Description Usage Arguments Details Value Author(s) References Examples

View source: R/genSimData.BayesNormal.R

Generating simulated data set from conditional normal distributions.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
genSimData.BayesNormal(
nCpGs,
nCases,
nControls,
mu.n = -2,
mu.c = 2,
d0 = 20,
s02 = 0.64,
s02.c = 1.5,
testPara = "var",
outlierFlag = FALSE,
eps = 0.001,
applier = lapply)
``` |

`nCpGs` |
integer. Number of genes. |

`nCases` |
integer. Number of cases. |

`nControls` |
integer. Number of controls. |

`mu.n` |
numeric. mean of the conditional normal distribution for controls. See details. |

`mu.c` |
numeric. mean of the conditional normal distribution for cases. See details. |

`d0` |
integer. degree of freedom for scale-inverse chi squared distribution. See details. |

`s02` |
numeric. scaling parameter for scale-inverse chi squared distribution for controls. See details. |

`s02.c` |
numeric. scaling parameter for scale-inverse chi squared distribution for cases. See details. |

`testPara` |
character string. indicating if the test is for testing equal mean, equal variance, or both. |

`outlierFlag` |
logical. indicating if outliers would be generated. If |

`eps` |
numeric. if |

`applier` |
function name to do |

Based on Phipson and Oshlack's (2014) simulation algorithm.
For each CpG site, variance of the DNA methylation was first sampled from an
scaled inverse chi-squared distribution with degree of freedom
*d_0* and scaling parameter *s_0^2*:
*σ^2_i ~ scale-inv χ^2(d_0, s_0^2)*.
M value for each CpG was then sampled from a normal distribution
with mean *μ_n* and variance equal to the simulated variance
*σ^2_i*.
For cases, the variance was first generated from
*σ^2_{i,c} ~ scale-inv χ^2(d_0, s_{0,c}^2)*.
M value for each CpG was then sampled from a normal distribution
with mean *μ_c* and variance equal to the simulated variance
*σ^2_{i,c}*.

An ExpressionSet object. The phenotype data of the ExpressionSet object
contains 2 columns: `arrayID`

(array id) and memSubj (subject
membership, i.e., case (`memSubj=1`

) or control (`memSubj=0`

)).
The feature data of the ExpressionSet object contains 4 elements:
`probe`

(probe id), `gene`

(psuedo gene symbol), `chr`

(psuedo chromosome number), and `memGenes`

(indicating if a gene is differentially expressed (when `testPara="mean"`

)
or indicating if a gene is differentially variable (when `testPara="var"`

) ).

Weiliang Qiu <[email protected]>, Brandon Guo <[email protected]>, Christopher Anderson <[email protected]>, Barbara Klanderman <[email protected]>, Vincent Carey <[email protected]>, Benjamin Raby <[email protected]>

Phipson B, Oshlack A.
DiffVar: A new method for detecting differential variability with application to methylation in cancer and aging.
*Genome Biol* 2014; 15:465

1 2 3 4 5 6 7 8 9 | ```
# generate simulated data set from conditional normal distribution
set.seed(1234567)
es.sim = genSimData.BayesNormal(nCpGs = 100,
nCases = 20, nControls = 20,
mu.n = -2, mu.c = 2,
d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
outlierFlag = FALSE,
eps = 1.0e-3, applier = lapply)
print(es.sim)
``` |

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