run.diffusion.map: Run diffusion map on PCA data (PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding)

Description

This function takes an object of class iCellR and runs diffusion map on PCA data.

Usage

run.diffusion.map(
  x = NULL,
  dims = 1:10,
  method = "destiny",
  ndim = 3,
  k = 5,
  alpha = 40,
  n.landmark = 2000,
  gamma = 1,
  t = "auto",
  knn.dist.method = "euclidean",
  init = NULL,
  mds.method = "metric",
  mds.dist.method = "euclidean",
  t.max = 100,
  npca = 100,
  plot.optimal.t = FALSE,
  verbose = 1,
  n.jobs = 1,
  seed = NULL,
  potential.method = NULL,
  use.alpha = NULL,
  n.svd = NULL,
  pca.method = NULL,
  g.kernel = NULL,
  diff.op = NULL,
  landmark.transitions = NULL,
  diff.op.t = NULL,
  dist.method = NULL
)

Value

An object of class iCellR.

Arguments

x: An object of class iCellR.
dims: PC dimentions to be used for UMAP analysis.
method: diffusion map method, default = "phate".
ndim: int, optional, default: 2 number of dimensions in which the data will be embedded
k: int, optional, default: 5 number of nearest neighbors on which to build kernel
alpha: int, optional, default: 40 sets decay rate of kernel tails. If NULL, alpha decaying kernel is not used
n.landmark: int, optional, default: 2000 number of landmarks to use in fast PHATE
gamma: float, optional, default: 1 Informational distance constant between -1 and 1. gamma=1 gives the PHATE log potential, gamma=0 gives a square root potential.
t: int, optional, default: 'auto' power to which the diffusion operator is powered sets the level of diffusion
knn.dist.method: string, optional, default: 'euclidean'. recommended values: 'euclidean', 'cosine', 'precomputed' Any metric from scipy.spatial.distance can be used distance metric for building kNN graph. If 'precomputed', data should be an n_samples x n_samples distance or affinity matrix. Distance matrices are assumed to have zeros down the diagonal, while affinity matrices are assumed to have non-zero values down the diagonal. This is detected automatically using data[0,0]. You can override this detection with knn.dist.method='precomputed_distance' or knn.dist.method='precomputed_affinity'.
init: phate object, optional object to use for initialization. Avoids recomputing intermediate steps if parameters are the same.
mds.method: string, optional, default: 'metric' choose from 'classic', 'metric', and 'nonmetric' which MDS algorithm is used for dimensionality reduction
mds.dist.method: string, optional, default: 'euclidean' recommended values: 'euclidean' and 'cosine'
t.max: int, optional, default: 100. Maximum value of t to test for automatic t selection.
npca: int, optional, default: 100 Number of principal components to use for calculating neighborhoods. For extremely large datasets, using n_pca < 20 allows neighborhoods to be calculated in log(n_samples) time.
plot.optimal.t: boolean, optional, if TRUE, produce a plot showing the Von Neumann Entropy curve for automatic t selection.
verbose: int or boolean, optional (default : 1) If TRUE or > 0, message verbose updates.
n.jobs: int, optional (default: 1) The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n.cpus + 1 + n.jobs) are used. Thus for n_jobs = -2, all CPUs but one are used
seed: int or NULL, random state (default: NULL)
potential.method: Deprecated. For log potential, use gamma=1. For sqrt potential, use gamma=0.
use.alpha: Deprecated To disable alpha decay, use alpha=NULL
n.svd: Deprecated.
pca.method: Deprecated.
g.kernel: Deprecated.
diff.op: Deprecated.
landmark.transitions: Deprecated.
diff.op.t: Deprecated.
dist.method: Deprecated.