27 papers · ranked by Valyu relevance
Dan Jackson, Richard Riley, Ian R White
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and…
Denis Polunin, Irina Shtaiger, Vadim Efimov
Biologists more and more have to deal with objects with non-numeric descriptions: texts (e.g. genetic sequences or even whole genomes), graphs, images, etc. There even could be no variables or descriptions at all when variability of objects is defined by similarity matrix. It is also possible to have too many variables…
Elyas Heidari, Vahid Balazadeh-Meresht, Ali Sharifi‐Zarchi
Increased application of multivariate data in many scientific areas has considerably raised the complexity of analysis and interpretation. Although quite a few approaches have been suggested to address this issue, there is still a gap between the most efficient proposed methods and available software. muvis is an R…
Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, Jérôme Saracco
'Jérôme Saracco'] Mixed data arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends standard multivariate analysis methods to incorporate this type of data. The key techniques/methods included in the package are principal component analysis for mixed…
Troy Vargason, Daniel P. Howsmon, Deborah L. McGuinness, Juergen Hahn
'Juergen Hahn'] Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls…
Glaucia Cristina Rodrigues Nascimento, Marcela Baraúna Magno, Giseon Heo, David Normando
High-dimensional data hinder sample visualization and limit exploration of data1. In these cases, we can make use of multivariate analysis techniques, such as Factor Analysis (FA) and/or Principal Component Analysis (PCA), to reduce a complex data set to one of lower dimensions so as to reveal any hidden features and…
Martin N. Hebart, Chris I. Baker
Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the…
Eladio J. Márquez, David Houle
Mutations virtually always have pleiotropic effects, yet most genome-wide association studies (GWAS) analyze effects one trait at a time. In order to investigate the performance of a multivariate approach to GWAS, we simulated scenarios where variation in a d-dimensional phenotype space was caused by a known subset of…
Ellen F. Mosleth, Kristian Hovde Liland
Modern analysis technologies output large amounts of multivariate data. The data may come from an experimental design or from other collected observations. We here present a flexible tool, called General Effect Modelling (GEM), for the analysis of any type of multivariate data influenced by one or more qualitative…
Eric Houngla Adjakossa, Ibrahim Sadissou, Mahouton Norbert Hounkonnou, Gregory Nuel + 1 more
'Gregory Nuel' 'Lourens J Waldorp'] In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more…
Diego Garrido-Martín, Miquel Calvo, Ferran Reverter, Roderic Guigó
The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations…
D. Vuckovic, P. Gasparini, N. Soranzo, V. Iotchkova
As new methods for multivariate analysis of Genome Wide Association Studies (GWAS) become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance based method for meta-analysis, generalized to an…
Dan Jackson, Ian R White, Richard D Riley
Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of…
Christopher James Rose, Unni Olsen, Maren Falch Lindberg, Eva Denison + 2 more
'Eva Denison' 'Arild Aamodt' 'Anners Lerdal'] Studies often estimate associations between an outcome and multiple variates. For example, studies of diagnostic test accuracy estimate sensitivity and specificity, and studies of predictive and prognostic factors typically estimate associations for multiple factors.…
Steven Geinitz, Reinhard Furrer, Stephan R. Sain
Classical analysis of variance requires that model terms be labeled as fixed or random and typically culminate by comparing variability from each batch (factor) to variability from errors; without a standard methodology to assess the magnitude of a batch's variability, to compare variability between batches, nor to…
Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing
We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised…
Jianjun Zhang, Qiuying Sha, Guanfu Liu, Xuexia Wang
There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests…
Authors not listed
While Raman spectroscopy offers notable experimental advantages as a probe of complex mixtures, its application in practice often confronts samples that present an overwhelming fluorescence background. Here, we explore the efficacy of two particular Raman spectrometric strategies for quantitative analysis under…
Authors not listed
Decades of extensive research have proved that β-amyloid (Aβ) peptides and their aggregation, inducing oxidative stress in the brain, play a key role in Alzheimer’s disease (AD) development. Moreover, Aβ peptides bind to Cu(II) ions, and the resulting complexes accelerate the aggregation process while promoting the…
Authors not listed
Presence of gold nanoparticles in an aqueous dispersion perturbs water molecules in their vicinity. Such water molecules form what is known as hydration shell and possess different vibrational attributes than those in the bulk dispersion. Raman spectroscopy was utilised to study these hydration shell water molecules…
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Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Angélique O. J. Cramer, Don van Ravenzwaaij, Dora Matzke, Helen Steingroever + 4 more
'Helen Steingroever' 'Ruud Wetzels' 'Raoul P. P. P. Grasman' 'Lourens J. Waldorp' 'Eric-Jan Wagenmakers'] Many psychologists do not realize that exploratory use of the popular multiway analysis of variance harbors a multiple-comparison problem. In the case of two factors, three separate null hypotheses are subject to…
William Fithian, Julie Josse
Multiple Correspondence Analysis (MCA) is a dimension reduction method which plays a large role in the analysis of tables with categorical nominal variables such as survey data. Though it is usually motivated and derived using geometric considerations, in fact we prove that it amounts to a single proximal Newtown step…
Ludwig A. Hothorn
In bio-medical studies, the p-values of the F-tests in ANOVA are usually interpreted independently as measures of the significance of the associated factors. This ’hidden multiplicity’ effect increases the false positive rate. Therefore, Cramer et al. (2016) proposed the Bonferroni adjustment of the p-values to control…
Authors not listed
MALDI MS analysis of liquid biopsy combined with ML enables non-invasive disease screening and monitoring. Presented an open-source R-based workflow covering all steps from raw data preprocessing to predictive model evaluation. The pipeline is customizable, transparent, and validated on clinical plasma samples from…
Steven Geinitz, Reinhard Furrer
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to the case where all levels of a factor are present in the data (i.e. the population of factor levels has been…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…