eLNNpaired (version 0.2.3)

gen_eLNNpaired: Generate a Simulated Data Set from eLNNpaired Model

Description

Generate a simulated data set from eLNNpaired model and store it into an ExpressionSet object.

Usage

gen_eLNNpaired(G, n, psi, t_pi, c1 = qnorm(0.95), c2 = qnorm(0.05))

Arguments

G

An integer, the number of genes.

n

An integer, the number of pairs for each gene.

psi

A vector of length 10. It contains the parameters after reparameterization as illustrated in paper: \(\delta_1\), \(\xi_1\), \(\lambda_1\), \(\nu_1\), \(\delta_2\), \(\xi_2\), \(\lambda_2\), \(\nu_2\), \(\lambda_3\), and \(\nu_3\).

t_pi

the cluster proportion for cluster 1 (over-expressed probes) and cluster 2 (under-expressed probes).

c1

A parameter in constraints. It should be in the form of c1 = qnorm(X), where X is a decimal smaller than 1 but close to 1. Larger X gives more stringent constraint. Default value is c1 = qnorm(0.95).

c2

A parameter in constraints. It should be in the form of c2 = qnorm(Y), where Y is a decimal larger than 0 but close to 0. Smaller Y gives more stringent constraint. Default value is c2 = qnorm(0.05).

Value

An ExpressionSet object, the feature data frame of which include memGenes.true (3-cluster membership for gene probes) and memGenes2.true (2-cluster membership for gene probes). In 3-cluster membership, 1 indicates over-expressed, 2 indicates under-expressed, and 3 indicates non-differentially expressed. In 2-cluster membership, 1 indicates differentially expressed, 0 indicates non-differentially expressed.

References

Li Y, Morrow J, Raby B, Tantisira K, Weiss ST, Huang W, Qiu W. (2017), <doi:10.1371/journal.pone.0174602>

Examples

set.seed(100)
G = 500
n = 10

delta_1 = -0.8184384  
xi_1 = -1.1858546 
lambda_1 = -10.6309216  
nu_1 = -3.5536255  

delta_2 = -0.8153614  
xi_2 = -1.4120148 
lambda_2 = -13.1999427  
nu_2 = -3.3873531   

lambda_3 = 0.7597441  
nu_3 = -2.0361091 

psi = c(delta_1, xi_1, lambda_1, nu_1,
        delta_2, xi_2, lambda_2, nu_2,
        lambda_3, nu_3)
t_pi = c(0.08592752, 0.07110449)

c1 = qnorm(0.95)
c2 = qnorm(0.05)

E_Set = gen_eLNNpaired(G, n, psi, t_pi, c1, c2)


print(E_Set)

# phenotype data
pDat = pData(E_Set)
print(pDat[1:2,])

# feature data
fDat = fData(E_Set)
print(fDat[1:2,])

print(table(fDat$memGenes.true, useNA="ifany"))
print(table(fDat$memGenes2.true, useNA="ifany"))

print(table(fDat$memGenes.true, fDat$memGenes2.true, useNA="ifany"))