cpg.assoc {CpGassoc} | R Documentation |

Association Analysis Between Methylation Beta Values and Phenotype of Interest. For more detail see Tutorial.

cpg.assoc(beta.val, indep, covariates = NULL, data = NULL, logit.transform = FALSE, chip.id = NULL, subset = NULL, random = FALSE, fdr.cutoff = 0.05, large.data = TRUE, fdr.method = "BH", logitperm = FALSE)

`beta.val` |
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 variable to be tested for association. |

`covariates` |
A data frame consisting of additional covariates to be included in the model. covariates can also be specified as a matrix
if it takes the form of a model matrix with no intercept column, or can be specified as 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 as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from the environment from which cpg.assoc is called. |

`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 desired FDR threshold. The default setting is .05. The set of CpG sites with FDR < fdr.cutoff will be labeled as significant. |

`large.data` |
Logical. Enables analyses of large datasets. When |

`fdr.method` |
Character. Method used to calculate False Discovery Rate. Choices include any of the methods available in |

`logitperm` |
Logical. For internal use only. |

`cpg.assoc`

is designed to test for association between an independent variable and methylation at a number of CpG sites, with the option to include additional covariates and factors.

`cpg.assoc`

assesses significance with the Holm (step-down Bonferroni) and FDR methods.

If `class(indep)='factor'`

, `cpg.assoc`

will perform an ANOVA test of the variable conditional on the covariates specified. Covariates, if entered, should be in the form of a data
frame, matrix, or vector. For example, `covariates=data.frame(weight,age,factor(city))`

. The data frame can also be specified prior to calling `cpg.assoc`

. The covariates
should either be vectors or columns of a matrix or data.frame.

`cpg.assoc`

is also designed to deal with large data sets. Setting `large.data=TRUE`

will make `cpg.assoc`

split up the data to enable efficient analysis of large datasets.

`cpg.assoc`

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.

`results` |
A data frame consisting of the t or F statistics and P-values for each CpG site, as well as indicators of Holm and FDR significance. CpG sites will be in the same order as the original
input, but the |

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

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

`info` |
A data frame consisting of the minimum P-value observed, the FDR method that was used, the phenotype of interest, the number of covariates in the model, the name of the matrix or data frame the methylation beta values were taken from, the FDR cutoff value and whether a mixed effects analysis was performed. |

`indep` |
The independent variable that was tested for association. |

`covariates` |
Data.frame or matrix of covariates, if specified (otherwise |

`chip` |
chip.id vector, if specified (otherwise |

`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.; Conneely, K.; Kilaru,V.

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

`cpg.work`

`cpg.perm`

`plot.cpg`

`scatterplot`

`cpg.combine`

`manhattan`

`plot.cpg.perm`

`sort.cpg.perm`

`sort.cpg`

`cpg.qc`

`cpg.GC`

# Sample output from CpGassoc ###NOTE: If you are dealing with large data, do not specify large.data=FALSE. ###The default option is true. ##This will involve partitioning up the data and performing more gc() to clear up space data(samplecpg,samplepheno,package="CpGassoc") results<-cpg.assoc(samplecpg,samplepheno$weight,large.data=FALSE) results #Analysis with covariates. There are multiple ways to do this. One can define the #dataframe prior or do it in the function call. test<-cpg.assoc(samplecpg,samplepheno$weight,data.frame(samplepheno$Distance, samplepheno$Dose),large.data=FALSE) # or covar<-data.frame(samplepheno$Distance,samplepheno$Dose) test2<-cpg.assoc(samplecpg,samplepheno$weight,covar,large.data=FALSE) #Doing a mixed effects model. This does take more time, so we will do a subset of #the samplecpg randtest<-cpg.assoc(samplecpg[1:10,],samplepheno$weight,chip.id=samplepheno$chip, random=TRUE,large.data=FALSE)

[Package *CpGassoc* version 2.60 Index]