scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

Chemometric exploratory data analysis for patients with diabetes type 2 and diabetic complications

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Miroslava Nedyalkova ◽  
Ralitsa Robeva ◽  
Atanaska Elenkova ◽  
Vasil Simeonov
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Diabetes Type 2 ◽  
Latent Factors ◽  
Multivariate Statistical ◽  
Patients With Diabetes ◽  
Exploratory Data ◽  
Components Analysis

Abstract The present study deals with the interpretation and modeling of clinical data for patients with diabetes mellitus type 2 (DMT2) additionally diagnosed with complications of the disease by the use of multivariate statistical methods. The major goal is to determine some specific clinical descriptors characterizing each health problem by applying the options of the exploratory data analysis. The results from the statistical analysis are commented in details by medical reasons for each of the complications. It was found that each of the complications is characterized by specific medical descriptors linked into each one of the five latent factors identified by factor and principal components analysis. Such an approach to interpret concomitant to DMT2 complications is original and allows a better understanding of the role of clinical parameters for diagnostic and prevention goals.

scholarly journals Comparing single cell datasets using DensityMorph

2021 ◽  
Author(s):  
Kristen Feher
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Cell Population ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Data Visualisation ◽  
Biological Interpretation ◽  
Exploratory Data ◽  
Visualisation Method ◽  
Time Point

The proliferation of single cell datasets has brought a wealth of information, but also great challenges in data analysis. Obtaining a cohesive overview of multiple single cell samples is difficult and requires consideration of cell population structure - which may or may not be well defined - along with subtle shifts in expression within cell populations across samples, and changes in population frequency across samples. Ideally, all this would be integrated with the experimental design, e.g. time point, genotype, treatment etc. Data visualisation is the most effective way of communicating analysis but often this takes the form of a plethora of t-SNE plots, colour coded according to marker and sample. In this manuscript, I introduce a novel exploratory data analysis and visualisation method that is centred around a novel quasi-distance (DensityMorph) between single cell samples. DensityMorph makes it possible to plot single cell samples in a manner analogous to performing principal component analysis on microarray samples. Biological interpretation is ensured by the introduction of Explanatory Components, which show how marker expression and coexpression drive the differences between samples. This method is a breakthrough in terms of displaying the most pertinent biological changes across single cell samples in a compact plot. Finally, it can be used either as a stand-alone method or to structure other types of analysis such as manual flow cytometry gating or cell population clustering.

scholarly journals Mimicking Complexity of Structured Data Matrix’s Information Content: Categorical Exploratory Data Analysis

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 594
Author(s):  
Fushing Hsieh ◽  
Elizabeth P. Chou ◽  
Ting-Li Chen
Keyword(s):  
Data Analysis ◽  
Information Content ◽  
Exploratory Data Analysis ◽  
Conditional Entropy ◽  
Principal Component ◽  
Structured Data ◽  
Data Matrix ◽  
The Social ◽  
Exploratory Data ◽  
Reliability And Robustness

We develop Categorical Exploratory Data Analysis (CEDA) with mimicking to explore and exhibit the complexity of information content that is contained within any data matrix: categorical, discrete, or continuous. Such complexity is shown through visible and explainable serial multiscale structural dependency with heterogeneity. CEDA is developed upon all features’ categorical nature via histogram and it is guided by all features’ associative patterns (order-2 dependence) in a mutual conditional entropy matrix. Higher-order structural dependency of k(≥3) features is exhibited through block patterns within heatmaps that are constructed by permuting contingency-kD-lattices of counts. By growing k, the resultant heatmap series contains global and large scales of structural dependency that constitute the data matrix’s information content. When involving continuous features, the principal component analysis (PCA) extracts fine-scale information content from each block in the final heatmap. Our mimicking protocol coherently simulates this heatmap series by preserving global-to-fine scales structural dependency. Upon every step of mimicking process, each accepted simulated heatmap is subject to constraints with respect to all of the reliable observed categorical patterns. For reliability and robustness in sciences, CEDA with mimicking enhances data visualization by revealing deterministic and stochastic structures within each scale-specific structural dependency. For inferences in Machine Learning (ML) and Statistics, it clarifies, upon which scales, which covariate feature-groups have major-vs.-minor predictive powers on response features. For the social justice of Artificial Intelligence (AI) products, it checks whether a data matrix incompletely prescribes the targeted system.

