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Generating correlated data for omics simulation. [PDF]
PLoS Computational BiologySimulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each
Jianing Yang +2 more
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Evaluation of GENESIS, SAIGE, REGENIE and fastGWA-GLMM for genome-wide association studies of binary traits in correlated data [PDF]
Frontiers in Genetics, 2022 Performing a genome-wide association study (GWAS) with a binary phenotype using family data is a challenging task. Using linear mixed effects models is typically unsuitable for binary traits, and numerical approximations of the likelihood function may ...
Anastasia Gurinovich +9 more
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A fast kernel independence test for cluster-correlated data [PDF]
Scientific Reports, 2022 Cluster-correlated data receives a lot of attention in biomedical and longitudinal studies and it is of interest to assess the generalized dependence between two multivariate variables under the cluster-correlated structure.
Hoseung Song +2 more
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Confidence intervals construction for difference of two means with incomplete correlated data [PDF]
BMC Medical Research Methodology, 2016 Background Incomplete data often arise in various clinical trials such as crossover trials, equivalence trials, and pre and post-test comparative studies.
Hui-Qiong Li, Nian-Sheng Tang, Jie-Yi Yi
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Learning Gaussian graphical models from correlated data [PDF]
Frontiers in Systems BiologyGaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables.
Zeyuan Song +10 more
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Differential expression analysis for spatially correlated data using smiDE [PDF]
Genome BiologyDifferential expression is a key application of imaging spatial transcriptomics, moving analysis beyond cell type localization to examining cell state responses to microenvironments.
Ana Gabriela Vasconcelos +4 more
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Stats, 2023 The area under the true ROC curve (AUC) is routinely used to determine how strongly a given model discriminates between the levels of a binary outcome. Standard inference with the AUC requires that outcomes be independent of each other.
Camden Bay +4 more
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Correlated Initialization for Correlated Data [PDF]
Neural Processing Letters, 2022 Spatial data exhibits the property that nearby points are correlated. This also holds for learnt representations across layers, but not for commonly used weight initialization methods. Our theoretical analysis quantifies the learning behavior of weights of a single spatial filter.
openaire +2 more sources
A Family of Correlated Observations: From Independent to Strongly Interrelated Ones
Stats, 2020 This paper proposes a new classification of correlated data types based upon the relative number of direct connections among observations, producing a family of correlated observations embracing seven categories, one whose empirical counterpart currently
Daniel A. Griffith
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Spatial Linear Mixed Effects Modelling for OCT Images: SLME Model
Journal of Imaging, 2020 Much recent research focuses on how to make disease detection more accurate as well as “slimmer”, i.e., allowing analysis with smaller datasets. Explanatory models are a hot research topic because they explain how the data are generated.
Wenyue Zhu +5 more
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