ResearchPad - k-means-clustering https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Design of composite measure schemes for comparative severity assessment in animal-based neuroscience research: A case study focussed on rat epilepsy models]]> https://www.researchpad.co/article/elastic_article_14687 Comparative severity assessment of animal models and experimental interventions is of utmost relevance for harm-benefit analysis during ethical evaluation, an animal welfare-based model prioritization as well as the validation of refinement measures. Unfortunately, there is a lack of evidence-based approaches to grade an animal’s burden in a sensitive, robust, precise, and objective manner. Particular challenges need to be considered in the context of animal-based neuroscientific research because models of neurological disorders can be characterized by relevant changes in the affective state of an animal. Here, we report about an approach for parameter selection and development of a composite measure scheme designed for precise analysis of the distress of animals in a specific model category. Data sets from the analysis of several behavioral and biochemical parameters in three different epilepsy models were subjected to a principal component analysis to select the most informative parameters. The top-ranking parameters included burrowing, open field locomotion, social interaction, and saccharin preference. These were combined to create a composite measure scheme (CMS). CMS data were subjected to cluster analysis enabling the allocation of severity levels to individual animals. The results provided information for a direct comparison between models indicating a comparable severity of the electrical and chemical post-status epilepticus models, and a lower severity of the kindling model. The new CMS can be directly applied for comparison of other rat models with seizure activity or for assessment of novel refinement approaches in the respective research field. The respective online tool for direct application of the CMS or for creating a new CMS based on other parameters from different models is available at https://github.com/mytalbot/cms. However, the robustness and generalizability needs to be further assessed in future studies. More importantly, our concept of parameter selection can serve as a practice example providing the basis for comparable approaches applicable to the development and validation of CMS for all kinds of disease models or interventions.

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<![CDATA[Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE]]> https://www.researchpad.co/article/5c8accc7d5eed0c48498ffa7

Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. DEPECHE is implemented in the open source R package DepecheR currently available at github.com/Theorell/DepecheR.

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<![CDATA[Discriminating severe seasonal allergic rhinitis. Results from a large nation-wide database]]> https://www.researchpad.co/article/5c084234d5eed0c484fcc2c0

Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into “mild”, “moderate”, and “severe”, through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8–9 for mild to moderate and of 11–12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach’s α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864).

The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention.

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