--- _id: '698' abstract: - lang: eng text: '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. ' author: - first_name: Yejun full_name: Wang, Yejun last_name: Wang - first_name: Mallika full_name: Nagarajan, Mallika last_name: Nagarajan - first_name: Caroline full_name: Uhler, Caroline id: 49ADD78E-F248-11E8-B48F-1D18A9856A87 last_name: Uhler orcid: 0000-0002-7008-0216 - first_name: Gv full_name: Shivashankar, Gv last_name: Shivashankar citation: ama: Wang Y, Nagarajan M, Uhler C, Shivashankar G. Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression. Molecular Biology of the Cell. 2017;28(14):1997-2009. doi:10.1091/mbc.E16-12-0825 apa: Wang, Y., Nagarajan, M., Uhler, C., & Shivashankar, G. (2017). Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression. Molecular Biology of the Cell. American Society for Cell Biology. https://doi.org/10.1091/mbc.E16-12-0825 chicago: Wang, Yejun, Mallika Nagarajan, Caroline Uhler, and Gv Shivashankar. “Orientation and Repositioning of Chromosomes Correlate with Cell Geometry Dependent Gene Expression.” Molecular Biology of the Cell. American Society for Cell Biology, 2017. https://doi.org/10.1091/mbc.E16-12-0825. ieee: Y. Wang, M. Nagarajan, C. Uhler, and G. Shivashankar, “Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression,” Molecular Biology of the Cell, vol. 28, no. 14. American Society for Cell Biology, pp. 1997–2009, 2017. ista: Wang Y, Nagarajan M, Uhler C, Shivashankar G. 2017. Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression. Molecular Biology of the Cell. 28(14), 1997–2009. mla: Wang, Yejun, et al. “Orientation and Repositioning of Chromosomes Correlate with Cell Geometry Dependent Gene Expression.” Molecular Biology of the Cell, vol. 28, no. 14, American Society for Cell Biology, 2017, pp. 1997–2009, doi:10.1091/mbc.E16-12-0825. short: Y. Wang, M. Nagarajan, C. Uhler, G. Shivashankar, Molecular Biology of the Cell 28 (2017) 1997–2009. date_created: 2018-12-11T11:47:59Z date_published: 2017-07-07T00:00:00Z date_updated: 2021-01-12T08:11:17Z day: '07' ddc: - '519' department: - _id: CaUh doi: 10.1091/mbc.E16-12-0825 file: - access_level: open_access checksum: de01dac9e30970cfa6ae902480a4e04d content_type: application/pdf creator: system date_created: 2018-12-12T10:10:53Z date_updated: 2020-07-14T12:47:46Z file_id: '4844' file_name: IST-2017-892-v1+1_Mol._Biol._Cell-2017-Wang-1997-2009.pdf file_size: 1086097 relation: main_file file_date_updated: 2020-07-14T12:47:46Z has_accepted_license: '1' intvolume: ' 28' issue: '14' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-sa/4.0/ month: '07' oa: 1 oa_version: Published Version page: 1997 - 2009 project: - _id: 2530CA10-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Y 903-N35 name: 'Gaussian Graphical Models: Theory and Applications' publication: Molecular Biology of the Cell publication_identifier: issn: - '10591524' publication_status: published publisher: American Society for Cell Biology publist_id: '7001' pubrep_id: '892' quality_controlled: '1' scopus_import: 1 status: public title: Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 28 year: '2017' ... --- _id: '1208' abstract: - lang: eng text: 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. article_processing_charge: No author: - first_name: Piotr full_name: Zwiernik, Piotr last_name: Zwiernik - first_name: Caroline full_name: Uhler, Caroline id: 49ADD78E-F248-11E8-B48F-1D18A9856A87 last_name: Uhler orcid: 0000-0002-7008-0216 - first_name: Donald full_name: Richards, Donald last_name: Richards citation: ama: 'Zwiernik P, Uhler C, Richards D. Maximum likelihood estimation for linear Gaussian covariance models. Journal of the Royal Statistical Society Series B: Statistical Methodology. 