run.impute: Impute the main data

Description

This function takes an object of class iCellR and runs imputation on the main data.

Usage

run.impute(
  x = NULL,
  imp.method = "iCellR.imp",
  dims = 1:10,
  nn = 10,
  ATAC.data = FALSE,
  rounding.digits = 4,
  round.num = TRUE,
  data.type = "pca",
  genes = "all_genes",
  k = 10,
  alpha = 15,
  t = "auto",
  npca = 100,
  init = NULL,
  t.max = 20,
  knn.dist.method = "euclidean",
  verbose = 1,
  n.jobs = 1,
  seed = NULL
)

Value

An object of class iCellR.

Arguments

x: An object of class iCellR.
imp.method: Choose between "iCellR.imp" and "magic", defualt = "iCellR.imp".
dims: PC dimentions to be used for the analysis, default = 10.
nn: Number of neighboring cells to find, default = 10.
ATAC.data: If TURE, it would normalize ATAC-Seq data and not RNA-Seq, default = FALSE.
rounding.digits: integer indicating the number of decimal places (round) or significant digits (signif) to be used.
round.num: Rounding of Numbers, default = FALSE.
data.type: Choose between "tsne", "pca", "umap", "diffusion", "knetl", default = "pca".
genes: character or integer vector, default: NULL vector of column names or column indices for which to return smoothed data If 'all_genes' or NULL, the entire smoothed matrix is returned
k: if imp.method is magic; int, optional, default: 10 number of nearest neighbors on which to build kernel
alpha: if imp.method is magic; int, optional, default: 15 sets decay rate of kernel tails. If NULL, alpha decaying kernel is not used
t: if imp.method is magic; int, optional, default: 'auto' power to which the diffusion operator is powered sets the level of diffusion. If 'auto', t is selected according to the Procrustes disparity of the diffused data.'
npca: number of PCA components that should be used; default: 100.
init: magic object, optional object to use for initialization. Avoids recomputing intermediate steps if parameters are the same.
t.max: if imp.method is magic; int, optional, default: 20 Maximum value of t to test for automatic t selection.
knn.dist.method: string, optional, default: 'euclidean'. recommended values: 'euclidean', 'cosine' Any metric from 'scipy.spatial.distance' can be used distance metric for building kNN graph.
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')