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  1. Article ; Online: Modelling ChIP-seq Data Using HMMs.

    Vinciotti, Veronica

    Methods in molecular biology (Clifton, N.J.)

    2017  Volume 1552, Page(s) 115–122

    Abstract: Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq ... ...

    Abstract Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq experiments. We show how a hidden Markov model can naturally account for spatial dependencies in the ChIP-seq data, how it can be used in the presence of data from multiple ChIP-seq experiments under the same biological condition, and how it naturally accounts for the different IP efficiencies of individual ChIP-seq experiments.
    Language English
    Publishing date 2017
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-6753-7_8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Simple and Adaptive Dispersion Regression Model for Count Data.

    Klakattawi, Hadeel S / Vinciotti, Veronica / Yu, Keming

    Entropy (Basel, Switzerland)

    2018  Volume 20, Issue 2

    Abstract: Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically ... ...

    Abstract Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discrete Weibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdispersion, underdispersion and covariate-specific dispersion. Maximum likelihood can be used for efficient parameter estimation. The description of the model, parameter inference and model diagnostics is accompanied by simulated and real data analyses.
    Language English
    Publishing date 2018-02-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e20020142
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: ℓ 1-Penalized censored Gaussian graphical model.

    Augugliaro, Luigi / Abbruzzo, Antonino / Vinciotti, Veronica

    Biostatistics (Oxford, England)

    2018  Volume 21, Issue 2, Page(s) e1–e16

    Abstract: Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator ... ...

    Abstract Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithms for inference. We evaluate the computational efficiency of the proposed algorithms by an extensive simulation study and show that, when censored data are available, our proposal is superior to existing competitors both in terms of network recovery and parameter estimation. We apply the proposed method to gene expression data generated by microfluidic Reverse Transcription quantitative Polymerase Chain Reaction technology in order to make inference on the regulatory mechanisms of blood development. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/cglasso).
    MeSH term(s) Algorithms ; Computer Simulation ; Gene Regulatory Networks ; Humans ; Normal Distribution ; Reverse Transcriptase Polymerase Chain Reaction
    Language English
    Publishing date 2018-09-10
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2031500-4
    ISSN 1468-4357 ; 1465-4644
    ISSN (online) 1468-4357
    ISSN 1465-4644
    DOI 10.1093/biostatistics/kxy043
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6259: Show issues Location:
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  4. Book ; Online: Cultures as networks of cultural traits

    De Benedictis, Luca / Rondinelli, Roberto / Vinciotti, Veronica

    A unifying framework for measuring culture and cultural distances

    2020  

    Abstract: Making use of the information from the World Value Survey (WVS), and operationalizing a definition of national culture that encompasses both the relevance of specific cultural traits and the interdependence among them, this paper proposes a methodology ... ...

    Abstract Making use of the information from the World Value Survey (WVS), and operationalizing a definition of national culture that encompasses both the relevance of specific cultural traits and the interdependence among them, this paper proposes a methodology to reveal the latent structure of national culture and to measure cultural distance between countries that takes into account both the difference in cultural traits and the difference in the network structure of national cultures. Exploiting the possibilities offered by copula graphical models for discrete data, this paper infers the cultural networks of all the countries included in the WVS (Wave 6) and proposes a novel unifying framework to measure national culture and international cultural distances. The Jeffreys' divergence between copula graphical models, taken as the measure of cultural distance between countries, captures the orthogonality of the two components of cultural distance: the one based on cultural traits and the one based on the network structure among them. Moreover, the two components are shown to correlate with different national and structural characteristics of cultural networks, thus encompassing the different informational sets related to national cultures.

    Comment: 27 pages, 12 figures, 4 tables
    Keywords Statistics - Applications ; Physics - Physics and Society ; 62P20 (Primary) ; 62P25 (Secondary)
    Subject code 390
    Publishing date 2020-07-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Non-redundant functions of H2A.Z.1 and H2A.Z.2 in chromosome segregation and cell cycle progression.

    Sales-Gil, Raquel / Kommer, Dorothee C / de Castro, Ines J / Amin, Hasnat A / Vinciotti, Veronica / Sisu, Cristina / Vagnarelli, Paola

    EMBO reports

    2021  Volume 22, Issue 11, Page(s) e52061

    Abstract: H2A.Z is a H2A-type histone variant essential for many aspects of cell biology, ranging from gene expression to genome stability. From deuterostomes, H2A.Z evolved into two paralogues, H2A.Z.1 and H2A.Z.2, that differ by only three amino acids and are ... ...

    Abstract H2A.Z is a H2A-type histone variant essential for many aspects of cell biology, ranging from gene expression to genome stability. From deuterostomes, H2A.Z evolved into two paralogues, H2A.Z.1 and H2A.Z.2, that differ by only three amino acids and are encoded by different genes (H2AFZ and H2AFV, respectively). Despite the importance of this histone variant in development and cellular homeostasis, very little is known about the individual functions of each paralogue in mammals. Here, we have investigated the distinct roles of the two paralogues in cell cycle regulation and unveiled non-redundant functions for H2A.Z.1 and H2A.Z.2 in cell division. Our findings show that H2A.Z.1 regulates the expression of cell cycle genes such as Myc and Ki-67 and its depletion leads to a G1 arrest and cellular senescence. On the contrary, H2A.Z.2, in a transcription-independent manner, is essential for centromere integrity and sister chromatid cohesion regulation, thus playing a key role in chromosome segregation.
    MeSH term(s) Animals ; Centromere/metabolism ; Chromosome Segregation ; Genomic Instability ; Histones/genetics ; Histones/metabolism
    Chemical Substances Histones
    Language English
    Publishing date 2021-08-23
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2020896-0
    ISSN 1469-3178 ; 1469-221X
    ISSN (online) 1469-3178
    ISSN 1469-221X
    DOI 10.15252/embr.202052061
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5433: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 41: Show issues

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  6. Book ; Online: Does hospital cooperation increase the quality of healthcare?