2017;79(4):1269-1292. doi:10.1111/rssb.12217' apa: 'Zwiernik, P., Uhler, C., & Richards, D. (2017). Maximum likelihood estimation for linear Gaussian covariance models. Journal of the Royal Statistical Society. Series B: Statistical Methodology. Wiley-Blackwell. https://doi.org/10.1111/rssb.12217' chicago: 'Zwiernik, Piotr, Caroline Uhler, and Donald Richards. “Maximum Likelihood Estimation for Linear Gaussian Covariance Models.” Journal of the Royal Statistical Society. Series B: Statistical Methodology. Wiley-Blackwell, 2017. https://doi.org/10.1111/rssb.12217.' ieee: 'P. Zwiernik, C. Uhler, and D. Richards, “Maximum likelihood estimation for linear Gaussian covariance models,” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 79, no. 4. Wiley-Blackwell, pp. 1269–1292, 2017.' ista: 'Zwiernik P, Uhler C, Richards D. 2017. Maximum likelihood estimation for linear Gaussian covariance models. Journal of the Royal Statistical Society. Series B: Statistical Methodology. 79(4), 1269–1292.' mla: 'Zwiernik, Piotr, et al. “Maximum Likelihood Estimation for Linear Gaussian Covariance Models.” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 79, no. 4, Wiley-Blackwell, 2017, pp. 1269–92, doi:10.1111/rssb.12217.' short: 'P. Zwiernik, C. Uhler, D. Richards, Journal of the Royal Statistical Society. Series B: Statistical Methodology 79 (2017) 1269–1292.' date_created: 2018-12-11T11:50:43Z date_published: 2017-09-01T00:00:00Z date_updated: 2023-09-20T11:17:21Z day: '01' department: - _id: CaUh doi: 10.1111/rssb.12217 external_id: isi: - '000411712300012' intvolume: ' 79' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1408.5604 month: '09' oa: 1 oa_version: Submitted Version page: 1269 - 1292 project: - _id: 2530CA10-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Y 903-N35 name: 'Gaussian Graphical Models: Theory and Applications' publication: 'Journal of the Royal Statistical Society. Series B: Statistical Methodology' publication_identifier: issn: - '13697412' publication_status: published publisher: Wiley-Blackwell publist_id: '6142' quality_controlled: '1' scopus_import: '1' status: public title: Maximum likelihood estimation for linear Gaussian covariance models type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 79 year: '2017' ... --- _id: '1168' abstract: - lang: eng text: 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. acknowledgement: 'This work has been supported by the project ANR-2011-IS01-001-01 “DESIRE” and Austrian Science Fund (FWF) I833-N18. Open access funding is provided by the Austrian Science Fund (FWF). ' article_processing_charge: No author: - first_name: Elisa full_name: Perrone, Elisa id: 2A5F8724-F248-11E8-B48F-1D18A9856A87 last_name: Perrone orcid: 0000-0003-0370-9835 - first_name: Andreas full_name: Rappold, Andreas last_name: Rappold - first_name: Werner full_name: Müller, Werner last_name: Müller citation: ama: Perrone E, Rappold A, Müller W. D inf s optimality in copula models. Statistical Methods and Applications. 2017;26(3):403-418. doi:10.1007/s10260-016-0375-6 apa: Perrone, E., Rappold, A., & Müller, W. (2017). D inf s optimality in copula models. Statistical Methods and Applications. Springer. https://doi.org/10.1007/s10260-016-0375-6 chicago: Perrone, Elisa, Andreas Rappold, and Werner Müller. “D Inf s Optimality in Copula Models.” Statistical Methods and Applications. Springer, 2017. https://doi.org/10.1007/s10260-016-0375-6. ieee: E. Perrone, A. Rappold, and W. Müller, “D inf s optimality in copula models,” Statistical Methods and Applications, vol. 26, no. 3. Springer, pp. 403–418, 2017. ista: Perrone E, Rappold A, Müller W. 2017. D inf s optimality in copula models. Statistical Methods and Applications. 