two GSA methods; however, this finding should be understood in the context of our specific simulation studies and the real microarray dataset that were used to compare the methods. Free R-codes to perform LCT for binary and continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html. The R-code to perform LCT for a continuous phenotype is available as Additional file 1. © 2013 Dinu et al.; licensee BioMed Central Ltd.


Priolo C.,Dana-Farber Cancer Institute | Priolo C.,Harvard University | Pyne S.,Dana-Farber Cancer Institute | Pyne S.,Cr Rao Advanced Institute Of Mathematics | And 15 more authors.
Cancer Research | Year: 2014

Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry-based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our fi ndings show how prostate tumors undergo a metabolic reprogramming that refl ects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics. ©2014 AACR.


Wang X.,University of Alberta | Pyne S.,CR Rao Advanced Institute of Mathematics | Pyne S.,Public Health Foundation of India | Dinu I.,University of Alberta
BMC Bioinformatics | Year: 2014

Background: Gene set analysis (GSA) methods test the association of sets of genes with phenotypes in gene expression microarray studies. While GSA methods on a single binary or categorical phenotype abounds, little attention has been paid to the case of a continuous phenotype, and there is no method to accommodate correlated multiple continuous phenotypes.Result: We propose here an extension of the linear combination test (LCT) to its new version for multiple continuous phenotypes, incorporating correlations among gene expressions of functionally related gene sets, as well as correlations among multiple phenotypes. Further, we extend our new method to its nonlinear version, referred as nonlinear combination test (NLCT), to test potential nonlinear association of gene sets with multiple phenotypes. Simulation study and a real microarray example demonstrate the practical aspects of the proposed methods.Conclusion: The proposed approaches are effective in controlling type I errors and powerful in testing associations between gene-sets and multiple continuous phenotypes. They are both computationally effective. Naively (univariately) analyzing a group of multiple correlated phenotypes could be dangerous. R-codes to perform LCT and NLCT for multiple continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html. © 2014 Wang et al.; licensee BioMed Central Ltd.


Ahfock D.,University of Queensland | Pyne S.,Public Health Foundation of India | Pyne S.,CR Rao Advanced Institute of Mathematics | Lee S.X.,University of Queensland | McLachlan G.J.,University of Queensland
Computational Statistics and Data Analysis | Year: 2016

The statistical matching problem involves the integration of multiple datasets where some variables are not observed jointly. This missing data pattern leaves most statistical models unidentifiable. Statistical inference is still possible when operating under the framework of partially identified models, where the goal is to bound the parameters rather than to estimate them precisely. In many matching problems, developing feasible bounds on the parameters is equivalent to finding the set of positive-definite completions of a partially specified covariance matrix. Existing methods for characterising the set of possible completions do not extend to high-dimensional problems. A Gibbs sampler to draw from the set of possible completions is proposed. The variation in the observed samples gives an estimate of the feasible region of the parameters. The Gibbs sampler extends easily to high-dimensional statistical matching problems. © 2016 The Authors


Oldfield A.,U.S. National Institutes of Health | Yang P.,U.S. National Institutes of Health | Conway A.,U.S. National Institutes of Health | Cinghu S.,U.S. National Institutes of Health | And 3 more authors.
Molecular Cell | Year: 2014

Cell type-specific master transcription factors (TFs) play vital roles in defining cell identity and function. However, the roles ubiquitous factors play in the specification of cell identity remain underappreciated. Here we show that the ubiquitous CCAAT-binding NF-Y complex is required for the maintenance ofembryonic stem cell (ESC) identity and is an essential component of the core pluripotency network. Genome-wide studies in ESCs and neurons reveal that NF-Y regulates not only genes with housekeeping functions through cell type-invariant promoter-proximal binding, but also genes required for cell identity by binding to cell type-specific enhancers with master TFs. Mechanistically, NF-Y's distinct DNA-binding mode promotes master/pioneer TF binding at enhancers by facilitating a permissive chromatin conformation. Our studies unearth a conceptually unique function for histone-fold domain (HFD) protein NF-Y in promoting chromatin accessibility and suggest that other HFD proteins with analogous structural and DNA-binding properties may function in similar ways. © 2014 Elsevier Inc.


Oldfield AndrewJ.,U.S. National Institutes of Health | Yang P.,U.S. National Institutes of Health | Conway AmandaE.,U.S. National Institutes of Health | Cinghu S.,U.S. National Institutes of Health | And 3 more authors.
Molecular Cell | Year: 2014

Cell type-specific master transcription factors (TFs) play vital roles in defining cell identity and function. However, the roles ubiquitous factors play in the specification of cell identity remain underappreciated. Here we show that the ubiquitous CCAAT-binding NF-Y complex is required for the maintenance of embryonic stem cell (ESC) identity and is an essential component of the core pluripotency network. Genome-wide studies in ESCs and neurons reveal that NF-Y regulates not only genes with housekeeping functions through cell type-invariant promoter-proximal binding, but also genes required for cell identity by binding to cell type-specific enhancers with master TFs. Mechanistically, NF-Y's distinct DNA-binding mode promotes master/pioneer TF binding at enhancers by facilitating a permissive chromatin conformation. Our studies unearth a conceptually unique function for histone-fold domain (HFD) protein NF-Y in promoting chromatin accessibility and suggest that other HFD proteins with analogous structural and DNA-binding properties may function in similar ways. © 2014 Elsevier Inc. All rights reserved.


