cmds obtain the coordinates of the elements in
x in a
k dimensional space
which best approximate the distances between objects.
For high-throughput sequencing data we define the distance between two
samples as 1 - correlation between their respective coverages.
This provides PCA analog for sequencing data.
Dimensionality of the reconstructed space, typically set to 2 or 3.
If set to
Number of cores. Setting
A character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated.
The function returns a
mdsFit object, with slots
points containing the coordinates,
d with the distances
dapprox with the distances between objects in
the approximated space, and
R.square indicating the percentage
of variability in
d accounted for by
Since the coverage distribution is typically highly asymetric, setting
logscale=TRUE reduces the influence of the highest coverage
regions in the distance computation, as this is based on the Pearson
signature(x = "list")
Multi-Dimensional Scaling to plot each element of the
list object in a k-dimensional space. The coverage is
computed for each element in
x, and the pairwise correlations
between elements is used to define distances.
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