Run TriMap — RunTriMap • scop

Run TriMap

Usage

RunTriMap(object, ...)

# S3 method for class 'Seurat'
RunTriMap(
  object,
  reduction = "pca",
  dims = NULL,
  features = NULL,
  assay = NULL,
  layer = "data",
  n_components = 2,
  n_inliers = 12,
  n_outliers = 4,
  n_random = 3,
  distance_method = "euclidean",
  lr = 0.1,
  n_iters = 400,
  apply_pca = TRUE,
  opt_method = "dbd",
  reduction.name = "trimap",
  reduction.key = "TriMap_",
  verbose = TRUE,
  seed.use = 11L,
  backend = c("cpp", "python"),
  ...
)

# Default S3 method
RunTriMap(
  object,
  assay = NULL,
  n_components = 2,
  n_inliers = 12,
  n_outliers = 4,
  n_random = 3,
  distance_method = "euclidean",
  lr = 0.1,
  n_iters = 400,
  apply_pca = TRUE,
  opt_method = "dbd",
  reduction.key = "TriMap_",
  verbose = TRUE,
  seed.use = 11L,
  backend = c("cpp", "python"),
  ...
)

Arguments

object: A Seurat object, matrix-like object, Neighbor, or Graph.
...: Passed to the trimap.TRIMAP function.
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: A number of TriMap components.
n_inliers: A number of nearest neighbors for forming the nearest neighbor triplets.
n_outliers: A number of outliers for forming the nearest neighbor triplets.
n_random: A number of random triplets per point.
distance_method: Distance metric for TriMap. Options are: "euclidean", "manhattan", "angular", "cosine", "hamming".
lr: The learning rate for TriMap.
n_iters: A number of iterations for TriMap.
apply_pca: Whether to apply PCA before the nearest-neighbor calculation.
opt_method: Optimization method for TriMap. Options are: "dbd", "sd", "momentum".
reduction.name: Name of the reduction to be stored in the Seurat object.
reduction.key: Prefix for the column names of the TriMap embeddings.
verbose: Whether to print the message. Default is TRUE.
seed.use: Random seed.
backend: TriMap backend. "cpp" uses a compiled triplet sampler and optimizer; "python" retains the official trimap package.

Examples

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