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 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 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 TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP–SNP interactions

2017 ◽  
Vol 33 (22) ◽  
pp. 3595-3602 ◽  
Author(s):  
Yao-Hwei Fang ◽  
Jie-Huei Wang ◽  
Chao A Hsiung
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Whole Genome ◽  
Selection Approach

Robust Variable Selection in Linear Mixed Models

2014 ◽  
Vol 43 (21) ◽  
pp. 4566-4581 ◽  
Author(s):  
Yali Fan ◽  
Guoyou Qin ◽  
Zhong Yi Zhu
Keyword(s):  
Variable Selection ◽  
Mixed Models ◽  
Linear Mixed Models

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

Variable Selection in Linear Mixed Models Using an Extended Class of Penalties

2012 ◽  
Vol 54 (4) ◽  
pp. 427-449 ◽  
Author(s):  
Julian D. Taylor ◽  
Arūnas P. Verbyla ◽  
Colin Cavanagh ◽  
Marcus Newberry
Keyword(s):  
Variable Selection ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Extended Class
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