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This article studies M-type estimators for fitting robust additive models in the presence of anomalous data. The components in the additive model are allowed to have different degrees of smoothness. We introduce a new class of wavelet-based robust M-type estimators for performing simultaneous additive component estimation and variable selection in such inhomogeneous additive models. Each additive component is approximated by a truncated series expansion of wavelet bases, making it feasible to apply the method to nonequispaced data and sample sizes that are not necessarily a power of 2. Sparsity of the additive components together with sparsity of the wavelet coefficients within each component (group), results into a bi-level group variable selection problem. In this framework, we discuss robust estimation and variable selection. A two-stage computational algorithm, consisting of a fast accelerated proximal gradient algorithm of coordinate descend type, and thresholding, is proposed. When using nonconvex redescending loss functions, and appropriate nonconvex penalty functions at the group level, we establish optimal convergence rates of the estimates. We prove variable selection consistency under a weak compatibility condition for sparse additive models. The theoretical results are complemented with some simulations and real data analysis, as well as a comparison to other existing methods.

We introduce a new methodology for anomaly detection (AD) in multichannel fast oscillating signals based on nonparametric penalized regression. Assuming the signals share similar shapes and characteristics, the estimation procedures are based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. Under the standard hypothesis of Gaussian additive noise, we model the signals by the RADWT and the anomalies as additive in each signal. Then we perform AD imposing a double penalty on the multiple regression model we obtained, promoting group sparsity both on the regression coefficients and on the anomalies. The first constraint preserves a common structure on the underlying signal components, the second one aims to identify the presence/absence of anomalies. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case.

A new technique for nonparametric regression of multichannel signals is presented. The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. In particular, two different frames are obtained by two RADWT with different Q-factors that give sparse representations of functions with low and high resonance. It is assumed that the signals are measured simultaneously on several independent channels and that they share the low resonance component and the spectral characteristics of the high resonance component. Then, a regression analysis is performed by means of the grouped lasso penalty. Furthermore, a result of asymptotic optimality of the estimator is presented using reasonable assumptions and exploiting recent results on group-lasso like procedures. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case example for the analysis and joint detection of sleep spindles and K-complex events for multiple electroencephalogram (EEG) signals., 38 pages, 8 figures

The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF's multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome. The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF-binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF’s multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome.

We introduce a new class of robust M-estimators for performing simultaneous parameter estimation and variable selection in high-dimensional regression models. We first explain the motivations for the key ingredient of our procedures which are inspired by regularization methods used in wavelet thresholding in noisy signal processing. The derived penalized estimation procedures are shown to enjoy theoretically the oracle property both in the classical finite dimensional case as well as the high-dimensional case when the number of variables p is not fixed but can grow with the sample size n, and to achieve optimal asymptotic rates of convergence. A fast accelerated proximal gradient algorithm, of coordinate descent type, is proposed and implemented for computing the estimates and appears to be surprisingly efficient in solving the corresponding regularization problems including the case for ultra high-dimensional data where $$p \gg n$$ . Finally, a very extensive simulation study and some real data analysis, compare several recent existing M-estimation procedures with the ones proposed in the paper, and demonstrate their utility and their advantages.

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

A technique for the restoration of low resonance component and high resonance component of K independently measured signals is presented. The definition of low and high resonance component is given by the Rational Dilatation Wavelet Transform (RADWT), a particular kind of finite frame that provides sparse representation of functions with different oscillations persistence. It is assumed that the signals are measured simultaneously on several independent channels and in each channel the underlying signal is the sum of two components: the low resonance component and the high resonance component, both sharing some common characteristic between the channels. Components restoration is performed by means of the lasso-type penalty and backfitting algorithm. Numerical experiments show the performance of the proposed method in different synthetic scenarios highlighting the advantage of estimating the two components separately rather than together.

The transcription factor CCCTC-binding factor (CTCF) modulates pleiotropic functions mostly related to gene expression regulation. The role of CTCF in large scale genome organization is also well established. A unifying model to explain relationships among many CTCF-mediated activities involves direct or indirect interactions with numerous protein cofactors recruited to specific binding sites. The co-association of CTCF with other architectural proteins such as cohesin, chromodomain helicases, and BRG1, further supports the interplay between master regulators of mammalian genome folding. Here, we report a comprehensive LC-MS/MS mapping of the components of the switch/sucrose nonfermentable (SWI/SNF) chromatin remodeling complex co-associated with CTCF including subunits belonging to the core, signature, and ATPase modules. We further show that the localization patterns of representative SWI/SNF members significantly overlap with CTCF sites on transcriptionally active chromatin regions. Moreover, we provide evidence of a direct binding of the BRK-BRG1 domain to the zinc finger motifs 4&ndash, 8 of CTCF, thus, suggesting that these domains mediate the interaction of CTCF with the SWI/SNF complex. These findings provide an updated view of the cooperative nature between CTCF and the SWI/SNF ATP-dependent chromatin remodeling complexes, an important step for understanding how these architectural proteins collaborate to shape the genome.

