Testing for equal correlation matrices with application to paired gene expression data

We present a novel method for testing the hypothesis of equality of two correlation matrices using paired high-dimensional datasets. We consider test statistics based on the average of squares, maximum and sum of exceedances of Fisher transform sample correlations and we derive approximate null distributions using asymptotic and non-parametric distributions. Theoretical results on the power of the tests are presented and backed up by a range of simulation experiments. We apply the methodology to a case study of colorectal tumour gene expression data with the aim of discovering biological pathway lists of genes that present significantly different correlation matrices on healthy and tumour samples. We find strong evidence for a large part of the pathway lists correlation matrices to change among the two medical conditions.Comment: 31 pages, 3 figure

Joint Estimation of Sparse Networks with application to Paired Gene Expression data

We consider a method to jointly estimate sparse precision matrices and their underlying graph structures using dependent high-dimensional datasets. We present a penalized maximum likelihood estimator which encourages both sparsity and similarity in the estimated precision matrices where tuning parameters are automatically selected by controlling the expected number of false positive edges. We also incorporate an extra step to remove edges which represent an overestimation of triangular motifs. We conduct a simulation study to show that the proposed methodology presents consistent results for different combinations of sample size and dimension. Then, we apply the suggested approaches to a high-dimensional real case study of gene expression data with samples in two medical conditions, healthy and colon cancer tissues, to estimate a common network of genes as well as the differentially connected genes that are important to the disease. We find denser graph structures for healthy samples than for tumor samples, with groups of genes interacting together in the shape of clusters.Comment: 34 pages, 10 figures, 7 table

Cellular Senescence Is Immunogenic and Promotes Antitumor Immunity

Senescencia celular; Inmunidad antitumoralSenescència cel·lular; Immunitat antitumoralCellular senescence; Antitumor immunityCellular senescence is a stress response that activates innate immune cells, but little is known about its interplay with the adaptive immune system. Here, we show that senescent cells combine several features that render them highly efficient in activating dendritic cells (DC) and antigen-specific CD8 T cells. This includes the release of alarmins, activation of IFN signaling, enhanced MHC class I machinery, and presentation of senescence-associated self-peptides that can activate CD8 T cells. In the context of cancer, immunization with senescent cancer cells elicits strong antitumor protection mediated by DCs and CD8 T cells. Interestingly, this protection is superior to immunization with cancer cells undergoing immunogenic cell death. Finally, the induction of senescence in human primary cancer cells also augments their ability to activate autologous antigen-specific tumor-infiltrating CD8 lymphocytes. Our study indicates that senescent cancer cells can be exploited to develop efficient and protective CD8-dependent antitumor immune responses. Significance: Our study shows that senescent cells are endowed with a high immunogenic potential—superior to the gold standard of immunogenic cell death. We harness these properties of senescent cells to trigger efficient and protective CD8-dependent antitumor immune responses.We are grateful to Maria Isabel Muñoz for assistance with the animal protocols; to Kevin Kovalchik for help with data sharing; to Francesca Castoldi for help in total RNA extraction for B16F10 and IMR-90 cells; to Fredrik Fagerstrom-Billai, Susann Fält, Anastasios Damdimopoulos, and David Brodin at Bioinformatics and Expression Analysis Core Facility, Karolinska Institute (KI), for assistance in RNA-seq and analysis; to the IRB core facilities (Functional Genomics, Biostatistics/Bioinformatics and Histopathology); and to the PCB (Animal House) for general research support. I. Marin was the recipient of an FPI fellowship from the Spanish Ministry of Science (PRE2018-083381). O. Boix was the recipient of an FPI-AGAUR fellowship from the Generalitat de Catalunya. A. Garcia-Garijo was supported by a PERIS grant (SLT017/20/000131) from the Generalitat de Catalunya. J.A. López-Domínguez and M. Kovatcheva were supported by a fellowship from the Spanish Association Against Cancer (AECC). Work in the laboratory of E. Caron was funded by the Fonds de recherche du Québec – Santé (FRQS), the Cole Foundation, CHU Sainte-Justine, the Charles-Bruneau Foundation, the Canada Foundation for Innovation, the National Sciences and Engineering Research Council (#RGPIN-2020-05232), and the Canadian Institutes of Health Research (#174924). E. Garralda received funding from the Comprehensive Program of Cancer Immunotherapy and Immunology II (CAIMI-II) supported by the BBVA Foundation (grant 53/2021). The M. Abad lab received funding from the Spanish Ministry of Science and Innovation (RTI2018-102046-B-I00A and RTC-2017-6123-1) and the AECC (PRYCO211023SERR). M. Abad was the recipient of a Ramón y Cajal contract from the Spanish Ministry of Science and Innovation (RYC-2013-14747). A. Gros received funding from the Spanish Ministry of Science cofunded by the European Regional Development Fund (ERDF; RTC-2017-6123-1), from the Instituto de Salud Carlos III (MS15/00058), and from CAIMI-II (grant 53/2021) supported by the BBVA Foundation. The work in the laboratory of F. Pietrocola is supported by a KI Starting Grant, a Starting Grant from the Swedish Research Council (2019_02050_3), and grants from the Harald Jeanssons Foundation, the Loo and Hans Osterman Foundation, and Cancerfonden (21 1637 Pj). Work in the laboratory of M. Serrano was funded by the IRB and La Caixa Foundation, and by grants from the Spanish Ministry of Science cofunded by the European Regional Development Fund (SAF-2017-82613-R, RTC-2017-6123-1), the European Research Council (ERC-2014-AdG/669622), Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement of Catalonia (Grup de Recerca consolidat 2017 SGR 282), and the AECC (PRYCO211023SERR). The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734

Selection of the Regularization Parameter in Graphical Models Using Network Characteristics

<p>Gaussian graphical models represent the underlying graph structure of conditional dependence between random variables, which can be determined using their partial correlation or precision matrix. In a high-dimensional setting, the precision matrix is estimated using penalized likelihood by adding a penalization term, which controls the amount of sparsity in the precision matrix and totally characterizes the complexity and structure of the graph. The most commonly used penalization term is the L1 norm of the precision matrix scaled by the regularization parameter, which determines the trade-off between sparsity of the graph and fit to the data. In this article, we propose several procedures to select the regularization parameter in the estimation of graphical models that focus on recovering reliably the appropriate network structure of the graph. We conduct an extensive simulation study to show that the proposed methods produce useful results for different network topologies. The approaches are also applied in a high-dimensional case study of gene expression data with the aim to discover the genes relevant to colon cancer. Using these data, we find graph structures, which are verified to display significant biological gene associations. Supplementary material is available online.</p

Selection of the Regularization Parameter in Graphical Models Using Network Characteristics

<p>Gaussian graphical models represent the underlying graph structure of conditional dependence between random variables, which can be determined using their partial correlation or precision matrix. In a high-dimensional setting, the precision matrix is estimated using penalized likelihood by adding a penalization term, which controls the amount of sparsity in the precision matrix and totally characterizes the complexity and structure of the graph. The most commonly used penalization term is the L1 norm of the precision matrix scaled by the regularization parameter, which determines the trade-off between sparsity of the graph and fit to the data. In this article, we propose several procedures to select the regularization parameter in the estimation of graphical models that focus on recovering reliably the appropriate network structure of the graph. We conduct an extensive simulation study to show that the proposed methods produce useful results for different network topologies. The approaches are also applied in a high-dimensional case study of gene expression data with the aim to discover the genes relevant to colon cancer. Using these data, we find graph structures, which are verified to display significant biological gene associations. Supplementary material is available online.</p