Description Usage Arguments Value Note Examples
The function does a PCA analysis using
prcomp
function using percent
methylation matrix as an input.
1 2 3 4 |
.Object |
a |
screeplot |
a logical value indicating whether to plot the variances against the number of the principal component. (default: FALSE) |
adj.lim |
a vector indicating the propotional adjustment of xlim (adj.lim[1]) and ylim (adj.lim[2]). This is primarily used for adjusting the visibility of sample labels on the on the PCA plot. (default: c(0.0004,0.1)) |
scale |
logical indicating if |
center |
logical indicating if |
comp |
vector of integers with 2 elements specifying which components to be plotted. |
transpose |
if TRUE (default) percent methylation matrix will be transposed, this is equivalent to doing PCA on variables that are regions/bases. The resulting plot will location of samples in the new coordinate system if FALSE the variables for the matrix will be samples and the resulting plot whill show how each sample (variable) contributes to the principle component.the samples that are highly correlated should have similar contributions to the principal components. |
sd.filter |
If |
sd.threshold |
A numeric value. If
|
filterByQuantile |
A logical determining if
|
obj.return |
if the result of |
The form of the value returned by PCASamples
is
the summary of principal component analysis by
prcomp
.
cor option is not in use anymore, since prcomp
is
used for PCA analysis instead of princomp
1 2 3 4 5 6 7 | data(methylKit)
# do PCA with filtering rows with low variation, filter rows with standard
# deviation lower than the 50th percentile of Standard deviation distribution
PCASamples(methylBase.obj,screeplot=FALSE, adj.lim=c(0.0004,0.1),
scale=TRUE,center=TRUE,comp=c(1,2),transpose=TRUE,sd.filter=TRUE,
sd.threshold=0.5,filterByQuantile=TRUE,obj.return=FALSE)
|
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