magieR: magieR Run magic with R
In maximilian-heeg/magieR: Run `magic-impute` on scRNA Seq dataset in R

magieR

R Documentation

magieR Run magic with R

Description

magieR Run magic with R

Usage

magieR(x, ...)

## S4 method for signature 'ANY'
magieR(
  x,
  genes = NULL,
  knn = 5,
  knn.max = NULL,
  decay = 1,
  t = 3,
  npca = 100,
  solver = "exact",
  t.max = 20,
  knn.dist.method = "euclidean",
  verbose = 1,
  n.jobs = 1,
  seed = NULL,
  ...
)

## S4 method for signature 'SingleCellExperiment'
magieR(x, assay = "logcounts", altExpName = "magic", ...)

## S4 method for signature 'Seurat'
magieR(x, slot = "data", assayName = "magic", ...)

Arguments

`x`	A matrix with genes as columns and observations as rows. Alternatively, a SingleCellExperiment or a Seurat object. For the SingleCellExperiment and Seurat methods, further arguments to pass to the ANY method.
`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
`knn`	int, optional, default: 5 number of nearest neighbors on which to compute bandwidth
`knn.max`	int, optional, default: NULL maximum number of neighbors for each point. If NULL, defaults to 3*knn
`decay`	int, optional, default: 1 sets decay rate of kernel tails. If NULL, alpha decaying kernel is not used
`t`	int, optional, default: 3 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.
`solver`	str, optional, default: 'exact' Which solver to use. "exact" uses the implementation described in van Dijk et al. (2018). "approximate" uses a faster implementation that performs imputation in the PCA space and then projects back to the gene space. Note, the "approximate" solver may return negative values.
`t.max`	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', print 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')
`assay`	An integer scalar or string specifying the assay of `x` containing the logcount matrix.
`altExpName`	A string specifying the alternative Experiment where the resulting magic matrix is stored in. See SingleCellExperiment::altExps
`slot`	A string specifying the slot of `x` containing the logcount matrix.
`assayName`	A string specifying the new Assay where the resulting magic matrix is stored in.