DiffusionMap class {destiny} | R Documentation

## Create a diffusion map of cells

### Description

The provided data can be a double matrix of expression data or a data.frame with all non-integer (double) columns being treated as expression data features (and the others ignored), or an ExpressionSet.

### Usage

    DiffusionMap(data = stopifnot_distmatrix(distance), sigma = "local",
      k = find_dm_k(n_samples(data, distance) - 1L), n_eigs = min(20L,
      n_samples(data, distance) - 2L), density_norm = TRUE, ...,
      distance = c("euclidean", "cosine", "rankcor"), n_local = seq(to = min(k,
      7L), length.out = min(k, 3L)), rotate = FALSE, censor_val = NULL,
      censor_range = NULL, missing_range = NULL, vars = NULL,
      verbose = !is.null(censor_range), suppress_dpt = FALSE)

### Arguments

`data` | Expression data to be analyzed and covariates. Provide `vars` to select specific columns other than the default: all double value columns. If `distance` is a distance matrix, `data` has to be a `data.frame` with covariates only.
`sigma` | Diffusion scale parameter of the Gaussian kernel. One of `'local'`, `'global'`, a (numeric) global sigma or a Sigmas object. When choosing `'global'`, a global sigma will be calculated using `find_sigmas`. (Optional. default: `'local'`) A larger sigma might be necessary if the eigenvalues can not be found because of a singularity in the matrix
`k` | Number of nearest neighbors to consider (default: a guess betweeen 100 and n - 1. See `find_dm_k`).
`n_eigs` | Number of eigenvectors/values to return (default: 20)
`density_norm` | logical. If TRUE, use density normalisation
`...` | All parameter after this are optional and have to be specified by name
`distance` | Distance measurement method applied to `data` or a distance matrix/`dist`. For the allowed values, see `find_knn`
`n_local` | If `sigma == 'local'`, the `n_local`th nearest neighbor(s) determine(s) the local sigma
`rotate` | logical. If TRUE, rotate the eigenvalues to get a slimmer diffusion map
`censor_val` | Value regarded as uncertain. Either a single value or one for every dimension (Optional, default: censor_val)
`censor_range` | Uncertainity range for censoring (Optional, default: none). A length-2-vector of certainty range start and end. TODO: also allow 2\times G matrix
`missing_range` | Whole data range for missing value model. Has to be specified if NAs are in the data
`vars` | Variables (columns) of the data to use. Specifying NULL will select all columns (default: All floating point value columns)
`verbose` | Show a progressbar and other progress information (default: do it if censoring is enabled)
`suppress_dpt` | Specify TRUE to skip calculation of necessary (but spacious) information for `DPT` in the returned object (default: FALSE)

### Value

A DiffusionMap object:

### Slots

`eigenvalues`

  Eigenvalues ranking the eigenvectors

`eigenvectors`

  Eigenvectors mapping the datapoints to `n_eigs` dimensions

`sigmas`

  Sigmas object with either information about the find_sigmas heuristic run or just local or optimal_sigma.

`data_env`

  Environment referencing the data used to create the diffusion map

`eigenvec0`

  First (constant) eigenvector not included as diffusion component.

`transitions`

  Transition probabilities. Can be NULL

`d`

  Density vector of transition probability matrix

`d_norm`

  Density vector of normalized transition probability matrix

`k`

  The k parameter for kNN

`n_local`

  The `n_local`th nearest neighbor(s) is/are used to determine local kernel density

`density_norm`

  Was density normalization used?

`rotate`

  Were the eigenvectors rotated?

`distance`

  Distance measurement method used

`censor_val`

  Censoring value

`censor_range`

  Censoring range

`missing_range`

  Whole data range for missing value model

`vars`

  Vars parameter used to extract the part of the data used for diffusion map creation

### See Also

DiffusionMap-methods to get and set the slots. `find_sigmas` to pre-calculate a fitting global `sigma` parameter

### Examples

    data(guo)
    DiffusionMap(guo)
    DiffusionMap(guo, 13, censor_val = 15, censor_range = c(15, 40), verbose = TRUE)

    covars <- data.frame(covar1 = letters[1:100])
    dists <- dist(matrix(rnorm(100*10), 100))
    DiffusionMap(covars, distance = dists)

[Package destiny version 2.6.2 Index]