In this paper, we present a 2-Dimensional (2D) Optimal Interpolation (OI) technique for spatially scattered infrared satellite observations, from which level 2 products have been obtained, in order to yield level 3, regularly gridded, data. The scheme derives from a Bayesian predictor-corrector scheme used in data assimilation and is based on the Kalman filter estimation. It has been applied to 15-minutes temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and temperature products and to Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) retrievals, a gas affecting the air quality. Results have been exemplified for target areas over Italy. In particular temperature retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. Our findings show that the proposed strategy is quite effective to fill gaps because of data voids due, e.g., to clouds, gains more efficiency in capturing the daily cycle for surface parameters and provides valuable information on NH3 concentration and variability in regions not yet covered by ground-based instruments.

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset.

In this paper we study sparse high dimensional additive partial linear models with nonparametric additive components of heterogeneous smoothness. We review several existing algo- rithms that have been developed for this problem in the recent literature, highlighting the connec- tions between them, and present some computationally efficient algorithms for fitting such models. To achieve optimal rates in large sample situations we use hybrid P-splines and block wavelet penal- isation techniques combined with adaptive (group) LASSO-like procedures for selecting the additive components in the nonparametric part of the models. Hence, the component selection and estimation in the nonparametric part may be viewed as a functional version of estimation and grouped variable selection. This allows to take advantage of several oracle results which yield asymptotic optimality of estimators in high-dimensional but sparse additive models. Numerical implementations of our procedures for proximal like algorithms are discussed. Large sample properties of the estimates and of the model selection are presented and the results are illustrated with simulated examples and a real data analysis.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks. Keywords: cancer, Cox model, high-dimensionality, gene expression, network, regularization, survival

Imprinting Control Regions (ICRs) need to maintain their parental allele-specific DNA methylation during early embryogenesis despite genome-wide demethylation and subsequent de novo methylation. ZFP57 and KAP1 are both required for maintaining the repressive DNA methylation and H3-lysine-9-trimethylation (H3K9me3) at ICRs. In vitro, ZFP57 binds a specific hexanucleotide motif that is enriched at its genomic binding sites. We now demonstrate in mouse embryonic stem cells (ESCs) that SNPs disrupting closely-spaced hexanucleotide motifs are associated with lack of ZFP57 binding and H3K9me3 enrichment. Through a transgenic approach in mouse ESCs, we further demonstrate that an ICR fragment containing three ZFP57 motif sequences recapitulates the original methylated or unmethylated status when integrated into the genome at an ectopic position. Mutation of Zfp57 or the hexanucleotide motifs led to loss of ZFP57 binding and DNA methylation of the transgene. Finally, we identified a sequence variant of the hexanucleotide motif that interacts with ZFP57 both in vivo and in vitro. The presence of multiple and closely located copies of ZFP57 motif variants emerges as a distinct characteristic that is required for the faithful maintenance of repressive epigenetic marks at ICRs and other ZFP57 binding sites. Imprinting Control Regions (ICRs) need to maintain their parental allele-specific DNA methylation during early embryogenesis despite genome-wide demethylation and subsequent de novo methylation. ZFP57 and KAP1 are both required for maintaining the repressive DNA methylation and H3-lysine-9-trimethylation (H3K9me3) at ICRs. In vitro, ZFP57 binds a specific hexanucleotide motif that is enriched at its genomic binding sites. We now demonstrate in mouse embryonic stem cells (ESCs) that SNPs disrupting closely-spaced hexanucleotide motifs are associated with lack of ZFP57 binding and H3K9me3 enrichment. Through a transgenic approach in mouse ESCs, we further demonstrate that an ICR fragment containing three ZFP57 motif sequences recapitulates the original methylated or unmethylated status when integrated into the genome at an ectopic position. Mutation of Zfp57 or the hexanucleotide motifs led to loss of ZFP57 binding and DNA methylation of the transgene. Finally, we identified a sequence variant of the hexanucleotide motif that interacts with ZFP57 both in vivo and in vitro. The presence of multiple and closely located copies of ZFP57 motif variants emerges as a distinct characteristic that is required for the faithful maintenance of repressive epigenetic marks at ICRs and other ZFP57 binding sites.

In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for high-dimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression.

