cpg.work {CpGassoc} | R Documentation |

Association Analysis Between Methylation Beta Values and Phenotype of Interest. This function contains the code that does the brunt of the work for `cpg.assoc`

and `cpg.perm`

.

cpg.work(beta.values, indep, covariates = NULL, data = NULL, logit.transform = FALSE, chip.id = NULL, subset = NULL, random = FALSE, fdr.cutoff = 0.05, callarge = FALSE, fdr.method = "BH", logitperm = FALSE,big.split=FALSE)

`beta.values` |
A vector, matrix, or data frame containing the beta values of interest (1 row per CpG site, 1 column per individual). |

`indep` |
A vector containing the main variable of interest. |

`covariates` |
A data frame consisting of the covariates of interest. covariates can also be a matrix if it is a model matrix minus the intercept column.
It can also be a vector if there is only one covariate of interest. Can also be a formula (e.g. |

`data` |
an optional data frame, list or environment (or object coercible by |

`logit.transform` |
logical. If |

`chip.id` |
An optional vector containing chip, batch identities, or other categorical factor of interest to the researcher. If specified, chip id will be included as a factor in the model. |

`subset` |
an optional logical vector specifying a subset of observations to be used in the fitting process. |

`random` |
logical. If |

`fdr.cutoff` |
The threshold at which to compare the FDR values. The default setting is .05. Any FDR values less than .05 will be considered significant. |

`callarge` |
logical. Used by |

`fdr.method` |
Character.Method used to calculate False Discovery Rate. Can be any of the methods listed in |

`logitperm` |
Passes from |

`big.split` |
Passes from |

`cpg.work`

does the analysis between the methylation and the phenotype of interest. It is called by `cpg.assoc`

to do the brunt of the work. It can be called itself with the same input as `cpg.assoc`

, it just cannot handle large data sets.

`cpg.work`

will return an object of class `"cpg"`

.
The functions summary and plot can be called to get a summary of results and to create QQ plots. The output is in the same order as the original input. To sort it by p-value, use the `sort`

function.

`results` |
A data frame consisting of the statistics and P-values for each CpG site. Also has the adjusted p-value based on the fdr.method and whether the site was Holm significant. |

`Holm.sig` |
A list of sites that met criteria for Holm significance. |

`FDR.sig` |
A data.frame of the sites that were FDR significant by the fdr method. |

`info` |
A data frame consisting of the minimum P-value observed, the fdr method used, what the phenotype of interest was, and the number of covariates in the model. |

`indep` |
The main phenotype of interest. |

`covariates` |
If covariates was non |

`chip` |
If chip.id was non |

`coefficients` |
A data frame consisting of the degrees of freedom, and if object is continous the intercept effect adjusted for possible covariates in the model, the estimated effect size, and the standard error.
The degrees of freedom is used in |

Barfield, R.; Kilaru,V.; Conneely, K.

Maintainer: R. Barfield: <rbarfield01@fas.harvard.edu>

`cpg.perm`

`cpg.assoc`

`plot.cpg`

`scatterplot`

`cpg.combine`

`manhattan`

`plot.cpg.perm`

`sort.cpg.perm`

`sort.cpg`

`cpg.qc`

`cpg.GC`

##See the examples listed in cpg.assoc for ways in which to use cpg.work. ##Just change the cpg.assoc to cpg.work.

[Package *CpGassoc* version 2.60 Index]