    Berta, Paolo / Vinciotti, Veronica / Moscone, Francesco

    2019  

    Abstract: Motivated by reasons such as altruism, managers from different hospitals may engage in cooperative behaviours, which shape the networked healthcare economy. In this paper we study the determinants of hospital cooperation and its association with the ... ...

    Abstract Motivated by reasons such as altruism, managers from different hospitals may engage in cooperative behaviours, which shape the networked healthcare economy. In this paper we study the determinants of hospital cooperation and its association with the quality delivered by hospitals, using Italian administrative data. We explore the impact on patient transfers between hospitals (cooperation/network) of a set of demand-supply factors, as well as distance-based centrality measures. We then use this framework to assess how such cooperation is related to the overall quality for the hospital of origin and of destination of the patient transfer. The over-dispersed Poisson mixed model that we propose, inspired by the literature on social relations models, is suitably defined to handle network data, which are rarely used in health economics. The results show that distance plays an important role in hospital cooperation, though there are other factors that matter such as geographical centrality. Another empirical finding is the existence of a positive relationship between hospital cooperation and the overall quality of the connected hospitals. The absence of a source of information on the quality of hospitals accessible to all providers, such as in the form of star ratings, may prevent some hospitals to engage and cooperate with other hospitals of potentially higher quality. This may result in a lower degree of cooperation among hospitals and a reduction in quality overall.
    Keywords Statistics - Applications
    Subject code 027
    Publishing date 2019-11-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: NEAT: an efficient network enrichment analysis test.

    Signorelli, Mirko / Vinciotti, Veronica / Wit, Ernst C

    BMC bioinformatics

    2016  Volume 17, Issue 1, Page(s) 352

    Abstract: Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal ... ...

    Abstract Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions.
    Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves.
    Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).
    MeSH term(s) Computer Simulation ; Gene Expression Profiling/methods ; Gene Ontology ; Gene Regulatory Networks ; Genes, Fungal ; Saccharomyces cerevisiae/genetics ; Software ; Stress, Physiological/genetics
    Language English
    Publishing date 2016-09-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-016-1203-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Robust methods for inferring sparse network structures

    Vinciotti, Veronica / Hashem, Hussein

    Computational statistics & data analysis. 2013 Nov., v. 67

    2013  

    Abstract: Networks appear in many fields, from finance to medicine, engineering, biology and social science. They often comprise of a very large number of entities, the nodes, and the interest lies in inferring the interactions between these entities, the edges, ... ...

    Abstract Networks appear in many fields, from finance to medicine, engineering, biology and social science. They often comprise of a very large number of entities, the nodes, and the interest lies in inferring the interactions between these entities, the edges, from relatively limited data. If the underlying network of interactions is sparse, two main statistical approaches are used to retrieve such a structure: covariance modeling approaches with a penalty constraint that encourages sparsity of the network, and nodewise regression approaches with sparse regression methods applied at each node. In the presence of outliers or departures from normality, robust approaches have been developed which relax the assumption of normality. Robust covariance modeling approaches are reviewed and compared with novel nodewise approaches where robust methods are used at each node. For low-dimensional problems, classical deviance tests are also included and compared with penalized likelihood approaches. Overall, copula approaches are found to perform best: they are comparable to the other methods under an assumption of normality or mild departures from this, but they are superior to the other methods when the assumption of normality is strongly violated.
    Keywords covariance ; models ; regression analysis
    Language English
    Dates of publication 2013-11
    Size p. 84-94.
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 1478763-5
    ISSN 0167-9473
    ISSN 0167-9473
    DOI 10.1016/j.csda.2013.05.004
    Database NAL-Catalogue (AGRICOLA)

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  9. Article: Sparse estimation of huge networks with a block-wise structure

    Moscone, Francesco / Tosetti, Elisa / Vinciotti, Veronica

    The econometrics journal Bd. 20 , 3, Seite S61-S85

    2017  

    Author's details Francesco Moscone, Elisa Tosetti and Veronica Vinciotti
    Keywords Block-wise dependence ; Graphical LASSO ; Graphical modelling ; Panels ; Spatial econometrics
    Language English
    Publisher Wiley-Blackwell
    Publishing place Oxford [u.a.]
    Document type Article
    ZDB-ID 1412265-0 ; 1475536-1
    ISSN 1368-423X ; 1368-4221
    ISSN (online) 1368-423X
    ISSN 1368-4221
    Database ECONomics Information System

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  10. Article ; Online: Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

    Vinciotti, Veronica / Augugliaro, Luigi / Abbruzzo, Antonino / Wit, Ernst C

    Statistical applications in genetics and molecular biology

    2016  Volume 15, Issue 3, Page(s) 193–212

    Abstract: Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these ... ...

    Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order - some entries of the precision matrix are a priori zeros - or equal dependency strengths across time lags - some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.
    Language English
    Publishing date 2016-06-01
    Publishing country Germany
    Document type Journal Article
    ISSN 1544-6115
    ISSN (online) 1544-6115
    DOI 10.1515/sagmb-2014-0075
    Database MEDical Literature Analysis and Retrieval System OnLINE

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