scholarly journals Diabetes mellitus type 2: Exploratory data analysis based on clinical reading

2020 ◽  
Vol 18 (1) ◽  
pp. 1041-1053
Author(s):  
Miroslava Nedyalkova ◽  
Sergio Madurga ◽  
Davide Ballabio ◽  
Ralitsa Robeva ◽  
Julia Romanova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Data Analysis ◽  
Health Status ◽  
Diabetes Mellitus Type 2 ◽  
Exploratory Data Analysis ◽  
Diabetes Mellitus Type ◽  
Control Group ◽  
Rapid Monitoring ◽  
Exploratory Data

AbstractDiabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of patients and more detailed approach to the health status of DMT2 patients.

Methods and Graphical Tools for Exploratory Data Analysis of Artificial Olfaction Experiments

2012 ◽  
pp. 317-339 ◽  
Author(s):  
Matteo Falasconi ◽  
Matteo Pardo ◽  
Giorgio Sberveglieri
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Linear Mapping ◽  
Multivariate Statistical ◽  
Human Errors ◽  
Full Dataset ◽  
Exploratory Data ◽  
Visualization Of Data ◽  
Data Investigation

Visualization and initial examination of the Electronic Nose data is one of the most important parts of the data analysis cycle. This aspect of data investigation should ideally be performed iteratively together with data collection in order to optimize experimental protocols and final results. Once exploration has been completed, a complete supervised data analysis on a full dataset can be run, leading to prediction and thereby to e-nose performance evaluation. Exploratory Data Analysis (EDA) comprises three tasks: checking the quality of the data, calculating summary statistics, and producing plots of the data to get a feel of their structure. Graphical visualization of data allows checking for instrumental malfunctioning, discovering human errors, removing outliers, understanding the influence of experimental parameters, verifying the ability of the machine in discriminating the examined samples, and eventually formulating new hypotheses. A number of different techniques have been developed for data visualization, including multivariate statistical analysis, non-linear mapping, and clustering techniques. This chapter will present an overview of methods, tools, and software for EDA of artificial olfaction experiments. These will cover visualization and data mining tools for both raw and preprocessed data, such as: histograms, scatter plots, feature and box plots, Principal Component Analysis (PCA), Cluster Analysis (CA), and Cluster Validity (CV). Some case studies that demonstrate the application of the methods to specific chemical sensing problems will be illustrated.

scholarly journals The Prevalence of Metabolic Syndrome and Its Components among People with Type 2 Diabetes in the Ho Municipality, Ghana: A Cross-Sectional Study

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
James Osei-Yeboah ◽  
William K. B. A. Owiredu ◽  
Gameli Kwame Norgbe ◽  
Sylvester Yao Lokpo ◽  
Jones Gyamfi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
High Blood Pressure ◽  
Abdominal Obesity ◽  
Principal Component ◽  
Cross Sectional ◽  
Predominant Component ◽  
Study Population

The cooccurrence of diabetes mellitus and metabolic syndrome potentiates the cardiovascular risk associated with each of the conditions; therefore characterizing metabolic syndrome among people with type 2 diabetes is beneficial for the purpose of cardiovascular disease prevention. This study aims at evaluating the prevalence of metabolic syndrome and its components among 162 patients with type 2 diabetes attending the diabetic clinic of the Ho Municipal Hospital, Ghana. Data obtained included anthropometric indices, blood pressure, serum lipids, glucose, and sociodemographics and clinical information. The overall prevalence of metabolic syndrome among the study population was 43.83%, 63.58%, and 69.14% using the NCEP-ATP III, the WHO, and the IDF criteria, respectively. The most predominant component among the study population was high blood pressure using the NCEP-ATP III (108 (66.67%)) and WHO (102 (62.96)) criteria and abdominal obesity (112 (69.14%)) for IDF criteria. High blood pressure was the most prevalent component among the males while abdominal obesity was the principal component among the females. In this population with type 2 diabetes, high prevalence of metabolic syndrome exists. Gender vulnerability to metabolic syndrome and multiple cluster components were skewed towards the female subpopulation with type 2 diabetes.

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