predict: predict patient labels

In BaderLab/netDx: Network-based patient classifier

Description

Once a model is trained, this function is used to classify new patients using selected features

Usage

predict(
  trainMAE,
  testMAE,
  groupList,
  featSel,
  makeNetFunc,
  outDir,
  impute = FALSE,
  verbose = FALSE,
  numCores = 1L,
  JavaMemory = 4L,
  debugMode = FALSE
)

Arguments

trainMAE	(MultiAssayExperiment) patient data for training samples. Same as provided to buildPredictor()
testMAE	(MultiAssayExperiment) new patient dataset for testing model. Assays must be the same as for trainMAE.
groupList	(list) list of features used to train the model. Keys are data types, and values are lists for groupings within those datatypes. e.g. keys could include 'clinical','rna','methylation', and values within 'rna' could include pathway names 'cell cycle', 'DNA repair', etc., featSel will be used to subset
featSel	(list) selected features to be used in the predictive model. keys are patient labels (e.g. "responder/nonresponder"), and values are feature names identified by running buildPredictor(). Feature names must correspond to names of groupList, from which they will be subset.
makeNetFunc	(function) function to create PSN features from patient data. See makeNetFunc in buildPredictor() for details
outDir	(char) directory for results
impute	(logical) if TRUE imputes train and test samples separately before creating features. Currently unsupported.
verbose	(logical) print messages
numCores	(integer) number of CPU cores for parallel processing
JavaMemory	(integer) memory in (Gb) used for each fold of CV
debugMode	(logical) Set to TRUE for detailed messages. Used for debugging.

Value

(data.frame) predicted patient similarities and labels columns are: 1) ID, 2) STATUS (ground truth), 3) <label>_SCORE: similarity score for the corresponding label, 4) PRED_CLASS: predicted class

BaderLab/netDx documentation built on Sept. 26, 2021, 9:13 a.m.