lfmm_test: Statistical tests with latent factor mixed models (linear...

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/lfmm.R

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 (linear model).

Usage

1
lfmm_test(Y, X, lfmm, calibrate = "gif")

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

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

calibrate

a character string, "gif" or "median+MAD". 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". If the "median+MAD" option is set, the pvalues are calibrated by computing the median and MAD of the zscores. If NULL, the pvalues are not calibrated.

Details

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

Value

a list with the following attributes:

Author(s)

cayek, francoio

See Also

glm_test

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
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 K = 6 factors
mod.lfmm <- lfmm_ridge(Y = Y, 
                       X = X, 
                       K = 6)

## 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
data("skin.exposure")
Y <- scale(skin.exposure$beta.value)
X <- scale(as.numeric(skin.exposure$exposure))

## Fit an LFMM with 2 latent factors
mod.lfmm <- lfmm_ridge(Y = Y,
                       X = X, 
                       K = 2)
                       
## 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")

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