summarizeMolecularProfiles-PharmacoSet-method: Takes molecular data from a PharmacoSet, and summarises them...
In PharmacoGx: Analysis of Large-Scale Pharmacogenomic Data

Description Usage Arguments Value Examples

Given a PharmacoSet with molecular data, this function will summarize the data into one profile per cell line, using the chosen summary.stat. Note that this does not really make sense with perturbation type data, and will combine experiments and controls when doing the summary if run on a perturbation dataset.

## S4 method for signature 'PharmacoSet'
summarizeMolecularProfiles(
  object,
  mDataType,
  cell.lines,
  features,
  summary.stat = c("mean", "median", "first", "last", "and", "or"),
  fill.missing = TRUE,
  summarize = TRUE,
  verbose = TRUE,
  binarize.threshold = NA,
  binarize.direction = c("less", "greater"),
  removeTreated = TRUE
)

`object`	`PharmacoSet` The PharmacoSet to summarize
`mDataType`	`character` which one of the molecular data types to use in the analysis, out of all the molecular data types available for the pset for example: rna, rnaseq, snp
`cell.lines`	`character` The cell lines to be summarized. If any cell.line has no data, missing values will be created
`features`	`caracter` A vector of the feature names to include in the summary
`summary.stat`	`character` which summary method to use if there are repeated cell.lines? Choices are "mean", "median", "first", or "last" In case molecular data type is mutation or fusion "and" and "or" choices are available
`fill.missing`	`boolean` should the missing cell lines not in the molecular data object be filled in with missing values?
`summarize`	A flag which when set to FALSE (defaults to TRUE) disables summarizing and returns the data unchanged as a ExpressionSet
`verbose`	`boolean` should messages be printed
`binarize.threshold`	`numeric` A value on which the molecular data is binarized. If NA, no binarization is done.
`binarize.direction`	`character` One of "less" or "greater", the direction of binarization on binarize.threshold, if it is not NA.
`removeTreated`	`logical` If treated/perturbation experiments are present, should they be removed? Defaults to yes.

matrix An updated PharmacoSet with the molecular data summarized per cell line.

data(GDSCsmall)
GDSCsmall <- summarizeMolecularProfiles(GDSCsmall,
                    mDataType = "rna", cell.lines=cellNames(GDSCsmall),
                    summary.stat = 'median', fill.missing = TRUE, verbose=TRUE)
GDSCsmall