Domain selection and familywise error rate for functional data: A unified framework.

Functional data are smooth, often continuous, random curves, which can be seen as an extreme case of multivariate data with infinite dimensionality. Just as componentwise inference for multivariate data naturally performs feature selection, subsetwise inference for functional data performs domain selection. In this paper, we present a unified testing framework for domain selection on populations of functional data. In detail, p-values of hypothesis tests performed on pointwise evaluations of functional data are suitably adjusted for providing control of the familywise error rate (FWER) over a family of subsets of the domain. We show that several state-of-the-art domain selection methods fit within this framework and differ from each other by the choice of the family over which the control of the FWER is provided. In the existing literature, these families are always defined a priori. In this work, we also propose a novel approach, coined thresholdwise testing, in which the family of subsets is instead built in a data-driven fashion. The method seamlessly generalizes to multidimensional domains in contrast to methods based on a priori defined families. We provide theoretical results with respect to consistency and control of the FWER for the methods within the unified framework. We illustrate the performance of the methods within the unified framework on simulated and real data examples and compare their performance with other existing methods.

Over three decades of natural history of limb girdle muscular dystrophy type R1/2A and R2/2B: Mathematical modelling of a multifactorial study.

We aimed to describe the natural history of Limb Girdle Muscular Dystrophy type 2A and 2B over more than three decades by considering muscular strength, motor, cardiac and respiratory function. 428 visits of nineteen 2A and twenty 2B patients were retrospectively analysed through a regression model to create the curves of evolution with disease duration of muscle strength (through Medical Research Council grading), motor function measure scale (D1, D2 and D3 domains) and cardio-pulmonary function tests. Clinically relevant muscular and motor function alterations occurred after the first decade of disease, while mild respiratory function alterations started after the second, with preserved cardiac function. Although type 2A showed relatively stronger distal lower limb muscles, while type 2B started with relatively stronger upper limb muscles, the corresponding motor functions were similar, becoming severely compromised after 25 years of disease. This was the longest retrospective study in types 2A and 2B. It defined curves of disease evolution not only from a neuromuscular, but also from functional, cardiac, and respiratory points of view, to be used to evaluate how the natural progression is changed by therapies. Due to slow disease progression, it was not possible to identify time sensitive endpoints.

A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study.

Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials-some of these with antimicrobial action-is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.

Functional Data Analysis of high-frequency load curves reveals drivers of residential electricity consumption.

Smart energy meters generate real time, high frequency data which can foster demand management and response of consumers and firms, with potential private and social benefits. However, proper statistical techniques are needed to make sense of this large amount of data and translate them into usable recommendations. Here, we apply Functional Data Analysis (FDA), a novel branch of Statistics that analyses functions-to identify drivers of residential electricity load curves. We evaluate a real time feedback intervention which involved about 1000 Italian households for a period of three years. Results of the FDA modelling reveal, for the first time, daytime-indexed patterns of residential electricity consumption which depend on the ownership of specific clusters of electrical appliances and an overall reduction of consumption after the introduction of real time feedback, unrelated to appliance ownership characteristics.

Can grimace scales estimate the pain status in horses and mice? A statistical approach to identify a classifier.

Pain recognition is fundamental for safeguarding animal welfare. Facial expressions have been investigated in several species and grimace scales have been developed as pain assessment tool in many species including horses (HGS) and mice (MGS). This study is intended to progress the validation of grimace scales, by proposing a statistical approach to identify a classifier that can estimate the pain status of the animal based on Facial Action Units (FAUs) included in HGS and MGS. To achieve this aim, through a validity study, the relation between FAUs included in HGS and MGS and the real pain condition was investigated. A specific statistical approach (Cumulative Link Mixed Model, Inter-rater reliability, Multiple Correspondence Analysis, Linear Discriminant Analysis and Support Vector Machines) was applied to two datasets. Our results confirm the reliability of both scales and show that individual FAU scores of HGS and MGS are related to the pain state of the animal. Finally, we identified the optimal weights of the FAU scores that can be used to best classify animals in pain with an accuracy greater than 70%. For the first time, this study describes a statistical approach to develop a classifier, based on HGS and MGS, for estimating the pain status of animals. The classifier proposed is the starting point to develop a computer-based image analysis for the automatic recognition of pain in horses and mice.

IWTomics: testing high-resolution sequence-based 'Omics' data at multiple locations and scales.

Summary: With increased generation of high-resolution sequence-based 'Omics' data, detecting statistically significant effects at different genomic locations and scales has become key to addressing several scientific questions. IWTomics is an R/Bioconductor package (integrated in Galaxy) that, exploiting sophisticated Functional Data Analysis techniques (i.e. statistical techniques that deal with the analysis of curves), allows users to pre-process, visualize and test these data at multiple locations and scales. The package provides a friendly, flexible and complete workflow that can be employed in many genomic and epigenomic applications. Availability and implementation: IWTomics is freely available at the Bioconductor website (http://bioconductor.org/packages/IWTomics) and on the main Galaxy instance (https://usegalaxy.org/). Supplementary information: Supplementary data are available at Bioinformatics online.

