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Run PHATE

Source: R/RunPHATE.R
RunPHATE.Rd

Run PHATE

Usage

RunPHATE(object, ...)

# S3 method for class 'Seurat'
RunPHATE(
  object,
  reduction = "pca",
  dims = NULL,
  features = NULL,
  assay = NULL,
  layer = "data",
  n_components = 2,
  knn = 5,
  decay = 40,
  n_landmark = 2000,
  t = "auto",
  gamma = 1,
  n_pca = 100,
  knn_dist = "euclidean",
  knn_max = NULL,
  t_max = 100,
  do_cluster = FALSE,
  backend = c("python", "cpp"),
  mds = "metric",
  mds_solver = "sgd",
  n_clusters = "auto",
  max_clusters = 100,
  reduction.name = "phate",
  reduction.key = "PHATE_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# Default S3 method
RunPHATE(
  object,
  assay = NULL,
  n_components = 2,
  knn = 5,
  decay = 40,
  n_landmark = 2000,
  t = "auto",
  gamma = 1,
  n_pca = 100,
  knn_dist = "euclidean",
  knn_max = NULL,
  t_max = 100,
  do_cluster = FALSE,
  backend = c("python", "cpp"),
  mds = "metric",
  mds_solver = "sgd",
  n_clusters = "auto",
  max_clusters = 100,
  reduction.key = "PHATE_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

Arguments

object

A Seurat object, matrix-like object, Neighbor, or Graph.

...

Passed to phate.PHATE.

reduction

Linear reduction used as input.

dims

Dimensions to use. Supply only one of dims, features, neighbor, or graph.

features

Features used instead of a reduction.

assay

Assay to use. NULL uses the default assay.

layer

Assay layer to use.

n_components

The number of PHATE components.

knn

A number of nearest neighbors on which to build kernel.

decay

The sets decay rate of kernel tails.

n_landmark

A number of landmarks to use in fast PHATE.

t

The power to which the diffusion operator is powered. This sets the level of diffusion. If "auto", t is selected according to the knee point in the Von Neumann Entropy of the diffusion operator.

gamma

The informational distance constant between -1 and 1. gamma=1 gives the PHATE log potential, gamma=0 gives a square root potential.

n_pca

A number of principal components to use for calculating neighborhoods. For extremely large datasets, using n_pca < 20 allows neighborhoods to be calculated in roughly log(n_samples) time.

knn_dist

The distance metric for k-nearest neighbors. Recommended values: "euclidean", "cosine", "precomputed".

knn_max

The maximum number of neighbors for which alpha decaying kernel is computed for each point. For very large datasets, setting knn_max to a small multiple of knn can speed up computation significantly.

t_max

The maximum t to test.

do_cluster

Whether to perform clustering on the PHATE embeddings.

backend

PHATE backend. "python" calls the upstream phate Python package and "cpp" uses the native C++ helper path.

mds

MDS algorithm passed to PHATE. The native C++ backend currently implements the "classic" path.

mds_solver

Metric MDS solver passed to the Python backend.

n_clusters

A number of clusters to be identified.

max_clusters

The maximum number of clusters to test.

reduction.name

Reduction to be stored in the Seurat object.

reduction.key

The prefix for the column names of the PHATE embeddings.

verbose

Whether to print the message. Default is TRUE.

seed.use

Random seed.

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
pancreas_sub <- RunPHATE(
  object = pancreas_sub,
  features = SeuratObject::VariableFeatures(pancreas_sub)
)
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "phate"
)
} # }