Plot2groups: an R package to plot scatter points for two groups of values

BackgroundBiological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.ResultsWe developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system.ConclusionGenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

R: a data analysis and statistical programming environment–an emerging tool for the geosciences

Design of NPP (nuclear power plant) safety important systems (relating to a critical information infrastructure) is basing on observing the regulatory documents and proving reliable plant performance during all its life-cycle. An aspect of the increased requirements for reliable and safe NPP APCS (automated process control system) performance is the necessity of perfecting NPP operator information support systems. An approach is considering, under which a core of the operator information support system is a flexible modeling software package that can be used as a digital shadow or digital twin in the dependence of solved tasks. This approach applies to a wide range of tasks of upper-level systems of NPP APCS.

RANKS is a flexible software package that can be easily applied to any bioinformatics task formalizable as ranking of nodes with respect to a property given as a label, such as automated protein function prediction, gene disease prioritization and drug repositioning. To this end RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks. To facilitate comparative assessment, baseline network-based methods, e.g. label propagation and random walk algorithms, have also been implemented. The package is available from CRAN: https://cran.r-project.org/ The package is written in R, except for the most computationally intensive functionalities which are implemented in C. valentini@di.unimi.it Supplementary data are available at Bioinformatics online.

In 2012, almost 700,000 new head and neck cancer (HNSCC) cases were estimated to occur worldwide with two thirds of those cases occurring in developing countries. Within the geographic areas in Latin America, characterized by high incidence rates of head and neck cancer, the region comprising Argentina, southern Brazil and Uruguay has the highest levels of incidence. Despite current therapy approaches, the survival rate is around 50%, and the main reason for treatment failure is the frequent development of regional recurrences, affecting around 30% of patients. In this context the use of ctDNA (specific biomarker for tumors) using techniques of mass sequencing (high sensitivity and specificity) is presented as a promising tool for noninvasive diagnosis and monitoring of HNSCC cases. Our primary goal was to provide a comprehensive evaluation of the presence of ctDNA in both plasma and oral rinses from a series of HNSCC cases without previous knowledge of tumor mutational status. 43 cases diagnosed as head and neck squamous cell carcinoma were selected from a multi center case-control study conducted in Argentina between 1998 and 2002. Subsite and stage distribution was as follows: 14 oral cavity, 6 oropharynx, 1 hypopharynx, 15 larynx and 7 overlapping lesions. 6 cases were stage III and 17 stage IV. Additionally, 50 controls matched by center, age, sex, smoking and alcohol status were included to assess a threshold of the background noise after sequencing. Targeted sequencing was performed independently in duplicate on plasma, oral rinses, germline DNA and the tumor material using the Ion Proton™ System. Sequencing included the entire coding region of TP53 as the most frequently mutated gene in HNSCC. Both Plasma and oral rinses samples were sequenced at an average depth of 10000X. For somatic variant calling we used a recently developed new statistical model and a bioinformatics pipeline based on Poisson distribution for estimation of sequencing errors distribution to accurately identify variants above the sequencing background. Statistical tests were carried out in R statistical programming environment. 74% of tumor samples sequenced harboured at least one TP53 somatic mutation. 10 TP53 variants were identified in the corresponding plasma samples and 1 of these variants matched the tumor mutation, possibly reflecting high tumor heterogeneity. In contrast, 90% of the variants detected in the tumor were also found in in the oral rinses with allelic frequencies ranging from 0.002 to 0.05 showing higher specificity of oral rinses as a surrogate sample for mutation detection. The detection and analysis of ctDNA in noninvasive samples might be included as a potential biomarker to improve early diagnosis and patient monitoring in HNSCC cases. Citation Format: Sandra Perdomo, Patrice Avogbe, Marta Vilensky, James Mckay, Paul Brennan. Detection of circulating tumor DNA in oral rinses and plasma from head and neck cancer cases in Argentina. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 483.

