r_mediation_cell_type: r_mediation_cell_type : function to simulate DNA methylation...

In jumentib/useFonc: Useful Functions

Description

r_mediation_cell_type : function to simulate DNA methylation data for mediation analyzis (and cell type)

Usage

r_mediation_cell_type(n, p, K, K.ct, freq = NULL, prop.causal.x = 0.01,
  prop.causal.y = 0.01, prop.causal.ylx = 0.5, prop.variance.y = 0.6,
  prop.variance.x = 0.2, rho = 0.2, sigma = 0.2, sd.A = 1,
  mean.A = 3, sd.B = 1, mean.B = 5, sd.U = 1, sd.V = 1,
  sd.ct = 1, alpha = NULL)

Arguments

n	: number of individuals
p	: number of cpg variables
K	: number of latent factors
K.ct	: number of cell type
freq	: (vector) mean methylation values (if NULL, set randomly)
prop.causal.x	: proportion of causal cpg M -> x
prop.causal.y	: proportion of causal cpg M -> y
prop.causal.ylx	: proportion of causal y in causal x
prop.variance.y	: proportion of phenotypic variance explained by latent structure (intensity of confounding)
prop.variance.x	: proportion of exposure variance explained by latent structure (intensity of confounding)
rho	: correlation outcome/exposure (direct effect)
sigma	: standard deviation of residual errors
sd.A	: standard deviation for effect sizes (A: M->X)
mean.A	: (vector) mean of effect sizes
sd.B	: standard deviation for effect sizes (B: M->Y)
mean.B	: (vector) mean of effect sizes
sd.U	: (vector) standard deviations for factors
sd.V	: standard deviations for loadings
sd.ct	: standard deviations for loadings (cell type)
alpha	: parameter for the dirichlet distribution (for cell type), default : runif(K.ct)
prop.causal	: proportion of causal variables (probes/loci)

Details

This function is used to simulate datasets for analysis of mediations. The simulation model is based on linear relationships. First, it construct a covariance matrix for X, Y and U using the parameter rho (direct effect or correlation between X and Y) and propvar (intensity of the confounders or correlation between Y and U). Then this matrix is used to simulate via normal laws X, Y and U. Thereafter, the effect sizes of X (A), Y (B) and U (V) are calculated using mean parameters of effect sizes (meanA and meanB) and standard deviations (sdA, sdB and sdV). Note that the effect sizes of X and Y are calculated only for causal mediators with X and/or Y. For non-causal mediators, the effect sizes is 0. On the other hand, a residual error matrix is calculated via the sigma (Z) parameter. Cell type is simulate with dirichlet distribution To finish the methylation matrix is calculated thanks to the formula : M = V*U + A*X + B*Y + Z

Value

M : matrix of methylation beta values

Y : phenotype/health outcome

B : effect sizes phenotype/health outcome

X : exposure

A : effect sizes exposure

mediators : set of true mediators

causal.x : set of CpGs associated with the exposure

causal.y : set of CpGs associated with the outcome

U : simulated confounders

V : loadings of coufounders

freq : mean methylation values

controls : true control gene (NOT USE for simulation study)

cell_type : proportion of cell type

tcell_type : loadings of cell type

Examples

# Simulate data :
simu <- r_mediation_cell_type(100, 500, 2, 5)

jumentib/useFonc documentation built on Nov. 18, 2019, 3:17 p.m.