Evolution of respiratory function in Duchenne muscular dystrophy from childhood to adulthood.

In Duchenne muscular dystrophy (DMD), it is still to be determined if specific timepoints can be identified during the natural evolution of respiratory dysfunction from childhood to adulthood and if scoliosis, steroid therapy and nocturnal noninvasive mechanical ventilation (NIMV) have any effect on it.In a 7-year retrospective study performed on 115 DMD patients (6-24 years), evaluated once or twice per year, with 574 visits in total, evolution mean curves of spirometry, lung volumes, spontaneous breathing and thoraco-abdominal pattern (measured by optoelectronic plethysmography) parameters were obtained by nonlinear regression model analysis.While predicted values of forced vital capacity, forced expiratory volume in 1 s, and peak expiratory flow decline continuously since childhood, during spontaneous breathing the following parameters become significantly different than normal in sequence: abdominal contribution to tidal volume (lower after 14.8 years), tidal volume (lower after 17.2 years), minute ventilation (lower after 18.1 years) and respiratory rate (higher after 22.1 years). Restrictive lung pattern and diaphragmatic impairment are exacerbated by scoliosis severity, slowed by steroids treatment and significantly affected by NIMV.Spirometry, lung volumes, breathing pattern and thoraco-abdominal contributions show different evolution curves over time. Specific timepoints of respiratory impairment are identified during disease progression. These should be considered when defining outcome measures in clinical trials and treatment strategies in DMD.

FunChIP: an R/Bioconductor package for functional classification of ChIP-seq shapes.

SUMMARY: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) generates local accumulations of sequencing reads on the genome ("peaks"), which correspond to specific protein-DNA interactions or chromatin modifications. Peaks are detected by considering their total area above a background signal, usually neglecting their shapes, which instead may convey additional biological information. We present FunChIP, an R/Bioconductor package for clustering peaks according to a functional representation of their shapes: after approximating their profiles with cubic B-splines, FunChIP minimizes their functional distance and classifies the peaks applying a k-mean alignment and clustering algorithm. The whole pipeline is user-friendly and provides visualization functions for a quick inspection of the results. An application to the transcription factor Myc in 3T9 murine fibroblasts shows that clusters of peaks with different shapes are associated with different genomic locations and different transcriptional regulatory activity. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and is available under Artistic Licence 2.0 from the Bioconductor website (http://bioconductor.org/packages/FunChIP). CONTACT: marco.morelli@iit.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The interval testing procedure: A general framework for inference in functional data analysis.

We introduce in this work the Interval Testing Procedure (ITP), a novel inferential technique for functional data. The procedure can be used to test different functional hypotheses, e.g., distributional equality between two or more functional populations, equality of mean function of a functional population to a reference. ITP involves three steps: (i) the representation of data on a (possibly high-dimensional) functional basis; (ii) the test of each possible set of consecutive basis coefficients; (iii) the computation of the adjusted p-values associated to each basis component, by means of a new strategy here proposed. We define a new type of error control, the interval-wise control of the family wise error rate, particularly suited for functional data. We show that ITP is provided with such a control. A simulation study comparing ITP with other testing procedures is reported. ITP is then applied to the analysis of hemodynamical features involved with cerebral aneurysm pathology. ITP is implemented in the fdatest R package.

Peak shape clustering reveals biological insights.

BACKGROUND: ChIP-seq experiments are widely used to detect and study DNA-protein interactions, such as transcription factor binding and chromatin modifications. However, downstream analysis of ChIP-seq data is currently restricted to the evaluation of signal intensity and the detection of enriched regions (peaks) in the genome. Other features of peak shape are almost always neglected, despite the remarkable differences shown by ChIP-seq for different proteins, as well as by distinct regions in a single experiment. RESULTS: We hypothesize that statistically significant differences in peak shape might have a functional role and a biological meaning. Thus, we design five indices able to summarize peak shapes and we employ multivariate clustering techniques to divide peaks into groups according to both their complexity and the intensity of their coverage function. In addition, our novel analysis pipeline employs a range of statistical and bioinformatics techniques to relate the obtained peak shapes to several independent genomic datasets, including other genome-wide protein-DNA maps and gene expression experiments. To clarify the meaning of peak shape, we apply our methodology to the study of the erythroid transcription factor GATA-1 in K562 cell line and in megakaryocytes. CONCLUSIONS: Our study demonstrates that ChIP-seq profiles include information regarding the binding of other proteins beside the one used for precipitation. In particular, peak shape provides new insights into cooperative transcriptional regulation and is correlated to gene expression.

