impute_regions: Generic imputation return function

In CompEpigen/scMethrix: Fast and efficient summarization of single cell whole genome bisulfate data

Description

Generic imputation return function

Usage

impute_regions(
  scm = NULL,
  assay = "score",
  new_assay = "impute",
  regions = NULL,
  n_chunks = 1,
  n_threads = 1,
  overlap_type = c("within", "start", "end", "any", "equal"),
  type = c("kNN", "iPCA", "RF"),
  verbose = TRUE,
  k = 10,
  n_pc = 2,
  ...
)

Arguments

scm	scMethrix; the single cell methylation experiment
assay	string; name of an existing assay. Default = "score"
new_assay	string; name for transformed assay. Default = "new_assay"
regions	Granges; the regions to impute. Default is by chromosome.
n_chunks	integer; Number of chunks to split the scMethrix object in case it is very large. Default = 1
n_threads	integer; Maximum number of parallel instances. Default = 1
overlap_type	defines the type of the overlap of the CpG sites with the target region. Default value is within. For detailed description, see the findOverlaps function of the IRanges package.
type	string/closure; the imputation to perform "kNN","iPCA",or "RF". Otherwise, a closure can be specified that returns the imputed matrix. Default = "kNN"
verbose	boolean; Flag for outputting function status messages. Default = TRUE
k	Number of neighbors to be used in the imputation (default=10)
n_pc	the range of principal components to check when using iPCA. Caution: this can be very time-intensive
...	further arguments passed to or from other methods

Details

Uses the specified imputation operation to evaluation an scMethrix object.

Value

list; two scMethrix objects names 'training' and 'test'

References

Hastie T, Tibshirani R, Narasimhan B, Chu G (2021). impute: impute: Imputation for microarray data. R package version 1.66.0.

Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118.

Bro, R., Kjeldahl, K. Smilde, A. K. and Kiers, H. A. L. (2008) Cross-validation of component models: A critical look at current methods. Analytical and Bioanalytical Chemistry, 5, 1241-1251.

Josse, J. and Husson, F. (2011). Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis. 56 (6), pp. 1869-1879.

Examples

data('scMethrix_data')
impute_regions(scMethrix_data)

CompEpigen/scMethrix documentation built on Nov. 6, 2021, 3:09 p.m.