classifySamples: Dichotimize a training expression set and fit a logistic...
In xlucpu/MOVICS: Multi-Omics integration and VIsualization in Cancer Subtyping

Description Usage Arguments Value Author(s)

Dichotimize a training expression set and fit a logistic ridge regression model which is applied to the test expression matirx. This function will return a set of probabilities.

classifySamples(
  trainingExprData,
  trainingPtype,
  testExprData,
  batchCorrect = "eb",
  minNumSamples = 10,
  selection = -1,
  printOutput = TRUE,
  numGenesSelected = 1000,
  numSens = 15,
  numRes = 55
)

`trainingExprData`	Gene expression matrix for samples for which we the phenotype is already known.
`trainingPtype`	The known phenotype, a vector in the same order as the columns of "trainingExprData" or with the same names as colnames of "trainingExprData".
`testExprData`	Gene expression matrix for samples on which we wish to predict a phenotype. Gene names as rows, samples names as columns.
`batchCorrect`	The type of batch correction to be used. Options are "eb", "none", .....
`minNumSamples`	The minimum number of test samples, print an error if the number of columns of "testExprData" is below this threshold. A large number of test samples may be necessary to correct for batch effects.
`selection`	How should duplicate gene ids be handled. Default is -1 which asks the user. 1 to summarize by their or 2 to disguard all duplicates.
`printOutput`	Set to FALSE to supress output
`numGenesSelected`	Specifies how genes are selected for "variableSelectionMethod". Options are "tTests", "pearson" and "spearman".
`numSens`	The number of sensitive cell lines to be fit in the logistic regression model.
`numRes`	The number of resistant cell lines fit in the logistic regression model.