`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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