LiMM‐PCA: Combining ASCA + and linear mixed models to analyse high‐dimensional designed data

2020 ◽  
Vol 34 (6) ◽  
Author(s):  
Manon Martin ◽  
Bernadette Govaerts
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
High Dimensional

scholarly journals CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

F1000Research ◽  
2019 ◽  
Vol 6 ◽  
pp. 748 ◽  
Author(s):  
Malgorzata Nowicka ◽  
Carsten Krieg ◽  
Helena L. Crowell ◽  
Lukas M. Weber ◽  
Felix J. Hartmann ◽  
...  
Keyword(s):  
Cell Population ◽  
High Throughput ◽  
Mixed Models ◽  
Exploratory Data Analysis ◽  
Linear Mixed Models ◽  
High Dimensional ◽  
Cell Populations ◽  
Dimensional Scaling ◽  
Exploratory Data

High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signalling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals).

scholarly journals Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models

2018 ◽  
Vol 67 (4) ◽  
pp. 813-839 ◽  
Author(s):  
Anna Bonnet ◽  
Céline Lévy‐Leduc ◽  
Elisabeth Gassiat ◽  
Roberto Toro ◽  
Thomas Bourgeron
Keyword(s):  
Variable Selection ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
High Dimensional ◽  
Heritability Estimation ◽  
Selection Approach

scholarly journals Fixed Effects Testing in High-Dimensional Linear Mixed Models

2020 ◽  
Vol 115 (532) ◽  
pp. 1835-1850
Author(s):  
Jelena Bradic ◽  
Gerda Claeskens ◽  
Thomas Gueuning
Keyword(s):  
Mixed Models ◽  
Fixed Effects ◽  
Linear Mixed Models ◽  
High Dimensional

scholarly journals Heritability estimation in high dimensional sparse linear mixed models

2015 ◽  
Vol 9 (2) ◽  
pp. 2099-2129 ◽  
Author(s):  
Anna Bonnet ◽  
Elisabeth Gassiat ◽  
Céline Lévy-Leduc
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
High Dimensional ◽  
Heritability Estimation

scholarly journals LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies

2018 ◽  
Author(s):  
Hannah Verena Meyer ◽  
Francesco Paolo Casale ◽  
Oliver Stegle ◽  
Ewan Birney
Keyword(s):  
Mixed Models ◽  
Growth Traits ◽  
Association Studies ◽  
Linear Mixed Models ◽  
Genetic Association Studies ◽  
Pleiotropic Effects ◽  
High Dimensional ◽  
Model Parameters ◽  
Moderate Number ◽  
Genome Wide

AbstractGenome-wide association studies have helped to shed light on the genetic architecture of complex traits and diseases. Deep phenotyping of population cohorts is increasingly applied, where multi-to high-dimensional phenotypes are recorded in the individuals. Whilst these rich datasets provide important opportunities to analyse complex trait structures and pleiotropic effects at a genome-wide scale, existing statistical methods for joint genetic analyses are hampered by computational limitations posed by high-dimensional phenotypes. Consequently, such multivariate analyses are currently limited to a moderate number of traits. Here, we introduce a method that combines linear mixed models with bootstrapping (LiMMBo) to enable computationally efficient joint genetic analysis of high-dimensional phenotypes. Our method builds on linear mixed models, thereby providing robust control for population structure and other confounding factors, and the model scales to larger datasets with up to hundreds of phenotypes. We first validate LiMMBo using simulations, demonstrating consistent covariance estimates at greatly reduced computational cost compared to existing methods. We also find LiMMBo yields consistent power advantages compared to univariate modelling strategies, where the advantages of multivariate mapping increases substantially with the phenotype dimensionality. Finally, we applied LiMMBo to 41 yeast growth traits to map their genetic determinants, finding previously known and novel pleiotropic relationships in this high-dimensional phenotype space. LiMMBo is accessible as open source software (https://github.com/HannahVMeyer/limmbo).Author summaryIn multi-trait genetic association studies one is interested in detecting genetic variants that are associated with one or multiple traits. Genetic variants that influence two or more traits are referred to as pleiotropic. Multivariate linear mixed models have been successfully applied to detect pleiotropic effects, by jointly modelling association signals across traits. However, these models are currently limited to a moderate number of phenotypes as the number of model parameters grows steeply with the number of phenotypes, raising a computational burden. We developed LiMMBo, a new approach for the joint analysis of high-dimensional phenotypes. Our method reduces the number of effective model parameters by introducing an intermediate subsampling step. We validate this strategy using simulations, where we apply LiMMBo for the genetic analysis of hundreds of phenotypes, detecting pleiotropic effects for a wide range of simulated genetic architectures. Finally, to illustrate LiMMBo in practice, we apply the model to a study of growth traits in yeast, where we identify pleiotropic effects for traits with formerly known genetic effects as well as revealing previously unconnected traits.

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