runDPCoA {mia} | R Documentation

## Calculation of Double Principal Correspondance analysis

### Description

Double Principal Correspondance analysis is made available via the `ade4` package in typical fashion. Results are stored in the `reducedDims` and are available for all the expected functions.

### Usage

    calculateDPCoA(x, y, ...)

    ## S4 method for signature 'ANY,ANY'
    calculateDPCoA(
      x,
      y,
      ncomponents = 2,
      ntop = NULL,
      subset_row = NULL,
      scale = FALSE,
      transposed = FALSE
    )

    ## S4 method for signature 'TreeSummarizedExperiment,missing'
    calculateDPCoA(
      x,
      ...,
      abund_values = exprs_values,
      exprs_values = "counts",
      dimred = NULL,
      n_dimred = NULL
    )

    runDPCoA(x, ..., altexp = NULL, name = "DPCoA")

### Arguments

`x` | For `calculateDPCoA`, a numeric matrix of expression values where rows are features and columns are cells. Alternatively, a `TreeSummarizedExperiment` containing such a matrix. For `runDPCoA` a TreeSummarizedExperiment containing the expression values as well as a `rowTree` to calculate `y` using `cophenetic.phylo`.
`y` | a `dist` or a symmetric `matrix` compatible with `ade4:dpcoa`
`...` | Currently not used.
`ncomponents` | Numeric scalar indicating the number of DPCoA dimensions to obtain.
`ntop` | Numeric scalar specifying the number of features with the highest variances to use for dimensionality reduction. Alternatively `NULL`, if all features should be used. (default: `ntop = NULL`)
`subset_row` | Vector specifying the subset of features to use for dimensionality reduction. This can be a character vector of row names, an integer vector of row indices or a logical vector.
`scale` | Logical scalar, should the expression values be standardized?
`transposed` | Logical scalar, is x transposed with cells in rows?
`abund_values` | a single `character` value for specifying which assay to use for calculation.
`exprs_values` | a single `character` value for specifying which assay to use for calculation. (Please use `abund_values` instead. At some point `exprs_values` will be disabled.)
`dimred` | String or integer scalar specifying the existing dimensionality reduction results to use.
`n_dimred` | Integer scalar or vector specifying the dimensions to use if dimred is specified.
`altexp` | String or integer scalar specifying an alternative experiment containing the input data.
`name` | String specifying the name to be used to store the result in the reducedDims of the output.

### Details

In addition to the reduced dimension on the features, the reduced dimension for samples are returned as well as `sample_red` attribute. `eig`, `feature_weights` and `sample_weights` are returned as attributes as well.

### Value

For `calculateDPCoA` a matrix with samples as rows and CCA dimensions as columns

For `runDPCoA` a modified `x` with the results stored in `reducedDim` as the given `name`

### See Also

`plotReducedDim` `reducedDims`

### Examples

    data(esophagus)
    dpcoa <- calculateDPCoA(esophagus)
    head(dpcoa)

    esophagus <- runDPCoA(esophagus)
    reducedDims(esophagus)

    library(scater)
    plotReducedDim(esophagus, "DPCoA")

[Package mia version 1.2.7 Index]