26(3), 403–418. mla: Perrone, Elisa, et al. “D Inf s Optimality in Copula Models.” Statistical Methods and Applications, vol. 26, no. 3, Springer, 2017, pp. 403–18, doi:10.1007/s10260-016-0375-6. short: E. Perrone, A. Rappold, W. Müller, Statistical Methods and Applications 26 (2017) 403–418. date_created: 2018-12-11T11:50:31Z date_published: 2017-08-01T00:00:00Z date_updated: 2023-09-20T11:25:09Z day: '01' ddc: - '519' department: - _id: CaUh doi: 10.1007/s10260-016-0375-6 external_id: isi: - '000407973200004' file: - access_level: open_access checksum: 0b2d1b647ca96e9ef13a14b8b6775e0f content_type: application/pdf creator: system date_created: 2018-12-12T10:16:13Z date_updated: 2020-07-14T12:44:37Z file_id: '5199' file_name: IST-2017-739-v1+2_10260_2016_375_MOESM1_ESM.pdf file_size: 56664 relation: main_file - access_level: open_access checksum: 3321ef34e02e28acfc427f77cf32812a content_type: application/pdf creator: system date_created: 2018-12-12T10:16:14Z date_updated: 2020-07-14T12:44:37Z file_id: '5200' file_name: IST-2017-739-v1+3_s10260-016-0375-6.pdf file_size: 688953 relation: main_file file_date_updated: 2020-07-14T12:44:37Z has_accepted_license: '1' intvolume: ' 26' isi: 1 issue: '3' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '08' oa: 1 oa_version: Submitted Version page: 403 - 418 publication: Statistical Methods and Applications publication_status: published publisher: Springer publist_id: '6189' pubrep_id: '739' quality_controlled: '1' scopus_import: '1' status: public title: D inf s optimality in copula models tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 26 year: '2017' ... --- _id: '1089' abstract: - lang: eng text: 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. article_processing_charge: No author: - first_name: Shaun full_name: Fallat, Shaun last_name: Fallat - first_name: Steffen full_name: Lauritzen, Steffen last_name: Lauritzen - first_name: Kayvan full_name: Sadeghi, Kayvan last_name: Sadeghi - first_name: Caroline full_name: Uhler, Caroline id: 49ADD78E-F248-11E8-B48F-1D18A9856A87 last_name: Uhler orcid: 0000-0002-7008-0216 - first_name: Nanny full_name: Wermuth, Nanny last_name: Wermuth - first_name: Piotr full_name: Zwiernik, Piotr last_name: Zwiernik citation: ama: Fallat S, Lauritzen S, Sadeghi K, Uhler C, Wermuth N, Zwiernik P. Total positivity in Markov structures. Annals of Statistics. 2017;45(3):1152-1184. doi:10.1214/16-AOS1478 apa: Fallat, S., Lauritzen, S., Sadeghi, K., Uhler, C., Wermuth, N., & Zwiernik, P. (2017). Total positivity in Markov structures. Annals of Statistics. Institute of Mathematical Statistics. https://doi.org/10.1214/16-AOS1478 chicago: Fallat, Shaun, Steffen Lauritzen, Kayvan Sadeghi, Caroline Uhler, Nanny Wermuth, and Piotr Zwiernik. “Total Positivity in Markov Structures.” Annals of Statistics. Institute of Mathematical Statistics, 2017. https://doi.org/10.1214/16-AOS1478. ieee: S. Fallat, S. Lauritzen, K. Sadeghi, C. Uhler, N. Wermuth, and P. Zwiernik, “Total positivity in Markov structures,” Annals of Statistics, vol. 45, no. 3. Institute of Mathematical Statistics, pp. 1152–1184, 2017. ista: Fallat S, Lauritzen S, Sadeghi K, Uhler C, Wermuth N, Zwiernik P. 2017. Total positivity in Markov structures. Annals of Statistics. 45(3), 1152–1184. mla: Fallat, Shaun, et al. “Total Positivity in Markov Structures.” Annals of Statistics, vol. 45, no. 3, Institute of Mathematical Statistics, 2017, pp. 1152–84, doi:10.1214/16-AOS1478. short: S. Fallat, S. Lauritzen, K. Sadeghi, C. Uhler, N. Wermuth, P. Zwiernik, Annals of Statistics 45 (2017) 1152–1184. date_created: 2018-12-11T11:50:05Z date_published: 2017-06-01T00:00:00Z date_updated: 2023-09-20T11:46:53Z day: '01' department: - _id: CaUh doi: 10.1214/16-AOS1478 external_id: isi: - '000404395900008' intvolume: ' 45' isi: 1 issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1510.01290 month: '06' oa: 1 oa_version: Submitted Version page: 1152 - 1184 project: - _id: 2530CA10-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Y 903-N35 name: 'Gaussian Graphical Models: Theory and Applications' publication: Annals of Statistics publication_identifier: issn: - '00905364' publication_status: published publisher: Institute of Mathematical Statistics publist_id: '6288' quality_controlled: '1' scopus_import: '1' status: public title: Total positivity in Markov structures type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 45 year: '2017' ... --- _id: '1293' abstract: - lang: eng text: For a graph G with p vertices the closed convex