scholarly journals Reproducible analysis of disease space via principal components using the novel R package syndRomics

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Abel Torres-Espín ◽  
Austin Chou ◽  
J Russell Huie ◽  
Nikos Kyritsis ◽  
Pavan S Upadhyayula ◽  
...  
Keyword(s):  
Complex Disease ◽  
Principal Component ◽  
R Package ◽  
Analytical Framework ◽  
Underlying Disease ◽  
Primary Outcome Measure ◽  
Biomedical Data ◽  
Disease States ◽  
Reproducible Analysis ◽  
Main Components

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

scholarly journals A U-statistics for integrative analysis of multilayer omics data

2020 ◽  
Vol 36 (8) ◽  
pp. 2365-2374
Author(s):  
Xiaqiong Wang ◽  
Yalu Wen
Keyword(s):  
Complex Disease ◽  
R Package ◽  
Underlying Disease ◽  
Supplementary Information ◽  
Disease Models ◽  
Omics Data ◽  
Robust Performance ◽  
Weighting Schemes ◽  
U Statistics ◽  
Different Types

Abstract Motivation The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges. Results We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods. Availability and implementation The R-package is available at https://github.com/YaluWen/Uomic. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

2021 ◽  
Vol 22 (3) ◽  
pp. 1399
Author(s):  
Salim Ghannoum ◽  
Waldir Leoncio Netto ◽  
Damiano Fantini ◽  
Benjamin Ragan-Kelley ◽  
Amirabbas Parizadeh ◽  
...  
Keyword(s):  
Single Cell ◽  
Biomarker Discovery ◽  
Enrichment Analysis ◽  
Myxoid Liposarcoma ◽  
R Package ◽  
Differential Analysis ◽  
A Cell ◽  
Reproducible Analysis ◽  
Transcriptomic Level ◽  
User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

scholarly journals Mid-Infrared Spectroscopy as a Rapid Tool to Qualitatively Predict the Effects of Species, Regions and Roasting on the Nutritional Composition of Australian Acacia Seed Species

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1879
Author(s):  
Oladipupo Q. Adiamo ◽  
Yasmina Sultanbawa ◽  
Daniel Cozzolino
Keyword(s):  
Principal Component ◽  
Nutritional Composition ◽  
Human Consumption ◽  
Mid Infrared ◽  
Mir Spectroscopy ◽  
As Species ◽  
Before And After ◽  
Mir Spectra ◽  
Main Components ◽  
Rapid Tool

In recent times, the popularity of adding value to under-utilized legumes have increased to enhance their use for human consumption. Acacia seed (AS) is an underutilized legume with over 40 edible species found in Australia. The study aimed to qualitatively characterize the chemical composition of 14 common edible AS species from 27 regions in Australia using mid-infrared (MIR) spectroscopy as a rapid tool. Raw and roasted (180 °C, 5, 7, and 9 min) AS flour were analysed using MIR spectroscopy. The wavenumbers (1045 cm−1, 1641 cm−1, and 2852–2926 cm−1) in the MIR spectra show the main components in the AS samples. Principal component analysis (PCA) of the MIR data displayed the clustering of samples according to species and roasting treatment. However, regional differences within the same AS species have less of an effect on the components, as shown in the PCA plot. Statistical analysis of absorbance at specific wavenumbers showed that roasting significantly (p < 0.05) reduced the compositions of some of the AS species. The results provided a foundation for hypothesizing the compositional similarity and/or differences among AS species before and after roasting.

Anemia of Inflammation

Hematology ◽  
2010 ◽  
Vol 2010 (1) ◽  
pp. 276-280 ◽  
Author(s):  
Cindy N. Roy
Keyword(s):  
Inflammatory Diseases ◽  
Underlying Disease ◽  
Comorbid Condition ◽  
Regulatory Pathways ◽  
Hepcidin Expression ◽  
Disease States ◽  
Poor Outcomes ◽  
The Impact ◽  
Inflammatory Insult ◽  
Anemia Of Inflammation

