DiffusionMap class {destiny} | R Documentation |
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"), 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)
data |
Expression data to be analyzed and covariates. Provide |
sigma |
Diffusion scale parameter of the Gaussian kernel. One of |
k |
Number of nearest neighbors to consider (default: a guess betweeen 100 and n - 1. See |
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 |
n_local |
If |
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 |
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_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
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)