cmds {htSeqTools} | R Documentation

## Classical Multi-Dimensional Scaling

### Description

`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.

### Usage

    cmds(x, k=2, logscale=TRUE, mc.cores=1, cor.method='pearson')

### Arguments

`x` | A `list` object, e.g. each element containing the output of a sequencing run.
`k` | Dimensionality of the reconstructed space, typically set to 2 or 3.
`logscale` | If set to `TRUE` correlations are computed for `log(x+1)`.
`mc.cores` | Number of cores. Setting `mc.cores>1` allows running computations in parallel. Setting `mc.cores` to too large a value may require a lot of memory.
`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.

### Value

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.

### Methods

`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.

### Examples

    data(htSample)
    cmds1 <- cmds(htSample)

    cmds1
    plot(cmds1)

[Package htSeqTools version 1.30.0 Index]