RESUMEN

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

Asunto(s)

RESUMEN

Nutrition outcomes (undernutrition, overweight, and obesity) among women are growing concerns across the globe. Currently, the rate of undernutrition and overweight among women in Nigeria is ranked among the highest in Africa. A major contributory factor reported is unstable food prices in the country. This study, therefore, examined the effects of food prices on nutrition outcomes among women in Nigeria. Secondary datasets retrieved from two different sources were used for this study. Cross-sectional data on weight and height for women were obtained from Nigeria Health Demographic Survey (NHDS). Data on monthly prices of the selected food items were obtained from the Nigeria Bureau of Statistics (NBS). The data were categorized into energy dense (yam tuber, garri, rice, and maize) and nutrient dense (egg, beef, and chicken). Multinomial logit regression was used to estimate the relationship between the prices of energy and nutrient-dense food prices concerning respondents' personal and environmental characteristics such as age, wealth status, and region; as well as the three nutrition outcomes for women (undernutrition, overnutrition, and obesity). This study revealed that the prevalence of overweight and obesity among women was 19.9% and 10.3%, respectively. Nutrition outcomes (obesity and overweight) were positively correlated with the price of energy-dense food with 0.2% and 0.3%, respectively. Nutrient-dense food price is negatively correlated with undernutrition with a probability of 0.1%. The study recommends that food policy instruments such as food prices and subsidies can be introduced to favor the consumption of healthier food to stem the prevalence of overweight and obesity in Nigeria.

RESUMEN

The computing environment has revolutionized the management and analysis of data in sciences during the last decades. This study aimed to evaluate the use of R software in research articles addressing the study of wildlife worldwide, particularly focusing on the research area "Veterinary Sciences". For this purpose, a systematic review mainly performed in the Web of Science database was conducted. Out of a total of 509 articles reviewed, our results show an increasing trend of the number of publications using the R software over time, as well as a wide geographical distribution at a global scale, particularly in North America, Europe, Australia and China. Most publications were categorized in research areas related to "Biological Sciences", while a minority of them was included in "Veterinary Sciences" (5.9%; 30/509). About the species groups assessed, many articles evaluated a single species group (96.5%), being mammals (50.7%) and birds (14.8%) the most studied ones. The present study showed a high variety of R-packages used in the publications reviewed, all of them related to data analysis, the study of genetic/phylogenetic information and graphical representation. Interestingly, the common use of packages between different research areas is indicative of the high interest of using R software in scientific articles. Our study points the R software as an open-source programming language that allows to support research addressing the study of wildlife, becoming a key software for many research areas, including "Veterinary Sciences". However, an in-depth methodological description about the use of R software in publications to improve the tracking, reproducibility and transparency is encouraged.

Asunto(s)

RESUMEN

The need for a comprehensive consolidated guide for R packages and tools that are used in microbiome data analysis is significant; thus, we aim to provide a detailed step-by-step dissection of the most used R packages and tools in the field of microbiome data integration and analysis. The guideline aims to be a user-friendly simplification and tutorial on five main packages, namely phyloseq, MegaR, DADA2, Metacoder, and microbiomeExplorer due to their high efficiency and benefit in microbiome data analysis.

Asunto(s)

RESUMEN

Introduction: Kidney renal clear cell carcinoma (KIRC) is a common cancer in people worldwide, and one of the main issues is developing suitable biomarkers. This study aims to investigate the expression of TSTD2 in KIRC and its impact on prognosis. Methods: RNA sequencing data from TCGA and GTEx were gathered to examine the functional enrichment of TSTD2-related differentially expressed genes (DEGs) using GO/KEGG, GSEA, immunocyte permeation analysis, and protein-protein interaction (PPI) network analysis. The Kaplan‒Meier-Cox regression model and the prognostic nomograph model were used to assess the clinical importance of TSTD2 in KIRC. R software was used to analyze the included studies. Finally, verification of cells and tissues was performed using immunohistochemical staining and quantitative real‒time PCR. Results: In contrast to normal samples, it was discovered that TSTD2 was underexpressed in a number of malignancies, including KIRC. Furthermore, in 163 KIRC samples, low expression of TSTD2 was linked to a poor prognosis, as were subgroups with age greater than 60, the integrin pathway, the development of elastic fibers, and high TNM stage, pathologic stage, and histologic grade (P < 0.05). Age and TNM stage were included in the nomogram prognostic model, and low TSTD2 was a prognostic predictor that could be used independently in Cox regression analysis. In addition, 408 DEGs with 111 upregulated genes and 297 downregulated genes were found between the high- and low-expression groups. Conclusion: The diminished expression of TSTD2 may serve as a biomarker for unfavorable outcomes in KIRC, and holds potential as a target for therapeutic interventions.