T-Cell Lymphomas in Mexico: Experience of a Third Level Reference Hospital

Bootstrap is a returned re-sampling method used to estimate the variance component and their variability. Adjusted bootstrap method was used by Wiley in p×i design for normal data in 2001. However, Wiley did not compare the difference between adjusted method and unadjusted method when estimating the variability. To expand Wiley’s 2001 study, our study applied Monte Carlo method to simulate four distribution data. The aim of simulation is to explore the effects of four different estimation methods when estimating the variability of estimated variance components for generalizability theory. The four distribution data are normal distribution data, dichotomous distribution data, polytomous distribution data and skewed distribution data. It is common that researchers focus on normal distribution data and neglect non-normal distribution data, yet non-normal distribution data could always be seen in tests such as TOEFL and GRE. There are several methods to estimate the variability of variance components, including traditional, bootstrap, jackknife and Markov Chain Monte Carlo (MCMC). Former research by Li and Zhang (2009) shows that bootstrap method is significantlybetter than traditional, jackknife, and MCMC methods in estimating the variability for four distribution data. Bootstrap method has superior cross-distribution quality when estimating the variability of estimated variance components. Li and Zhang (2009) also suggest that bootstrap method should be adopted with a divide-and-conquer strategy to obtain good estimated standard error and estimated confidence interval and the criteria of such strategy should be set to: boot-p for person, boot-pi for item, and boot-i for person and item. However, it is unclear that which of the bootstrap methods (adjusted and unadjusted) is better for boot-p, boot-pi, and boot-i. Therefore, our study intends to probe into this comparison as well. This aim of the study is to explore whether adjusted bootstrap method is superior to unadjusted method in improving the estimation of variance components and their variability relative for generalizability theory. The simulation is implemented in R statistical programming environment. To simulate skewed data, HyperbolicDist package is used. Some criteria are set to compare the four methods. The bias is considered when variance components and their standard errors are estimated. The smaller the absolute bias is, the more reliable the result is. The criterion of confidence intervals is 80% interval coverage. If the 80% interval coverage is closer to 0.80, the confidence interval is more reliable. The results indicate that for four distribution data, adjusted bootstrap method is superior to unadjusted bootstrap method whether in point estimation of variance components or in variability estimation of variance components. For its improvement of the estimation of variance components and their variability for generalizability theory, adjusted bootstrap should be adopted as soon as possible.

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

This paper introduces the second edition of a software package for use in teaching cognitive psychology and research methods. Detailed are the distinctive features of the edition and how this courseware can be used effectively. Fifteen content programs were developed to provide multiple levels of use, ranging from simple demonstrations to sophisticated templates for designing complex independent research projects. Relative to its predecessor, data handling is greatly enhanced, the overall power of the programs to respond to a wider range of responses is increased, and the breadth of the content is expanded significantly.

<strong class="journal-contentHeaderColor">Abstract.</strong> This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (~100s m) to mesoscale (~10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

This paper describes an educational case study approach to FACTS controllers on an electric power network. Application software, SIMFACTS, was developed by the authors with the GUIDE tool from Matlabtrade in order to simulate the power system effects resulting from the inclusion of FACTS controllers. The studied topologies were: series, shunt and combined compensators. A simple explanation of several controllers, mainly STATCOM, SVC, TCSC, SSSC and UPFC is made in order to understand the singular concepts behind FACTS technology. A case study showing the main results regarding the impact of FACTS controllers on a steady-state operation of a test power network is presented. It is also included an example of the power flow solutions using one of SSSC controller. A sudden bus load change with a coupled STATCOM controller is also presented.

Python is a powerful high-level open source programming language that is available for multiple platforms. It supports object-oriented programming and has recently become a serious alternative to low-level compiled languages such as C + +. It is easy to learn and use, and is recognized for very fast development times, which makes it suitable for rapid software prototyping as well as teaching purposes. We motivate the use of Python and its free extension modules for high performance stand-alone applications in econometrics and statistics, and as a tool for gluing different applications together. (It is in this sense that Python forms a “unified” environment for statistical research.) We give details on the core language features, which will enable a user to immediately begin work, and then provide practical examples of advanced uses of Python. Finally, we compare the run-time performance of extended Python against a number of commonly-used statistical packages and programming environments. Supplemental materials are available for this article. Go to the publisher's online edition of Econometric Reviews to view the free supplemental file.

Oligotyping is a novel, supervised computational method that classifies closely related sequences into “oligotypes” (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

Methods for the Behavioral, Educational, and Social Sciences (MBESS; Kelley, 2007b) is an open source package for R (R Development Core Team, 2007b), an open source statistical programming language and environment. MBESS implements methods that are not widely available elsewhere, yet are especially helpful for the idiosyncratic techniques used within the behavioral, educational, and social sciences. The major categories of functions are those that relate to confidence interval formation for noncentral t, F, and chi2 parameters, confidence intervals for standardized effect sizes (which require noncentral distributions), and sample size planning issues from the power analytic and accuracy in parameter estimation perspectives. In addition, MBESS contains collections of other functions that should be helpful to substantive researchers and methodologists. MBESS is a long-term project that will continue to be updated and expanded so that important methods can continue to be made available to researchers in the behavioral, educational, and social sciences.

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code.