Abstract Inflammation arising from various etiologies, including infection, autoimmune disorders, chronic diseases, and aging, can promote anemia. The anemia of inflammation (AI) is most often normocytic and normochromic and is usually mild. Characteristic changes in systemic iron handling, erythrocyte production, and erythrocyte life span all contribute to AI. The preferred treatment is directed at the underlying disease. However, when the inflammatory insult is intractable, or the cause has not been diagnosed, there are limited options for treatment of AI. Because anemia is a comorbid condition that is associated with poor outcomes in various chronic disease states, understanding its pathogenesis and developing new tools for its treatment should remain a priority. Hepcidin antimicrobial peptide has taken center stage in recent years as a potent modulator of iron availability. As the technology for quantitative hepcidin analysis improves, hepcidin's role in various disease states is also being revealed. Recent insights concerning the regulatory pathways that modify hepcidin expression have identified novel targets for drug development. As the field advances with such therapeutics, the analysis of the impact of normalized hemoglobin on disease outcomes will confirm whether anemia is a reversible independent contributor to the morbidity and mortality associated with inflammatory diseases.

scholarly journals The food patterns of a multicenter cohort of Brazilian nulliparous pregnant women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria J. Miele ◽  
Renato T. Souza ◽  
Iracema M. Calderon ◽  
Francisco E. Feitosa ◽  
Débora F. Leite ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Dietary Patterns ◽  
Principal Component ◽  
Good Representation ◽  
Processed Foods ◽  
Food Groups ◽  
Dietary Recall ◽  
Complex Process ◽  
Nutrient Consumption ◽  
Main Components

AbstractAssessment of human nutrition is a complex process, in pregnant women identify dietary patterns through mean nutrient consumption can be an opportunity to better educate women on how to improve their overall health through better eating. This exploratory study aimed to identify a posteriori dietary patterns in a cohort of nulliparous pregnant women. The principal component analysis (PCA) technique was performed, with Varimax orthogonal rotation of data extracted from the 24-h dietary recall, applied at 20 weeks of gestation. We analysed 1.145 dietary recalls, identifying five main components that explained 81% of the dietary pattern of the sample. Dietary patterns found were: Obesogenic, represented by ultra-processed foods, processed foods, and food groups rich in carbohydrates, fats and sugars; Traditional, most influenced by natural, minimally processed foods, groups of animal proteins and beans; Intermediate was similar to the obesogenic, although there were lower loads; Vegetarian, which was the only good representation of fruits, vegetables and dairy products; and Protein, which best represented the groups of proteins (animal and vegetable). The obesogenic and intermediate patterns represented over 37% of the variation in food consumption highlighting the opportunity to improve maternal health especially for women at first mothering.

Idiopathic juvenile osteoporosis in a child: a four-year follow-up with review of literature

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Aashima Dabas ◽  
Rakhi Malhotra ◽  
Ravindra Kumar ◽  
Rajesh Khadgawat
Keyword(s):  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Underlying Disease ◽  
Bone Fragility ◽  
Management Approach ◽  
Primary Osteoporosis ◽  
Disease States ◽  
Idiopathic Juvenile Osteoporosis ◽  
Density Lipoprotein Receptor

Abstract Objectives Childhood osteoporosis is an uncommon condition that usually develops secondary to underlying disease states. Idiopathic juvenile osteoporosis or early onset osteoporosis is a rare cause of primary osteoporosis in childhood associated with mutations in “bone fragility” genes. Case presentation The index case presented with upper back pain and was detected to have multiple vertebral fractures. Further workup for the cause revealed a homozygous benign mutation in low-density lipoprotein receptor-related protein 5, which was also detected in the mother who remained asymptomatic till presentation. The child was successfully treated with intravenous zoledronate. Conclusions The case report describes the management approach and four-year follow-up of the child.

