Perform cross-validation of merged gene expression data.

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

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.