One-leg hop kinematics 20 years following anterior cruciate ligament rupture: Data revisited using functional data analysis.

BACKGROUND: Despite interventions, anterior cruciate ligament ruptures can cause long-term deficits. To assist in identifying and treating deficiencies, 3D-motion analysis is used for objectivizing data. Conventional statistics are commonly employed to analyze kinematics, reducing continuous data series to discrete variables. Conversely, functional data analysis considers the entire data series. METHODS: Here, we employ functional data analysis to examine and compare the entire time-domain of knee-kinematic curves from one-leg hops between and within three groups. All subjects (n=95) were part of a long-term follow-up study involving anterior cruciate ligament ruptures treated ~20 years ago conservatively with physiotherapy only or with reconstructive surgery and physiotherapy, and matched knee-healthy controls. FINDINGS: Between-group differences (injured leg, treated groups; non-dominant leg, controls) were identified during the take-off and landing phases, and in the sagittal (flexion/extension) rather than coronal (abduction/adduction) and transverse (internal/external) planes. Overall, surgical and control groups demonstrated comparable knee-kinematic curves. However, compared to controls, the physiotherapy-only group exhibited less flexion during the take-off (0-55% of the normalized phase) and landing (44-73%) phase. Between-leg differences were absent in controls and the surgically treated group, but observed during the flight (4-22%, injured leg>flexion) and the landing (57-85%, injured leg

Object Oriented Data Analysis: A few methodological challenges.

This is a discussion of the paper "Overview of object oriented data analysis" by J. Steve Marron and Andrés M. Alonso.

CD4+ T cells against human papillomavirus-18 E7 in patients with high-grade cervical lesions associate with the absence of the virus in the cervix.

Cervical neoplastic lesions are associated with infection by high-risk human papilloma-viruses (HPV). The two genotypes most frequently found in the lesions are HPV-16 and HPV-18 with a prevalence of 50-60% and 15-18%, respectively. The E6 and E7 viral oncoproteins are involved in the transformation process and represent foreign antigens for the host. We previously reported that anti-HPV-18 E6 CD4(+) T cells are present in patients with high-grade HPV-18-expressing cervical lesions but also in 50% of the total consecutive patients tested, independently of the HPV type carried. These results indicated that HPV-18 E6 is immunogenic and suggested that all responsive patients, irrespective of the HPV expressed, had encountered HPV-18 and cleared the infection. Here, we investigated anti-HPV-18 E7 CD4(+) T-cell immunity in a cohort of 23 HPV-18 E6-responsive patients. We found that, although E7-specific CD4(+) T cells were present in all women, a robust T helper type (Th1)/Th2 type response against E7 was associated with HPV-18-negative status, suggesting that indeed these patients might have cleared the virus. In agreement with this hypothesis, we found strong anti-E7 CD4(+) T-cell immunity in 20% of 24 healthy donors without evidence of disease. In contrast, a robust Th1/Th2 type response against E6 but not E7 correlated with a lack of disease relapse and/or infection recurrence but did not discriminate between HPV-18-positive and HPV-18-negative patients. Collectively, our data suggest different roles for anti-HPV-18 E6 and E7 CD4(+) T cells in anti-viral and anti-tumour immunity.

IFN-gamma produced by human papilloma virus-18 E6-specific CD4+ T cells predicts the clinical outcome after surgery in patients with high-grade cervical lesions.

Cervical neoplastic lesions are associated with infection by high-risk human papilloma viruses (HPVs). HPV-16 and HPV-18 are the most common genotypes. It has been proposed that development of HPV-16-positive cervical lesions is associated with impaired CD4(+) T cell immunity against early Ags. The aim of the study was to evaluate whether this impairment also applies to HPV-18. We investigated the presence and the quality of anti-HPV-18 E6 CD4(+) T cell responses in the blood of 37 consecutive patients with high-grade cervical lesions, 25 normal donors, and 20 cord bloods. The immune infiltrate in the cervical lesions was also evaluated. The characteristics of the responses were correlated to the clinical outcome. We found that one or more HPV-18 E6 peptides, containing naturally processed epitopes, were able to induce a response in 40-50% of the patients, depending on the effector function tested. Importantly, these percentages rose to 80-100% when HPV-18-positive patients were considered. HPV-18 E6-specific CD4(+) T cells produced mixed Th1/Th2 responses and statistical analysis of the cytokines produced revealed that the amount of IFN-gamma released could predict infection persistence and/or disease relapse after surgery. Finally, we found that a higher number of infiltrating CD4(+) and T-bet(+) T cells in the lesions correlated with a favorable clinical outcome. Our results strongly suggest a relevant role for CD4(+) T cells in the control of the HPV-18 compared with HPV-16 infections in patients with high-grade cervical lesions and identify an immunologic parameter potentially useful for patients' stratification.