latent factor mixed models: lfmm

Genome and epigenome-wide association studies are plagued with the problems of confounding and causality. The R package lfmm implements new algorithms for parameter estimation in latent factor mixed models (LFMM). The algorithms are designed for the correction of unobserved confounders. The new methods are computationally efficient, and provide statistically optimal corrections resulting in improved power and control for false discoveries. The package lfmm provides two main functions for estimating latent confounders (or factors): lfmm_ridge and lfmm_lasso. Those functions are based on optimal solutions of regularized least-squares problems. A short tutorial provides brief examples on how the R packages lfmm can be used for fitting latent factor mixed models and evaluating association between a response matrix (SNP genotype or methylation levels) and a variable of interest (phenotype or exposure levels) in genome-wide (GW), genome-environment (GE), epigenome-wide (EW) association studies. Corresponding software is available at the following url


Installing the latest version from github requires devtools:

# install.packages("devtools")

cayek/MatrixFactorizationR documentation built on Feb. 19, 2018, 2:04 p.m.