epi_beta | R Documentation |

`epi_beta`

method models the DNA methylation data
using a beta distribution.
First, the beta distribution parameters of the
reference population are precomputed and passed to the method.
Then, we compute the probability of observing the methylation values
of the case from the reference beta distribution.
CpGs with p-values smaller than a threshold
`pvalue_threshold`

and with
a methylation difference with the
mean reference methylation
higher than `diff_threshold`

are defined as outlier CpGs. Finally,
epimutations are defined as a
group of contiguous outlier CpGs.

```
epi_beta(
beta_params,
beta_mean,
betas_case,
case,
controls,
betas,
annot,
pvalue_threshold,
diff_threshold,
min_cpgs = 3,
maxGap
)
```

`beta_params` |
matrix with the parameters of the reference beta distributions for each CpG in the dataset. |

`beta_mean` |
beta values mean. |

`betas_case` |
matrix with the methylation values for a case. |

`case` |
case sample name. |

`controls` |
control samples names. |

`betas` |
a matrix containing the beta values for all samples. |

`annot` |
annotation of the CpGs. |

`pvalue_threshold` |
minimum p-value to consider a CpG an outlier. |

`diff_threshold` |
minimum methylation difference between the CpG and the mean methylation to consider a position an outlier. |

`min_cpgs` |
minimum number of CpGs to consider an epimutation. |

`maxGap` |
maximum distance between two contiguous CpGs to combine them into an epimutation. |

The function returns a data frame with the candidate regions to be epimutations.

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