Perform cross-validation of merged gene expression data.

Run cross-validation to predict a response variable from gene expression data across multiple studies.

Usage

metapredictCv(
  ematMerged,
  sampleMetadata,
  weights,
  alpha,
  nFolds = 10,
  foldid = NA,
  nRepeats = 3,
  yName = "class",
  addlFeatureColnames = NA,
  ...
)

Arguments

ematMerged: matrix of gene expression for genes by samples.
sampleMetadata: data.frame of sample metadata, with rownames corresponding to sample names.
weights: vector of weights.
alpha: vector of values for alpha, the elastic net mixing parameter.
nFolds: number of folds. Ignored, if foldid is not NA.
foldid: vector of values specifying what fold each observation is in.
nRepeats: number of times to perform cross-validation. Ignored, if foldid is not NA.
yName: column in sampleMetadata containing values of the response variable.
addlFeatureColnames: optional vector of column names containing other features to be used for predicting the response variable.
...: Other arguments passed to glmnet::cv.glmnet().

Value

A list of cv.glmnet objects.