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Merge the discovery datasets with each validation dataset, train a glmnet model on the samples from the discovery datasets, then predicts the response variable for the samples in the respective validation dataset.

Usage

metapredict(
  ematList,
  studyMetadata,
  sampleMetadata,
  discoveryStudyNames,
  alpha,
  lambda,
  weights,
  batchColname = "study",
  covariateName = NA,
  className = "class",
  type = "response",
  ...
)

Arguments

ematList

Named list of expression matrices.

studyMetadata

data.frame of study metadata.

sampleMetadata

data.frame of sample metadata, with rownames corresponding to sample names.

discoveryStudyNames

vector of study names for training.

alpha

value of alpha for the elastic net mixing parameter.

lambda

value of regularization parameter.

weights

vector of weights for training the glmnet model.

batchColname

column in sampleMetadata containing batch information for sva::ComBat().

covariateName

column in sampleMetadata containing additional covariates for sva::ComBat() besides batch.

className

column in sampleMetadata containing values of the response variable.

type

type of prediction to make, passed to glmnet::predict.glmnet().

...

Other arguments passed to glmnet::glmnet().

Value

A named list of objects from glmnet::predict.glmnet().