| evalDetection {MethylIT.utils} | R Documentation |
For a given cutpoint (e.g., previously estimated with the function estimateCutPoint), 'evalDetection' will return the evaluation of the methylation signal into two clases: signal from control and signal from treatment samples.
evalDetection(LR, control.names, treatment.names, cutpoint, div.col = 7L, seed = 1234, verbose = TRUE)
LR |
A list of GRanges objects (LR) including control and treatment
GRanges containing divergence values for each cytosine site in the
meta-column. LR can be generated, for example, by the function
|
control.names |
Names/IDs of the control samples, which must be include in the variable LR. |
treatment.names |
Names/IDs of the treatment samples, which must be included in the variable LR. |
cutpoint |
Cutpoint to select DIMPs. Cytosine positions with divergence greater than 'cutpoint' will selected as DIMPs. Cutpoints are estimated with the function 'estimateCutPoint'. Cytosine positions with divergence values greater than the cutpoint are considered members of the "positive class". |
div.col |
Column number for divergence variable used in the ROC analysis and estimation of the cutpoints. |
seed |
Random seed used for random number generation. |
verbose |
if TRUE, prints the function log to stdout |
The regulatory methylation signal is also an output from a natural
process that continuously takes place across the ontogenetic development
of the organisms. So, we expect to see methylation signal under natural,
ordinary conditions. Here, to evaluate the performance of signal
classification obtained with the application of some classifier/detector
or rule, the cross-tabulation of observed and predicted classes with
associated statistics are calculated with function
confusionMatrix fron package "caret".
A classification result with low accuracy and compromising values from other classification performance indicators (see below) suggest that the treatment does not induce a significant regulatory signal different from control.
the list with the statisitics returned by the function
confusionMatrix fron package "caret".
set.seed(123) #'#' To set a seed for random number generation
#'#' GRanges object of the reference with methylation levels in
#'#' its matacolumn
num.points <- 5000
Ref <- makeGRangesFromDataFrame(
data.frame(chr = '1',
start = 1:num.points,
end = 1:num.points,
strand = '*',
p1 = rbeta(num.points, shape1 = 1, shape2 = 1.5)),
keep.extra.columns = TRUE)
#'#' List of Granges objects of individuals methylation levels
Indiv <- GRangesList(
sample11 = makeGRangesFromDataFrame(
data.frame(chr = '1',
start = 1:num.points,
end = 1:num.points,
strand = '*',
p2 = rbeta(num.points, shape1 = 1.5, shape2 = 2)),
keep.extra.columns = TRUE),
sample12 = makeGRangesFromDataFrame(
data.frame(chr = '1',
start = 1:num.points,
end = 1:num.points,
strand = '*',
p2 = rbeta(num.points, shape1 = 1.6, shape2 = 2)),
keep.extra.columns = TRUE),
sample21 = makeGRangesFromDataFrame(
data.frame(chr = '1',
start = 1:num.points,
end = 1:num.points,
strand = '*',
p2 = rbeta(num.points, shape1 = 40, shape2 = 4)),
keep.extra.columns = TRUE),
sample22 = makeGRangesFromDataFrame(
data.frame(chr = '1',
start = 1:num.points,
end = 1:num.points,
strand = '*',
p2 = rbeta(num.points, shape1 = 41, shape2 = 4)),
keep.extra.columns = TRUE))
#'#' To estimate Hellinger divergence using only the methylation levels.
HD <- estimateDivergence(ref = Ref, indiv = Indiv, meth.level = TRUE,
columns = 1)
res <- evalDetection(LR = HD, control.names = c("sample11", "sample12"),
treatment.names = c("sample21", "sample22"),
cutpoint = 0.85, div.col = 3L, seed=1234, verbose=TRUE)