MethylCal: MethylCal: Bayesian Calibration of Methylation Levels

Description Usage Arguments Value References Examples

Correction of case/controls samples using MethylCal calibration

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MethylCalCorrection(data, Target = NULL, prior = "LG", n_Control = 0,
  n_Case = 0, level_Control = 0.9986501, opt_BoxPlot = 0,
  dir = NULL, printing = TRUE, plotting = TRUE, cex_par = 1.25)

`data`	Formatted input data frame obtained from the function `Formatting`.
`Target`	Name of the target DMR/CpG island/gene to be visualised.
`prior`	Prior distribution set-up for the random effects and the Latent Gaussian Field (Rue et al., 2009). Three different priors are implemented: `LG`: log-Gamma prior is the default prior with `a = 1` and `b = 0.1` parametrization; `PC`: Penalized Complexity prior (Simpson et al., 2017); `HC`: Half-Cauchy prior (Wang X et al., 2018).
`n_Control`	Number of controls samples.
`n_Case`	Number of case samples.
`level_Control`	Level of significance of the differential methylation test between case and control corrected samples. Default value is `level_Control = 0.9986501` which correspond to z = 3 quantile for normally distributed data.
`opt_BoxPlot`	Boxplot option: If `opt_BoxPlot = 0` (default), BoxPlot is centered around the median, whereas if `opt_BoxPlot = 1` it is centered around the mean.
`dir`	In Unix-specific OS, user-specified directory where the plots in `pdf` format are saved. If the directory is not specified, figures are saved in the current working directory.
`printing`	If `printing = TRUE` (default), the corrected methylation levels of the case/control samples using MethylCal calibration (Ochoa et al., 2019) are printed on the screen.
`plotting`	If `plotting = TRUE` (default), the corrected methylation levels for the control samples as well as its (1-`level_Control)`% confidence interval are depicted. BoxPlot of the corrected methylation levels are also shown for each control sample. If cases are included, a second figure presents a BoxPlot for each case sample as well as the controls' (1-`level_Control)`% confidence interval and the hyper- and hypo-methylated cases (top red triangles).
`cex_par`	Number indicating the amount by which plotting text and symbols should be scaled relative to the default (`cex_par = 1`).

This function returns the corrected methylation level for the control and (if selected) case samples using MethylCal calibration. Based a parametric t-test at (1-level_Control)%, hyper- and hypo-methylated cases are also flagged.

In Unix-specific OS, figures are saved in the current directory, unless otherwise specified by the user, in pdf format. In Windows OS, figures are printed on the screen.

Ochoa E, Zuber V, Fernandez-Jimenez N, Bilbao JR, Clark GR, Maher ER and Bottolo L. MethylCal: Bayesian calibration of methylation levels. Submitted. 2019.

Wang X, Ryan YY, Faraway JJ. Bayesian Regression Modeling with INLA. 2018, 1st edition. Chapman and Hall/CRC.

Simpson S, Rue H, Riebler A, Martins TG, Sorbye SH. Penalising model component complexity: A principled, practical approach to constructing priors. Statist Sci. 2017; 1:1-28. (doi)

Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J Roy Stat Soc B Met. 2009; 71(2):319-392. (doi)

data(BWS_data)
AMP = c(0, 25, 50, 75, 100)
data = Formatting(BWS_data, AMP = AMP, n_Control = 15)
corr_data = MethylCalCorrection(data, Target = "KCNQ1OT1", n_Control = 15)

data(Celiac_data)
AMP = c(0, 12.5, 25, 37.5, 50, 62.5, 87.5, 100)
data = Formatting(Celiac_data, AMP = AMP, n_Control = 13, n_Case = (2 * 17))
corr_data = MethylCalCorrection(data, Target = "NFKBIA", n_Control = 13, n_Case = (2 * 17), 
opt_BoxPlot = 1)