Isoscape Computation and Inference of Spatial Origins With Mixed Models Using the R package IsoriX

2019 ◽  
pp. 207-236 ◽  
Author(s):  
Alexandre Courtiol ◽  
François Rousset ◽  
Marie-Sophie Rohwäder ◽  
David X. Soto ◽  
Linn S. Lehnert ◽  
...  
Keyword(s):  
Mixed Models ◽  
R Package

scholarly journals Software update: Moving the R package sommer to multivariate mixed models for genome-assisted prediction

2018 ◽  
Author(s):  
Giovanny Covarrubias-Pazaran
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
R Package ◽  
Animal Breeding ◽  
Breeding Programs ◽  
Incidence Matrices ◽  
Software Update ◽  
Trial Analysis ◽  
User Friendly

AbstractIn the last decade the use of mixed models has become a pivotal part in the implementation of genome-assisted prediction in plant and animal breeding programs. Exploiting the use genetic correlation among traits through multivariate predictions has been proposed in recent years as a way to boost prediction accuracy and understand pleiotropy and other genetic and ecological phenomena better. Multiple mixed model solvers able to use relationship matrices or deal with marker-based incidence matrices have been released in the last years but multivariate versions are scarse. Such solvers have become quite popular in plant and animal breeding thanks to user-friendly platforms such as R. Among such software one of the most recent and popular is the sommer package. In this short communication we discuss the update of the package that is able to run multivariate mixed models with multiple random effects and different covariance structures at the level of random effects and trait-to-trait covariance along with other functionalities for genetic analysis and field trial analysis to enhance the genome-assisted prediction capabilities of researchers.

Likelihood‐Based Inference for Generalized Linear Mixed Models: Inference with the R Package glmm

Stat ◽  
2020 ◽  
Author(s):  
Christina Knudson ◽  
Sydney Benson ◽  
Charles Geyer ◽  
Galin Jones
Keyword(s):  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
R Package

scholarly journals NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Xinyan Zhang ◽  
Nengjun Yi
Keyword(s):  
Data Analysis ◽  
Mixed Models ◽  
Negative Binomial ◽  
Longitudinal Design ◽  
R Package ◽  
Metagenomic Data ◽  
The Public ◽  
Metagenomics Data ◽  
R Packages ◽  
Over Dispersion

Abstract Background Microbiome/metagenomic data have specific characteristics, including varying total sequence reads, over-dispersion, and zero-inflation, which require tailored analytic tools. Many microbiome/metagenomic studies follow a longitudinal design to collect samples, which further complicates the analysis methods needed. A flexible and efficient R package is needed for analyzing processed multilevel or longitudinal microbiome/metagenomic data. Results NBZIMM is a freely available R package that provides functions for setting up and fitting negative binomial mixed models, zero-inflated negative binomial mixed models, and zero-inflated Gaussian mixed models. It also provides functions to summarize the results from fitted models, both numerically and graphically. The main functions are built on top of the commonly used R packages nlme and MASS, allowing us to incorporate the well-developed analytic procedures into the framework for analyzing over-dispersed and zero-inflated count or proportion data with multilevel structures (e.g., longitudinal studies). The statistical methods and their implementations in NBZIMM particularly address the data characteristics and the complex designs in microbiome/metagenomic studies. The package is freely available from the public GitHub repository https://github.com/nyiuab/NBZIMM. Conclusion The NBZIMM package provides useful tools for complex microbiome/metagenomics data analysis.

scholarly journals Modelling isoscapes using mixed models

2017 ◽  
Author(s):  
Alexandre Courtiol ◽  
Francois Rousset
Keyword(s):  
Spatial Variation ◽  
Mixed Models ◽  
Model Comparison ◽  
Isotope Composition ◽  
Information Criterion ◽  
R Package ◽  
Correlated Data ◽  
Residual Variance ◽  
Earth Systems ◽  
Statistical Framework