A Semantic Differential for Evaluating the Sound Quality of Fan Systems

2017 ◽  
Author(s):  
Carolin Feldmann ◽  
Thomas Carolus ◽  
Marc Schneider
Keyword(s):  
Principal Component ◽  
Semantic Differential ◽  
Sound Quality ◽  
Heat Pumps ◽  
First Case ◽  
Objective Metrics ◽  
Main Components ◽  
Air Conditioning Systems ◽  
The University

Fans are main components e.g. in heating, ventilating and air conditioning systems for vehicles or buildings, cooling units of engines and electronic circuits, and household appliances such as kitchen exhaust hoods or vacuum cleaners. End-users increasingly demand a high sound quality of their system or device. The overall objective of a recent research project at the University of Siegen is a multidimensional assessment of fan sound quality. In a first step an advanced novel semantic differential for the assessment of fan-related sounds is established with the aid of carefully designed jury tests. Eventually, this semantic differential is employed for sound quality jury tests of fans in kitchen exhaust hoods, heat pumps and air purifiers as a first case. Finally, a prediction model is suggested, which relates the outcome from the jury tests to objective metrics. A principal component analysis is carried out and yields five main assessment criteria with 23 relevant adjective scales. The results show that the perceived sound quality of fan systems is mainly determined by the loudness and tonality of the sound. The spectral content (represented by the sharpness) as well as the time structure (represented by the roughness) have no significant impact on perceived sound quality of the fan systems investigated.

scholarly journals strandCet: R package for estimating natural and non-natural mortality-at-age of cetaceans from age-structured strandings

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5768 ◽  
Author(s):  
Camilo Saavedra
Keyword(s):  
Total Mortality ◽  
R Package ◽  
Analytical Framework ◽  
Population Projections ◽  
Sources Of Information ◽  
Mortality Models ◽  
Credible Intervals ◽  
Leslie Matrices ◽  
Age Structured ◽  
Derived Data

Mortality is one of the most important parameters for the study of population dynamics. One of the main sources of information to calculate the mortality of cetaceans arises from the observed age-structure of stranded animals. A method based on an adaptation of a Heligman-Pollard model is proposed. A freely accessible package of functions (strandCet) has been created to apply this method in the statistical software R. Total, natural, and anthropogenic mortality-at-age is estimated using only data of stranded cetaceans whose age is known. Bayesian melding estimation with Incremental Mixture Importance Sampling is used for fitting this model. This characteristic, which accounts for uncertainty, further eases the estimation of credible intervals. The package also includes functions to perform life tables, Siler mortality models to calculate total mortality-at-age and Leslie matrices to derive population projections. Estimated mortalities can be tested under different scenarios. Population parameters as population growth, net production or generation time can be derived from population projections. The strandCet R package provides a new analytical framework to assess mortality in cetacean populations and to explore the consequences of management decisions using only stranding-derived data.

scholarly journals IMPLEMENTASI R-SHINY UNTUK ANALISIS BIPLOT KOMPONEN UTAMA (Studi Kasus: Penggunaan Alat Kontrasepsi pada Peserta Aktif KB di Provinsi Jawa Tengah Tahun 2019)

2022 ◽  
Vol 10 (4) ◽  
pp. 499-507
Author(s):  
Andreanto Andreanto ◽  
Hasbi Yasin ◽  
Agus Rusgiyono
Keyword(s):  
Open Source Software ◽  
Birth Rate ◽  
Principal Component ◽  
Contraceptive Device ◽  
Biplot Analysis ◽  
Contraceptive Devices ◽  
The Family ◽  
R Shiny ◽  
Main Components ◽  
The Relationship

The population problem is a fairly complex and complicated problem. Therefore, Indonesia seeks to control the birth rate with the Family Planning program. The implementation of this program can be evaluated through statistical data. The statistical analysis used is biplot principal component analysis to see the relationship between districts/cities in choosing the contraceptive device/method used, the variance of each contraceptive device/method, the correlation between contraceptive devices/methods, and the superiority value of the contraceptive device/method in the population. each district/city. The problem with performing the analysis is the limitations of easy-to-use open source software. As with R, users must understand writing code to perform data analysis. Therefore, to perform a biplot analysis of the principal components, an RShiny application has been created using RStudio. The R-Shiny that has been made has many  advantages,  including  complete  results  which  include  data  display,  data transformation, SVD matrix, to graphs along with plot graph interpretation. The results of the principal component biplot analysis using R-Shiny with α =1 have the advantage of a good principal component biplot, which is 95.63%. This shows that the biplot interpretation of the main components produced can be explained well the relationship between the district/city and the contraceptive methods/devices used. 

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