run.diffusion.map {iCellR}

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
)

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.

Value

An object of class iCellR.