For a given cutpoint (previously estimated with the function estimateCutPoint), 'selectDIMP' will return the differentially informative methylated positions (DMPs). DMPs are cytosine positions for which the divergence is greater than the cutpoint.

selectDIMP(
LR,
div.col = NULL,
pval.col = NULL,
absolute = FALSE,
cutpoint,
tv.col = NULL,
tv.cut = NULL
)

# S3 method for InfDiv
print(x, ...)

# S3 method for pDMP
print(x, ...)

# S3 method for testDMP
print(x, ...)

Arguments

LR

An object from 'pDMP' class.

div.col

Number of the column where the divergence variable (i.e., Hellinger divergence or total variation) is located in the GRanges meta-columns.

pval.col

If the cutpoints is a p-value, then the column number for p-values should be provided. Default: NULL. Notice that one of the parameter values div.col or pval.col must be given.

absolute

Logic (default, FALSE). Total variation (TV, the difference of methylation levels) is normally an output in the downstream MethylIT analysis. If 'absolute = TRUE', then TV is transformed into $$|TV|$$, which is an information divergence that can be fitted to Weibull or to Generalized Gamma distribution. So, if the nonlinear fit was performed for $$|TV|$$, then here absolute must be set to TRUE.

cutpoint

Cutpoint to select DMPs. Cytosine positions with divergence greater than 'cutpoint' will selected as DMPs. Cutpoints are estimated with the function 'estimateCutPoint'.

tv.col

Column number for the total variation to be used for filtering cytosine positions (if provided).

tv.cut

If tv.cut and tv.col are provided, then cytosine sites $$k$$ with $$|TV| < tv.cut$$ are removed.

x

An object from 'pDMP_OR_InfDiv' class

Value

An object from 'pDMP' class containing only differentially informative position (DMPs).

Details

Theoretically a DMP denotes a cytosine position with high probability to be differentially methylated. That is, in the statistical molecular-biophysics context, a DMP must be considered only in a probabilistic term and not as an absolute deterministic experimental output.

The uncertainty and dynamics of the DNA methylation process, the continuous action of the omnipresent thermal fluctuations, as well as, the inherent stochasticity of the biochemical reactions make it impossible to ensure whether a specific cytosine position is methylated in an absolutely deterministic sense. Notice that the concept of DMP is not applicable to a single cell (if we use an instrumentation/protocol to directly measure methylation at the molecular level, and not via PCR), since a concrete, single DNA cytosine position in a single cell is methylated or not methylated.

However, when pooling DNA extracted from a tissue, the previous reasonings about uncertainty hold plus an additional uncertainty factor: cells from the same tissue are not synchronized but are found in different stages of their ontogenetic developments. Hence, the DMP concept holds in the mentioned circumstances where the uncertainty of methylation is present.

Examples

## Get a dataset of potential signals and the estimated cutpoint from the
## package
data(PS, cutpoint)

## The estimated cutpoints are used to discriminate signals from the noise.
## That is, DMPs are selected using the cupoints
DMPs <- selectDIMP(PS, div.col = 9L, cutpoint = cutpoint\$cutpoint)