glm_test {lfmm}

GLM tests with latent factor mixed models

Description

This function returns significance values for the association between each column of the response matrix, Y, and the explanatory variables, X, including correction for unobserved confounders (latent factors). The test is based on an LFMM fitted with a ridge or lasso penalty and a generalized linear model.

Usage

glm_test(Y, X, lfmm.obj, calibrate = "gif", family = binomial(link = "logit"))

Arguments

Y

a response variable matrix with n rows and p columns. Each column is a response variable (numeric).

X

an explanatory variable matrix with n rows and d columns. Each column corresponds to an explanatory variable (numeric).

lfmm.obj

an object of class lfmm returned by the lfmm_lasso or lfmm_ridge function

calibrate

a character string, "gif". If the "gif" option is set (default), significance values are calibrated by using the genomic control method. Genomic control uses a robust estimate of the variance of z-scores called "genomic inflation factor".

family

a description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function.

Details

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

Value

a list with the following attributes:

• B the effect size matrix with dimensions p x d.

• score a p x d matrix which contains z-scores for each explanatory variable (columns of X),

• pvalue a p x d matrix which contains p-values for each explanatory variable,

• calibrated.pvalue a p x d matrix which contains calibrated p-values for each explanatory variable,

• gif a numeric value for the genomic inflation factor.

Author(s)

Kevin Caye, Basile Jumentier, Olivier Francois

See Also

lfmm_test

Examples

library(lfmm)


## An EWAS example with Y = methylation data 
## and X = "exposure" 
## Simulate the data

dat <- lfmm_sampler(n = 100, 
                    p = 500,
                    K = 3,
                    outlier.prop = 0.01,
                    cs = 0.1,
                    sigma = 0.2,
                    B.sd = 5,
                    B.mean = 0,
                    U.sd = 1.0,
                    V.sd = 1.0)
                    
Y <- pnorm(dat$Y)
X <- dat$X

## Fit an LFMM with 2 latent factors
mod.lfmm <- lfmm_ridge(Y = Y,
                       X = X, 
                       K = 3)
                       
## Perform association testing using the fitted model:
pv <- glm_test(Y = pnorm(Y), 
                X = X,
                lfmm.obj = mod.lfmm, 
                family = binomial(link = "probit"),
                calibrate = "gif")
                
## Manhattan plot with true associations shown
causal <- dat$outlier
pvalues <- pv$calibrated.pvalue
plot(-log10(pvalues), 
     pch = 19, 
     cex = .3,
     xlab = "Probe",
     col = "grey")
     
points(causal, 
      -log10(pvalues)[causal], 
       col = "blue")