TY - JOUR
AB - Extracellular matrix signals from the microenvironment regulate gene expression patterns and cell behavior. Using a combination of experiments and geometric models, we demonstrate correlations between cell geometry, three-dimensional (3D) organization of chromosome territories, and gene expression. Fluorescence in situ hybridization experiments showed that micropatterned fibroblasts cultured on anisotropic versus isotropic substrates resulted in repositioning of specific chromosomes, which contained genes that were differentially regulated by cell geometries. Experiments combined with ellipsoid packing models revealed that the mechanosensitivity of chromosomes was correlated with their orientation in the nucleus. Transcription inhibition experiments suggested that the intermingling degree was more sensitive to global changes in transcription than to chromosome radial positioning and its orientations. These results suggested that cell geometry modulated 3D chromosome arrangement, and their neighborhoods correlated with gene expression patterns in a predictable manner. This is central to understanding geometric control of genetic programs involved in cellular homeostasis and the associated diseases.
AU - Wang, Yejun
AU - Nagarajan, Mallika
AU - Uhler, Caroline
AU - Shivashankar, Gv
ID - 698
IS - 14
JF - Molecular Biology of the Cell
SN - 10591524
TI - Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression
VL - 28
ER -
TY - JOUR
AB - We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian models with linear constraints on the covariance matrix. Maximum likelihood estimation for this class of models leads to a non-convex optimization problem which typically has many local maxima. Using recent results on the asymptotic distribution of extreme eigenvalues of the Wishart distribution, we provide sufficient conditions for any hill climbing method to converge to the global maximum. Although we are primarily interested in the case in which n≫p, the proofs of our results utilize large sample asymptotic theory under the scheme n/p→γ>1. Remarkably, our numerical simulations indicate that our results remain valid for p as small as 2. An important consequence of this analysis is that, for sample sizes n≃14p, maximum likelihood estimation for linear Gaussian covariance models behaves as if it were a convex optimization problem. © 2016 The Royal Statistical Society and Blackwell Publishing Ltd.
AU - Zwiernik, Piotr
AU - Uhler, Caroline
AU - Richards, Donald
ID - 1208
IS - 4
JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology
SN - 13697412
TI - Maximum likelihood estimation for linear Gaussian covariance models
VL - 79
ER -
TY - JOUR
AB - We discuss properties of distributions that are multivariate totally positive of order two (MTP2) related to conditional independence. In particular, we show that any independence model generated by an MTP2 distribution is a compositional semigraphoid which is upward-stable and singleton-transitive. In addition, we prove that any MTP2 distribution satisfying an appropriate support condition is faithful to its concentration graph. Finally, we analyze factorization properties of MTP2 distributions and discuss ways of constructing MTP2 distributions; in particular we give conditions on the log-linear parameters of a discrete distribution which ensure MTP2 and characterize conditional Gaussian distributions which satisfy MTP2.
AU - Fallat, Shaun
AU - Lauritzen, Steffen
AU - Sadeghi, Kayvan
AU - Uhler, Caroline
AU - Wermuth, Nanny
AU - Zwiernik, Piotr
ID - 1089
IS - 3
JF - Annals of Statistics
SN - 00905364
TI - Total positivity in Markov structures
VL - 45
ER -
TY - JOUR
AB - Optimum experimental design theory has recently been extended for parameter estimation in copula models. The use of these models allows one to gain in flexibility by considering the model parameter set split into marginal and dependence parameters. However, this separation also leads to the natural issue of estimating only a subset of all model parameters. In this work, we treat this problem with the application of the (Formula presented.)-optimality to copula models. First, we provide an extension of the corresponding equivalence theory. Then, we analyze a wide range of flexible copula models to highlight the usefulness of (Formula presented.)-optimality in many possible scenarios. Finally, we discuss how the usage of the introduced design criterion also relates to the more general issue of copula selection and optimal design for model discrimination.
AU - Perrone, Elisa
AU - Rappold, Andreas
AU - Müller, Werner
ID - 1168
IS - 3
JF - Statistical Methods and Applications
TI - D inf s optimality in copula models
VL - 26
ER -
TY - JOUR
AB - Relational models for contingency tables are generalizations of log-linear models, allowing effects associated with arbitrary subsets of cells in the table, and not necessarily containing the overall effect, that is, a common parameter in every cell. Similarly to log-linear models, relational models can be extended to non-negative distributions, but the extension requires more complex methods. An extended relational model is defined as an algebraic variety, and it turns out to be the closure of the original model with respect to the Bregman divergence. In the extended relational model, the MLE of the cell parameters always exists and is unique, but some of its properties may be different from those of the MLE under log-linear models. The MLE can be computed using a generalized iterative scaling procedure based on Bregman projections.
AU - Klimova, Anna
AU - Rudas, Tamás
ID - 1833
JF - Journal of Multivariate Analysis
TI - On the closure of relational models
VL - 143
ER -