relabelStates {RJaCGH} | R Documentation |
For every model, each hidden state is assigned to a state of copy number alteration ('normal', 'loss1', 'loss2', 'gain1', 'gain2'...)
relabelStates(obj, normal.reference = 0, normal.ref.percentile = 0.95, auto.label = NULL) ## S3 method for class 'RJaCGH': relabelStates(obj, normal.reference = 0, normal.ref.percentile = 0.95, auto.label = NULL) ## S3 method for class 'RJaCGH.Chrom': relabelStates(obj, normal.reference = 0, normal.ref.percentile = 0.95, auto.label = NULL) ## S3 method for class 'RJaCGH.genome': relabelStates(obj, normal.reference = 0, normal.ref.percentile = 0.95, auto.label = NULL) ## S3 method for class 'RJaCGH.array': relabelStates(obj, normal.reference = 0, normal.ref.percentile = 0.95, auto.label = NULL)
obj |
An object returned form RJaCGH of class 'RJaCGH', 'RJaCGH.Chrom', 'RJaCGH.genome', 'RJaCGH.array'. |
normal.reference |
The value considered as the mean of the normal
state. See details. By default is 0 . |
normal.ref.percentile |
Percentage for the relabelling of states. See details. by default is 0.95. |
auto.label |
If not NULL, should be the minimum proportion of observations labeled as 'Normal'. See details. |
A relabelling of hidden states is performed to match biological
states. The states that have the normal.reference
value
inside a normal.ref.percentile
% probability interval
based on a normal distribution with means the median of mu
and sd the square root of the median of sigma.2
are labelled as
'Normal'. If no state is close enough to normal.reference
then
there will not be a normal state. Bear this in mind for
normalization issues.
If auto.label
is not null, closest states to 'Normal' are also
labelled as 'Normal' until a proportion of auto.label
is
reached. Please note that the default value is 0.60, so at least the
60% of the observations will be labelled as 'Normal'.
If this laeblling is not satisfactory, you can relabel manually. See
the example.
An object of the same class as obj
with hidden states relabelled.
Oscar M. Rueda and Ramon Diaz Uriarte
Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122
y <- c(rnorm(100, 0, 1), rnorm(10, -3, 1), rnorm(20, 3, 1), rnorm(100,0, 1)) Pos <- sample(x=1:500, size=230, replace=TRUE) Pos <- cumsum(Pos) Chrom <- rep(1:23, rep(10, 23)) jp <- list(sigma.tau.mu=rep(0.05, 4), sigma.tau.sigma.2=rep(0.03, 4), sigma.tau.beta=rep(0.07, 4), tau.split.mu=0.1, tau.split.beta=0.1) fit.chrom <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Chrom", burnin=10, TOT=1000, k.max = 4, jump.parameters=jp) plot(fit.chrom) fit.chrom.2 <- relabelStates(fit.chrom, normal.reference=3) plot(fit.chrom.2) ## Manual labelling fit.chrom.2[[1]][[2]]$state.labels <- c("Normal", "Normal")