lfmm_lasso: LFMM least-squares estimates with lasso penalty
In bcm-uga/lfmm: Latent Factor Mixed Models

Description Usage Arguments Details Value Author(s) Examples

This function computes regularized least squares estimates for latent factor mixed models using a lasso penalty.

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lfmm_lasso(Y, X, K, nozero.prop = 0.01, lambda.num = 100,
  lambda.min.ratio = 0.01, lambda = NULL, it.max = 100,
  relative.err.epsilon = 1e-06)

`Y`	a response variable matrix with n rows and p columns. Each column is a response variable (e.g., SNP genotype, gene expression level, beta-normalized methylation profile, etc). Response variables must be encoded as numeric.
`X`	an explanatory variable matrix with n rows and d columns. Each column corresponds to a distinct explanatory variable (eg. phenotype). Explanatory variables must be encoded as numeric.
`K`	an integer for the number of latent factors in the regression model.
`nozero.prop`	a numeric value for the expected proportion of non-zero effect sizes.
`lambda.num`	a numeric value for the number of 'lambda' values (obscure).
`lambda.min.ratio`	(obscure parameter) a numeric value for the smallest `lambda` value, A fraction of `lambda.max`, the data derived entry value (i.e. the smallest value for which all coefficients are zero).
`lambda`	(obscure parameter) Smallest value of `lambda`. A fraction of 'lambda.max', the (data derived) entry value (i.e. the smallest value for which all coefficients are zero).
`it.max`	an integer value for the number of iterations of the algorithm.
`relative.err.epsilon`	a numeric value for a relative convergence error. Determine whether the algorithm converges or not.

The algorithm minimizes the following penalized least-squares criterion

The response variable matrix Y and the explanatory variable are centered.

an object of class lfmm with the following attributes:

U the latent variable score matrix with dimensions n x K,
V the latent variable axes matrix with dimensions p x K,
B the effect size matrix with dimensions p x d.

cayek, francoio

library(lfmm)

## a GWAS example with Y = SNPs and X = phenotype
data(example.data)
Y <- example.data$genotype
X <- example.data$phenotype

## Fit an LFMM with 6 factors
mod.lfmm <- lfmm_lasso(Y = Y, 
                       X = X, 
                       K = 6,
                       nozero.prop = 0.01)
                       
## Perform association testing using the fitted model:
pv <- lfmm_test(Y = Y, 
                X = X, 
                lfmm = mod.lfmm, 
                calibrate = "gif")
                
## Manhattan plot with causal loci shown
pvalues <- pv$calibrated.pvalue
plot(-log10(pvalues), 
      pch = 19, cex = .2, 
      col = "grey", xlab = "SNP")
      
points(example.data$causal.set, 
       -log10(pvalues)[example.data$causal.set], 
       type = "h", col = "blue")
       
## An EWAS example with Y = methylation data 
## and X = exposure
Y <- scale(skin.exposure$beta.value)
X <- scale(as.numeric(skin.exposure$exposure))

## Fit an LFMM with 2 latent factors
mod.lfmm <- lfmm_lasso(Y = Y,
                       X = X, 
                       K = 2,
                       nozero.prop = 0.01)
                       
## Perform association testing using the fitted model:
pv <- lfmm_test(Y = Y, 
                X = X,
                lfmm = mod.lfmm, 
                calibrate = "gif")
                
## Manhattan plot with true associations shown
pvalues <- pv$calibrated.pvalue
plot(-log10(pvalues), 
     pch = 19, 
     cex = .3,
     xlab = "Probe",
     col = "grey")
     
causal.set <- seq(11, 1496, by = 80)
points(causal.set, 
       -log10(pvalues)[causal.set], 
       col = "blue")