DiffusionMap class: 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, sigma = "local", k = find_dm_k(nrow(data) - 1L),
  n_eigs = min(20L, nrow(data) - 2L), density_norm = TRUE, ...,
  distance = c("euclidean", "cosine", "rankcor"), n_local = 5L,
  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. Provide vars to select specific columns other than the default: all double value columns

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

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. Euclidean distance (default), cosine distance (\(1-corr(c_1, c_2)\)) or rank correlation distance (\(1-corr(rank(c_1), rank(c_2))\)).

n_local

If sigma == 'local', the n_localth nearest neighbor determines the local sigma.

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 is used to determine local kernel density
density_norm: Was density normalization used?
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

Run this code

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