RESUMEN

The landscape of analytical tools for population genomics continues to evolve. However, these tools are scattered across programming languages, making them largely inaccessible for many biologists. In this issue of Molecular Ecology Resources, Hemstrom and Jones, 2022 (Mol Ecol Resour; 962) introduce a new R package, snpR. This package combines a large number of existing analyses, to provide a one-stop shop for population genomics. F-statistics, admixture analyses, effective population size inferences, genome-wide association studies (GWAS), and parentage analyses are all implemented natively within the package. A variety of third-party software can also be run without leaving the R environment. The authors pay particular attention to data structure - avoiding redundancy - and allowing analyses to be run across multiple sample or single-nucleotide polymorphism (SNP) groupings. Because of its great accessibility and wide range of analyses, snpR has the potential to become a favourite within the Molecular Ecology community.

Asunto(s)

RESUMEN

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.

Asunto(s)

RESUMEN

BACKGROUND: In clinical trial development, it is a critical step to submit applications, amendments, supplements, and reports on medicinal products to regulatory agencies. The electronic common technical document is the standard format to enable worldwide regulatory submission. There is a growing trend of using R for clinical trial analysis and reporting as part of regulatory submissions, where R functions, analysis scripts, analysis results, and all proprietary code dependencies are required to be included. One unmet and significant gap is the lack of tools, guidance, and publicly available examples to prepare submission R programs following the electronic common technical document specification. METHODS: We introduce a simple and sufficient R package, pkglite, to convert analysis scripts and associated proprietary dependency R packages into a compact, text-based file, which makes the submission document self-contained, easy to restore, transfer, review, and submit following the electronic common technical document specification and regulatory guidelines (e.g. the study data technical conformance guide from the US Food and Drug Administration). The pkglite R package is published on Comprehensive R Archive Network and developed on GitHub. RESULTS: As a tool, pkglite can pack and unpack multiple R packages with their dependencies to facilitate the reproduction and make it an off-the-shelf tool for both sponsors and reviewers. As a grammar, pkglite provides an explicit trace of the packing scope using the concept of file specifications. As a standard, pkglite offers an open file format to represent and exchange R packages as a text file. We use a mock-up example to demonstrate the workflow of using pkglite to prepare submission programs following the electronic common technical document specification. CONCLUSION: pkglite and the proposed workflow enable the sponsor to submit well-organized R scripts following the electronic common technical document specification. The workflow has been used in the first publicly available R-based submission to the US Food and Drug Administration by the R Consortium R submission working group (https://www.r-consortium.org/blog/2022/03/16/update-successful-r-based-test-package-submitted-to-fda).

Asunto(s)

RESUMEN

Abstract: R Statistics is a comprehensive and widely-used suite of packages for statistical operations. From 27 R packages indexed with the word "Rasch", 11 packages capable of Rasch estimation and analysis are identified and critiqued. A commercial Rasch application is included for comparison. Three R data frames are used. A larger and a smaller 0/1 data frame are analyzed with the Dichotomous Rasch Model. A polytomous 0/1/2 data frame is analyzed with the Partial Credit Model. The R packages can all use the same data frame. They are easy to use and mostly fast, though their documentation is generally skimpy. Every package has obvious shortcomings, but the unique features of each package could make them all useful. For general Rasch estimation and fit analysis of dichotomous data, three packages stand out: eRm, TAM and autoRasch. Two packages stand out for polytomous data: TAM and autoRasch.

RESUMEN

BACKGROUND: Recent deep sequencing technologies have proven to be valuable resources to gain insights into the expression profiles of diverse tRNAs. However, despite these technologies, the association of tRNAs with diverse diseases has not been explored in depth because analytical tools are lacking. RESULTS: We developed a user-friendly tool, tRNA Expression Analysis Software Utilizing R for Easy use (tReasure), to analyze differentially expressed tRNAs (DEtRNAs) from deep sequencing data of small RNAs using R packages. tReasure can quantify individual mature tRNAs, isodecoders, and isoacceptors. By adopting stringent mapping strategies, tReasure supports the precise measurement of mature tRNA read counts. The whole analysis workflow for determining DEtRNAs (uploading FASTQ files, removing adapter sequences and poor-quality reads, mapping and quantifying tRNAs, filtering out low count tRNAs, determining DEtRNAs, and visualizing statistical analysis) can be performed with the tReasure package. CONCLUSIONS: tReasure is an open-source software available for download at https://treasure.pmrc.re.kr and will be indispensable for users who have little experience with command-line software to explore the biological implication of tRNA expression.

