imputeKNN: Impute missing values/NAs with KNN

In biobenkj/compartmentalizer: Higher-order chromatin domain inference in single samples from methylation arrays and single cells from scRNA-seq and scATAC-seq

Impute missing values/NAs with KNN

Description

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

data("meth_array_450k_chr14", package = "compartmap")
#impute
imputed <- imputeKNN(array.data.chr14, assay = "array")

biobenkj/compartmentalizer documentation built on Oct. 13, 2023, 5:26 p.m.