Bhattacharyya A.,RCI Inc | Saraswat V.K.,Defence Research and Development Organization | Manimaran P.,CR Rao Advanced Institute of Mathematics | Rao S.B.,CR Rao Advanced Institute of Mathematics
Applied Soft Computing Journal | Year: 2015

In this paper, using the Dempster-Shafer theory (DST) of evidence, a new decision criterion is proposed which can quickly classify airborne objects without any a priori knowledge, whose data are laced with environmental noise characteristics, within 10 seconds (10 s) from the time it is detected. Kinematic parameters of an airborne object received from radars are used to classify it into one of the six classes, which include three levels of ballistic target discrimination, aerodynamic, satellite and unknown. The DST is chosen as it can suitably handle the element of uncertainty, limited a priori data and short observation times that exist with the data acquired for the purpose of classification. The focus of the work is on ballistic targets in a theater of war. The approach is compared with the popularly known k-NN and decision tree techniques and is found to perform better with the chosen data sets. This approach is tested using both real flight test data and simulated data. © 2015 Elsevier B.V. All rights reserved.


Bennett R.,Harvard University | Ysasi A.,Harvard University | Belle J.,Harvard University | Wagner W.,Johannes Gutenberg University Mainz | And 4 more authors.
Frontiers in Oncology | Year: 2014

Complex tissues such as the lung are composed of structural hierarchies such as alveoli, alveolar ducts and lobules. Some structural units, such as the alveolar duct, appear to participate in tissue repair as well as the development of bronchioalveolar carcinoma. Here, we demonstrate an approach to conduct laser microdissection of the lung alveolar duct for singlecell PCR analysis. Our approach involved three steps. 1) The initial preparation used mechanical sectioning of the lung tissue with sufficient thickness to encompass the structure of interest. In the case of the alveolar duct, the precision-cut lung slices were 200um thick; the slices were processed using near-physiologic conditions to preserve the state of viable cells. 2) The lung slices were examined by transmission light microscopy to target the alveolar duct. The air-filled lung was sufficiently accessible by light microscopy that counterstains or fluorescent labels were unnecessary to identify the alveolar duct. 3) The enzymatic and microfluidic isolation of single cells allowed for the harvest of as few as several thousand cells for PCR analysis. Microfluidics based arrays were used to measure the expression of selected marker genes in individual cells to characterize different cell populations. Preliminary work suggests the unique value of this approach to understanding the intra- and intercellular interactions within the regenerating alveolar duct. © 2014 Bennett, Ysasi, Belle, Wagner, Konerding, Blainey, Pyne and Mentzer.


Azad A.,Purdue University | Khan A.,Purdue University | Rajwa B.,Purdue University | Pyne S.,Cr Rao Advanced Institute Of Mathematics | Pothen A.,Purdue University
2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 | Year: 2013

We describe an algorithm to dynamically classify flow cytometry data samples into several classes based on their immunophenotypes. Flow cytometry data consists of fluorescence measurements of several proteins that characterize different cell types in blood or cultured cell lines. Each sample is initially clustered to identify the cell populations present in it. Using a combinatorial dissimilarity measure between cell populations in samples, we compute meta-clusters that correspond to the same cell population across samples. The collection of meta-clusters in a class of samples then de- scribes a template for that class. We organize the samples into a template tree, and use it to classify new samples into existing classes or create a new class if needed. We dynamically update the templates and their statistical parameters as new samples are classified, so that the new information is reflected in the classes. We use our dynamic classification algorithm to classify T cells that on stimulation with an antibody show increased abundance of the proteins SLP- 76 and ZAP-70. These proteins are involved in a platform that assembles signaling proteins in the immune response. We also use the algorithm to show that variation in an immune subsystem between individuals is a larger effect than variation in multiple samples from one individual. Copyright © 2007 by the Association for Computing Machinery.


Prakasa Rao B.L.S.,CR Rao Advanced Institute of Mathematics
Physica A: Statistical Mechanics and its Applications | Year: 2016

It has been observed that the stock price process can be modeled with driving force as a mixed fractional Brownian motion with Hurst index H>34 whenever long-range dependence is possibly present. We obtain a closed form expression for the price of a geometric Asian option under the mixed fractional Brownian motion environment. We consider also Asian power options when the payoff function is a power function. © 2015 Elsevier B.V. All rights reserved.

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