Pick features for cell type prediction with caret by repeated cross validation

Usage

pickCompProbesCaret(
  betas,
  meta,
  ct,
  ps,
  min.delta.beta,
  p.val,
  caretMods,
  filterK = 1000,
  seed = 1234,
  plot = TRUE,
  verbose = TRUE
)

Arguments

betas: A beta matrix of reference DNA methylation data that will be used to select for features for cell type prediction
meta: A data frame of phenotype data, specifying which of the reference samples are of what cell types
ct: A vector of characters specifying the cell types to deconvolute
ps: A character of either "any" or "both" to specify first line filter for T test prior to passing to machine learning algorithms
min.delta.beta: A numeric variable defining T test minimum delta beta for first line filter prior to passing to machine learning algorithms
p.val: A numeric variable defining maximum T test p.value for first line filter prior to passing to machine learning algorithms
caretMods: A vector of characters, selecting the models to use to pick the cell type prediction features, options include "lasso", "EL", "BLR", "CART", "RF", "GBM", "PLDA", "GAnRF", "GAnNB", "GAnSVM", and "GAnNN"
filterK: An integer, representing the number probes to input to the machine learning algorithms with top T test probes
seed: An integer specifying the seed for reproducibility
plot: A Boolean specifying whether to plot a heatmap showing the clustering performance using the probes selected
verbose: A Boolean specifying whether the function should be verbose or not