Impute missing values/NAs with KNN

Usage

imputeKNN(
  obj,
  rowmax = 0.5,
  colmax = 0.8,
  k = 10,
  maxp = 1500,
  in.place = TRUE,
  drop.sparse.samps = TRUE,
  assay = c("array", "atac", "bisulfite")
)

Arguments

obj: Input SummarizedExperiment object
rowmax: Maximum fraction of NAs that can exist in a row
colmax: Maximum fraction of NAs that can exist in a column/sample
k: Number of neighbors to be used in the imputation
maxp: Largest block of regions/loci imputed using KNN
in.place: Whether to modify the Beta/counts in place (default: TRUE)
drop.sparse.samps: Whether to drop samples that are too sparse (default: TRUE)
assay: The type of assay ("array", "bisulfite")

Value

Imputed data matrix that is added to the assays slot

Examples

if (requireNamespace("minfi", quietly = TRUE)) {
  data("array_data_chr14", package = "compartmap")
  #impute
  imputed <- imputeKNN(array.data.chr14, assay = "array")
}
#> Dropping samples with >80% NAs.
#> Imputing missing data with kNN.
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