eLNNpaired (version 0.2.3)

gen_simple:

Description

Generate Simulated Data Sets from a Simple Model.

Usage

gen_simple(
  G, 
  n = 30, 
  psi = c(0.441, 1, -0.442, 1, 2), 
  t_pi = c(0.086, 0.071)
)

Arguments

G

An integer, the number of genes.

n

An integer, the number of pairs for each gene.

psi

A vector of 5 elements containing model parameters \(\mu_1\), \(\sigma_1\), \(\mu_2\), \(\sigma_2\), and \(\sigma_3\).

t_pi

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

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.

Details

We assume there are three clusters of gene probes: (1) over-expressed; (2) under-expressed; and (3) non-differentially expressed. For probes in cluster 1, we assume the within-pair log2 difference of gene expression is from \(N(\mu_1, \sigma_1^2)\). For probes in cluster 2, we assume the within-pair log2 difference of gene expression is from \(N(\mu_2, \sigma_2^2)\). For probes in cluster 3, we assume the within-pair log2 difference of gene expression is from \(N(0, \sigma_3^2)\). \(\mu_1>0\) and \(\mu_2<0\).

References

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

Examples

es=gen_simple(
  G = 500,
  n = 30,
  psi = c(0.441, 1, -0.442, 1, 2),
  t_pi = c(0.086, 0.071)
)

print(es)