Isoscapes are maps depicting the continuous spatial (and sometimes temporal) variation in isotope composition. They have various applications ranging from the study of isotope circulation in the main earth systems to the determination of the provenance of migratory animals. Isoscapes can be produced from the fit of statistical models to observations originating from a set of discrete locations. Mixed models are powerful tools for drawing inferences from correlated data. While they are widely used to study non-spatial variation, they are often overlooked in spatial analyses. In particular, they have not been used to study the spatial variation of isotope composition. Here, we introduce this statistical framework and illustrate the methodology by building isoscapes of the isotope composition of hydrogen for precipitation water in Europe. For this example, the approach based on mixed models presents a higher predictive power than a widespread alternative approach. We discuss other advantages offered by mixed models including: the ability to model the residual variance in isotope composition, the quantification of prediction uncertainty, and the simplicity of model comparison and selection using an adequate information criterion: the conditional AIC (cAIC). We provide all source code required for the replication of the results of this paper as a small R package to foster a transparent comparison between alternative frameworks used to model isoscapes.

Technical note: An R package for fitting generalized linear mixed models in animal breeding1

2010 ◽  
Vol 88 (2) ◽  
pp. 497-504 ◽  
Author(s):  
A. I. Vazquez ◽  
D. M. Bates ◽  
G. J. M. Rosa ◽  
D. Gianola ◽  
K. A. Weigel
Keyword(s):  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
R Package ◽  
Technical Note

scholarly journals muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Helena L. Crowell ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Daniela Calini ◽  
Ludovic Collin ◽  
...  
Keyword(s):  
Single Cell ◽  
Mixed Models ◽  
Large Scale ◽  
R Package ◽  
State Transitions ◽  
Cell Level ◽  
Method Performance ◽  
Statistical Framework ◽  
Mouse Cortex ◽  
Transcriptomics Data

AbstractSingle-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

scholarly journals lme4GS: An R-Package for Genomic Selection

2021 ◽  
Vol 12 ◽  
Author(s):  
Diana Caamal-Pat ◽  
Paulino Pérez-Rodríguez ◽  
José Crossa ◽  
Ciro Velasco-Cruz ◽  
Sergio Pérez-Elizalde ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Statistical Models ◽  
Mixed Models ◽  
Bandwidth Selection ◽  
Real Data ◽  
R Package ◽  
Covariance Matrices ◽  
Covariance Structures ◽  
Or Groups

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data

2020 ◽  
Vol 36 (8) ◽  
pp. 2345-2351 ◽  
Author(s):  
Xinyan Zhang ◽  
Nengjun Yi
Keyword(s):  
Count Data ◽  
Mixed Models ◽  
Negative Binomial ◽  
Linear Mixed Models ◽  
Human Microbiome ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Metagenomics Data

Abstract Motivation Longitudinal metagenomics data, including both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic associations between the human microbiome and various diseases. However, analytic tools have not been fully developed to simultaneously address the main challenges of longitudinal metagenomics data, i.e. high-dimensionality, dependence among samples and zero-inflation of observed counts. Results We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze high-dimensional longitudinal metagenomic count data. The FZINBMM approach is based on zero-inflated negative binomial mixed models (ZINBMMs) for modeling longitudinal metagenomic count data and a fast EM-IWLS algorithm for fitting ZINBMMs. FZINBMM takes advantage of a commonly used procedure for fitting linear mixed models, which allows us to include various types of fixed and random effects and within-subject correlation structures and quickly analyze many taxa. We found that FZINBMM remarkably outperformed in computational efficiency and was statistically comparable with two R packages, GLMMadaptive and glmmTMB, that use numerical integration to fit ZINBMMs. Extensive simulations and real data applications showed that FZINBMM outperformed other previous methods, including linear mixed models, negative binomial mixed models and zero-inflated Gaussian mixed models. Availability and implementation FZINBMM has been implemented in the R package NBZIMM, available in the public GitHub repository http://github.com//nyiuab//NBZIMM. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

2017 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Miquel Vázquez-Santiago ◽  
Helena Brunel ◽  
Angel Martinez-Perez ◽  
Hugues Aschard ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Random Effects ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
R Package ◽  
Covariance Matrices ◽  
Linkage Studies ◽  
Genetic Studies ◽  
Related Individuals ◽  
Mapping Models

AbstractBackgroundQuantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.ResultsTo address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.ConclusionsOur software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl.

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