Run PaCMAP (Pairwise Controlled Manifold Approximation)

Usage

RunPaCMAP(object, ...)

# S3 method for class 'Seurat'
RunPaCMAP(
  object,
  reduction = "pca",
  dims = NULL,
  features = NULL,
  assay = NULL,
  layer = "data",
  n_components = 2,
  n.neighbors = NULL,
  MN_ratio = 0.5,
  FP_ratio = 2,
  distance_method = "euclidean",
  lr = 1,
  num_iters = 450L,
  apply_pca = TRUE,
  init = "random",
  reduction.name = "pacmap",
  reduction.key = "PaCMAP_",
  verbose = TRUE,
  seed.use = 11L,
  ...
)

# Default S3 method
RunPaCMAP(
  object,
  assay = NULL,
  n_components = 2,
  n.neighbors = NULL,
  MN_ratio = 0.5,
  FP_ratio = 2,
  distance_method = "euclidean",
  lr = 1,
  num_iters = 450L,
  apply_pca = TRUE,
  init = "random",
  reduction.key = "PaCMAP_",
  verbose = TRUE,
  seed.use = 11L,
  ...
)

Arguments

object: An object. This can be a Seurat object, a matrix-like object, a Neighbor object, or a Graph object.
...: Additional arguments to be passed to pacmap.PaCMAP.
reduction: Which dimensionality reduction to use. Default is "pca".
dims: The dimensions to be used. Default is NULL.
features: A character vector of features to use. Default is NULL.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used.
layer: Which layer to use. Default is data.
n_components: The number of PaCMAP components. Default is 2.
n.neighbors: A number of neighbors considered in the k-Nearest Neighbor graph. Default is 10 for dataset whose sample size is smaller than 10000. For large dataset whose sample size (n) is larger than 10000, the default value is: 10 + 15 * (log10(n) - 4).
MN_ratio: The ratio of the ratio of the number of mid-near pairs to the number of neighbors. Default is 0.5.
FP_ratio: The ratio of the ratio of the number of further pairs to the number of neighbors. Default is 2.
distance_method: The distance metric to be used. Default is "euclidean".
lr: The learning rate of the AdaGrad optimizer. Default is 1.
num_iters: The number of iterations for PaCMAP optimization. Default is 450.
apply_pca: Whether pacmap should apply PCA to the data before constructing the k-Nearest Neighbor graph. Using PCA to preprocess the data can largely accelerate the DR process without losing too much accuracy. Notice that this option does not affect the initialization of the optimization process. Default is TRUE.
init: The initialization of the lower dimensional embedding. One of "pca" or "random". Default is "random".
reduction.name: The name of the reduction to be stored in the Seurat object. Default is "pacmap".
reduction.key: The prefix for the column names of the PaCMAP embeddings. Default is "PaCMAP_".
verbose: Whether to print the message. Default is TRUE.
seed.use: Random seed for reproducibility. Default is 11.

Examples

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