cone S⪰0(G) consists of all real positive semidefinite p×p matrices whose sparsity pattern is given by G, that is, those matrices with zeros in the off-diagonal entries corresponding to nonedges of G. The extremal rays of this cone and their associated ranks have applications to matrix completion problems, maximum likelihood estimation in Gaussian graphical models in statistics, and Gauss elimination for sparse matrices. While the maximum rank of an extremal ray in S⪰0(G), known as the sparsity order of G, has been characterized for different classes of graphs, we here study all possible extremal ranks of S⪰0(G). We investigate when the geometry of the (±1)-cut polytope of G yields a polyhedral characterization of the set of extremal ranks of S⪰0(G). For a graph G without K5 minors, we show that appropriately chosen normal vectors to the facets of the (±1)-cut polytope of G specify the off-diagonal entries of extremal matrices in S⪰0(G). We also prove that for appropriately chosen scalars the constant term of the linear equation of each facet-supporting hyperplane is the rank of its corresponding extremal matrix in S⪰0(G). Furthermore, we show that if G is series-parallel then this gives a complete characterization of all possible extremal ranks of S⪰0(G). Consequently, the sparsity order problem for series-parallel graphs can be solved in terms of polyhedral geometry. acknowledgement: We wish to thank Alexander Engström and Bernd Sturmfels for various valuable discussions and insights. We also thank the two anonymous referees for their thoughtful feedback on the paper. CU was partially supported by the Austrian Science Fund (FWF) Y 903-N35. author: - first_name: Liam T full_name: Solus, Liam T id: 2AADA620-F248-11E8-B48F-1D18A9856A87 last_name: Solus - first_name: Caroline full_name: Uhler, Caroline id: 49ADD78E-F248-11E8-B48F-1D18A9856A87 last_name: Uhler orcid: 0000-0002-7008-0216 - first_name: Ruriko full_name: Yoshida, Ruriko last_name: Yoshida citation: ama: Solus LT, Uhler C, Yoshida R. Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors. Linear Algebra and Its Applications. 2016;509:247-275. doi:10.1016/j.laa.2016.07.026 apa: Solus, L. T., Uhler, C., & Yoshida, R. (2016). Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors. Linear Algebra and Its Applications. Elsevier. https://doi.org/10.1016/j.laa.2016.07.026 chicago: Solus, Liam T, Caroline Uhler, and Ruriko Yoshida. “Extremal Positive Semidefinite Matrices Whose Sparsity Pattern Is given by Graphs without K5 Minors.” Linear Algebra and Its Applications. Elsevier, 2016. https://doi.org/10.1016/j.laa.2016.07.026. ieee: L. T. Solus, C. Uhler, and R. Yoshida, “Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors,” Linear Algebra and Its Applications, vol. 509. Elsevier, pp. 247–275, 2016. ista: Solus LT, Uhler C, Yoshida R. 2016. Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors. Linear Algebra and Its Applications. 509, 247–275. mla: Solus, Liam T., et al. “Extremal Positive Semidefinite Matrices Whose Sparsity Pattern Is given by Graphs without K5 Minors.” Linear Algebra and Its Applications, vol. 509, Elsevier, 2016, pp. 247–75, doi:10.1016/j.laa.2016.07.026. short: L.T. Solus, C. Uhler, R. Yoshida, Linear Algebra and Its Applications 509 (2016) 247–275. date_created: 2018-12-11T11:51:11Z date_published: 2016-11-15T00:00:00Z date_updated: 2021-01-12T06:49:40Z day: '15' department: - _id: CaUh doi: 10.1016/j.laa.2016.07.026 intvolume: ' 509' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/1506.06702.pdf month: '11' oa: 1 oa_version: Preprint page: 247 - 275 project: - _id: 2530CA10-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Y 903-N35 name: 'Gaussian Graphical Models: Theory and Applications' publication: Linear Algebra and Its Applications publication_status: published publisher: Elsevier publist_id: '6024' quality_controlled: '1' scopus_import: 1 status: public title: Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 509 year: '2016' ...