R: Create a diffusion map of cells

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), an ExpressionSet, or a SingleCellExperiment.

Usage

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"),
  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
`...`	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`
`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_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

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 2.12.0 Index]