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.
1 |
x |
A |
k |
Dimensionality of the reconstructed space, typically set to 2 or 3. |
logscale |
If set to |
mc.cores |
Number of cores. Setting |
cor.method |
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
between elements, dapprox
with the distances between objects in
the approximated space, and R.square
indicating the percentage
of variability in d
accounted for by dapprox
.
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
correlation coefficient.
signature(x = "list")
Use Classical
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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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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