The classical smoothing data problem is analyzed in a Sobolev space under the assumption of white noise. A Fourier series method based on regularization endowed with generalized cross validation is considered to approximate the unknown function. This approximation is globally optimal, i.e., the mean integrated squared error reaches the optimal rate in the minimax sense. In this paper the pointwise convergence property is studied. Specifically, it is proved that the smoothed solution is locally convergent but not locally optimal. Examples of functions for which the approximation is subefficient are given. It is shown that optimality and superefficiency are possible when restricting to more regular subspaces of the Sobolev space.

The use of compost for soil amendment is a promising agricultural practice environmentally and economically viable. In the framework of a wide research project designed to evaluate the effects of soil amendment with municipal solid waste compost in comparison with traditional mineral fertilization practices, 54 different cultivars (Cvs) of potatoes were AFLP (amplified fragment length polymorphism) molecularly fingerprinted. The seven most genetically biodiverse potato Cvs were used to establish an experimental field in southern Italy. The field area was divided into two portions fertilized with compost (20 Mg ha(-1)) or with ammonium sulphate (200 kg ha(-1)). No significant differences in productivity, organoleptic characteristics and element concentrations were observed between the potato tubers obtained with both kinds of soil fertilization, while the tubers grown on compost amended soil showed, on average, higher K concentrations with respect to those grown on mineral fertilised soil. cDNA-AFLP (complementary DNA-AFLP) and MSAP (methylation sensitive amplified polymorphism) analyses were carried out on both leaves and tubers of one selected Cv to estimate if any transcriptome alterations or epigenetic modifications were induced by the two kinds of fertilization, however no variations were detected. Chemical and biological soil qualities (i.e., microbial respiration, FDA hydrolysis, alkaline and acid phosphatase) were assessed on soil samples at the start of the experiment and at the end of potato crop cycle. No significant differences in soil pH and limited ones, in the available fraction of some trace elements, were observed; while conductivity was much higher for the compost amended portion of the experimental field. Microbial respiration, FDA hydrolysis and acid phosphatase activities were significantly increased by compost amendment, in comparison with mineral fertilization. Finally, a sensory panel of potato Cvs detected no significant differences among qualitative descriptors and among potatoes coming from the two differently fertilized soils.

The problem of smoothing data trough a transform in the Fourier domain is analyzed in the case of correlated noise affecting data. A regularization method and two GCV-type criteria are resorted in order to solve the problem, in analogy with the case of uncorrelated noise. All convergence theorems stated for uncorrelated noise are here generalized to the case of correlated noise. Numerical experiments on significant test functions are shown.

Establishment, maintenance and reprogramming of DNA methylation is a fundamental process in the shaping of the epigenome of importance in development, cell differentiation and health state of individuals. Genomic imprinting represents a well characterized epigenetic process from germ cells to adults of parent of origin region-specific DNA methylation control leading to allele-specific regulated expression. Recently we showed that the ZFP57 factor, together with associated chromatin modifiers, is required for the maintenance of DNA methylation at Imprinting Control Regions (ICRs). Based on the results of ChIP Seq profiling in murine ES cells of endogenous ZFP57, showing association to hundreds regions including also several CpG islands different from ICRs, our aim is to establish the correlation and ZFP57-dependence of DNA methylation and histone modifications at targeted loci and their epigenetic ontogeny. We will report on ongoing studies that integrate existing DNA methylation genome-wide studies, ChIP seq profiling of ZFP57 and associated modifiers, and analysis of DNA methylation status of selected ZFP57 targets in murine ES cells.

Several lines of evidence have provided clues for the pivotal role played by the KRAB- containing zinc finger protein ZFP57 in the establishment and maintenance of the asymmetric epigenetic state, both DNA methylation and histone tails specific modifications, of the several tens of imprinted loci in mouse. ZFP57 associates with KAP1, the general corepressor of the abundant KRAB-ZFPs family, a scaffold for the recruitment of chromatin modifiers that include the histone H3 methyltransferase SETDB1 and heterochromatin protein-1 (HP1) isoforms, thus promoting H3 trimethylation at lysine 9 and chromoproteins assembly at targeted sites. Further, DNA methyltransferases are also present in KAP1 complexes, a finding that correlates with the requirement for ZFP57 to mantain DNA methylation at targeted sites. We have established in murine ES cells the epigenomic profile of the endogenous Zfp57 factor along with those of the associated KAP1, SETDB1, HP1g and the complex-dependent H3K9 trimethylation. We will report on the DNA binding motifs inference, features of bound genomic targets along with the gene association analysis that support potential pivotal roles of the locus-specific targeting ZFP57 factor in shaping the epigenetic state of multiple genes in addition to imprinted loci, through histone modification, Hp1-mediated heterochromatinization and DNA methylation.