Asunto(s)

RESUMEN

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

RESUMEN

Acute myeloid leukemia (AML) is a frequent malignancy in adults worldwide; identifying preferable biomarkers has become one of the current challenges. Given that COMMD7 has been reported associated with tumor progression in various human solid cancers but rarely reported in AML, herein, RNA sequencing data from TCGA and GTEx were obtained for analysis of COMMD7 expression and differentially expressed gene (DEG). Furthermore, functional enrichment analysis of COMMD7-related DEGs was performed by GO/KEGG, GSEA, immune cell infiltration analysis, and protein-protein interaction (PPI) network. In addition, the clinical significance of COMMD7 in AML was figured out by Kaplan-Meier Cox regression and prognostic nomogram model. R package was used to analyze incorporated studies. As a result, COMMD7 was highly expressed in various malignancies, including AML, compared with normal samples. Moreover, high expression of COMMD7 was associated with poor prognosis in 151 AML samples, as well as subgroups with age >60, NPM1 mutation-positive, FLT3 mutation-negative, and DNMT3A mutation-negative, et al. (P < 0.05). High COMMD7 was an independent prognostic factor in Cox regression analysis; Age and cytogenetics risk were included in the nomogram prognostic model. Furthermore, a total of 529 DEGs were identified between the high- and the low- expression group, of which 92 genes were up-regulated and 437 genes were down-regulated. Collectively, high expression of COMMD7 is a potential biomarker for adverse outcomes in AML. The DEGs and pathways recognized in the study provide a preliminary grasp of the underlying molecular mechanisms of AML carcinogenesis and progression.

Asunto(s)

RESUMEN

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages is also discussed.

Asunto(s)

RESUMEN

Data pre-processing of the LC-MS data is a critical step in untargeted metabolomics studies in order to achieve correct biological interpretations. Several tools have been developed for pre-processing, and these can be classified into either commercial or open source software. This case report aims to compare two specific methodologies, Agilent Profinder vs. R pipeline, for a metabolomic study with a large number of samples. Specifically, 369 plasma samples were analyzed by HPLC-ESI-QTOF-MS. The collected data were pre-processed by both methodologies and later evaluated by several parameters (number of peaks, degree of missingness, quality of the peaks, degree of misalignments, and robustness in multivariate models). The vendor software was characterized by ease of use, friendly interface and good quality of the graphs. The open source methodology could more effectively correct the drifts due to between and within batch effects. In addition, the evaluated statistical methods achieved better classification results with higher parsimony for the open source methodology, indicating higher data quality. Although both methodologies have strengths and weaknesses, the open source methodology seems to be more appropriate for studies with a large number of samples mainly due to its higher capacity and versatility that allows combining different packages, functions, and methods in a single environment.

RESUMEN

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a "color space," producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance, a technique borrowed from computer vision, that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions of colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

RESUMEN

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages ( https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB ), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers ( https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu ). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

Asunto(s)

RESUMEN

The Child Language Data Exchange System (CHILDES) has played a critical role in research on child language development, particularly in characterizing the early language learning environment. Access to these data can be both complex for novices and difficult to automate for advanced users, however. To address these issues, we introduce childes-db, a database-formatted mirror of CHILDES that improves data accessibility and usability by offering novel interfaces, including browsable web applications and an R application programming interface (API). Along with versioned infrastructure that facilitates reproducibility of past analyses, these interfaces lower barriers to analyzing naturalistic parent-child language, allowing for a wider range of researchers in language and cognitive development to easily leverage CHILDES in their work.

Asunto(s)

RESUMEN

DNA barcoding is now a common tool in parasitology and epidemiology, which require good methods for identification not only of parasites and pathogens but vectors and reservoirs. This special issue presents some advances and challenges in barcoding of microbes, parasites, and their vectors and reservoirs. DNA barcoding found new applications in disease ecology, conservation parasitology, environmental parasitology and in paleoparasitology. New technologies such as next-generation sequencing and matrix-assisted laser desorption-ionization time-of-flight have made it now possible to investigate large samples of specimens. By allowing the investigation of parasites at the interface between environment, biodiversity, animal and human health, barcoding and biobanking have important policy outcomes as well as ethics and legal implications. The special issue 'Advances and challenges in the barcoding of parasites, vectors and reservoirs' illustrates some recent advances and proposes new avenues for research in barcoding in parasitology.

Asunto(s)

RESUMEN

Longitudinal, prospective studies often rely on multi-omics approaches, wherein various specimens are analyzed for genomic, metabolomic, and/or transcriptomic profiles. In practice, longitudinal studies in humans and other animals routinely suffer from subject dropout, irregular sampling, and biological variation that may not be normally distributed. As a result, testing hypotheses about observations over time can be statistically challenging without performing transformations and dramatic simplifications to the dataset, causing a loss of longitudinal power in the process. Here, we introduce splinectomeR, an R package that uses smoothing splines to summarize data for straightforward hypothesis testing in longitudinal studies. The package is open-source, and can be used interactively within R or run from the command line as a standalone tool. We present a novel in-depth analysis of a published large-scale microbiome study as an example of its utility in straightforward testing of key hypotheses. We expect that splinectomeR will be a useful tool for hypothesis testing in longitudinal microbiome studies.