DiffusionMap-class: Create a diffusion map of cells
In destiny: Creates diffusion maps

Description Usage Arguments Value Slots See Also Examples

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), an ExpressionSet, or a SingleCellExperiment.

DiffusionMap(
  data = stopifnot_distmatrix(distance),
  sigma = "local",
  k = find_dm_k(dataset_n_observations(data, distance) - 1L),
  n_eigs = min(20L, dataset_n_observations(data, distance) - 2L),
  density_norm = TRUE,
  ...,
  distance = c("euclidean", "cosine", "rankcor", "l2"),
  n_pcs = NULL,
  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,
  knn_params = list(),
  verbose = !is.null(censor_range),
  suppress_dpt = FALSE
)

`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
`...`	Unused. All parameters to the right of the `...` have to be specified by name (e.g. `DiffusionMap(data, distance = 'cosine')`)
`distance`	Distance measurement method applied to `data` or a distance matrix/`dist`. For the allowed values, see `find_knn`. If this is a `sparseMatrix`, zeros are interpreted as "not a close neighbors", which allows the use of kNN-sparsified matrices (see the return value of `find_knn`.
`n_pcs`	Number of principal components to compute to base calculations on. Using e.g. 50 DCs results in more regular looking diffusion maps. The default NULL will not compute principal components, but use `reducedDims(data, 'pca')` if present. Set to NA to suppress using PCs.
`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)
`knn_params`	Parameters passed to `find_knn`
`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)

A DiffusionMap object:

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_pcs: Number of principal components used in kNN computation (NA if raw data was used)
n_local: The n_localth 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
knn_params: Parameters passed to find_knn

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

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)