radSensitivitySig: Creates a signature representing the association between gene...
In RadioGx: Analysis of Large-Scale Radio-Genomic Data

Description Usage Arguments Value Examples

Given a RadioSet of the sensitivity experiment type, and a list of radiation types, the function will compute a signature for the effect of gene expression on the molecular profile of a cell. The function returns the estimated coefficient, the t-stat, the p-value and the false discovery rate associated with that coefficient, in a 3 dimensional array, with genes in the first direction, drugs in the second, and the selected return values in the third.

radSensitivitySig(
  rSet,
  mDataType,
  radiation.types,
  features,
  sensitivity.measure = "AUC_recomputed",
  molecular.summary.stat = c("mean", "median", "first", "last", "or", "and"),
  sensitivity.summary.stat = c("mean", "median", "first", "last"),
  returnValues = c("estimate", "pvalue", "fdr"),
  sensitivity.cutoff = NA,
  standardize = c("SD", "rescale", "none"),
  nthread = 1,
  verbose = TRUE,
  ...
)

`rSet`	A `RadioSet` of the perturbation experiment type
`mDataType`	`character` which one of the molecular data types to use in the analysis, out of dna, rna, rnaseq, snp, cnv
`radiation.types`	`character` a vector of radiation.types for which to compute the signatures. Should match the names used in the PharmacoSet.
`features`	`character` a vector of features for which to compute the signatures. Should match the names used in correspondant molecular data in PharmacoSet.
`sensitivity.measure`	`character` which measure of the radiation sensitivity should the function use for its computations? Use the sensitivityMeasures function to find out what measures are available for each PSet.
`molecular.summary.stat`	What summary statistic should be used to summarize duplicates for cell line molecular profile measurements?
`sensitivity.summary.stat`	What summary statistic should be used to summarize duplicates for cell line sensitivity measurements?
`returnValues`	`character` Which of estimate, t-stat, p-value and fdr should the function return for each gene?
`sensitivity.cutoff`	Allows to provide upper and lower bounds to sensitivity measures in the cases where the values exceed physical values due to numerical or other errors.
`standardize`	`character` One of "SD", "rescale", or "none", for the form of standardization of the data to use. If "SD", the the data is scaled so that SD = 1. If rescale, then the data is scaled so that the 95 interquantile range lies in [0,1]. If none no rescaling is done.
`nthread`	`numeric` if multiple cores are available, how many cores should the computation be parallelized over?
`verbose`	`boolean` 'TRUE' if the warnings and other infomrative message shoud be displayed
`...`	additional arguments not currently fully supported by the function

list a 3D array with genes in the first dimension, radiation.types in the second, and return values in the third.

data(clevelandSmall)
rad.sensitivity <- radSensitivitySig(clevelandSmall, mDataType="rna",
             nthread=1, features = fNames(clevelandSmall, "rna")[1],
             radiation.types=radiationTypes(clevelandSmall))
print(rad.sensitivity)