radSensitivitySig {RadioGx} | R Documentation |
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 |
mDataType |
|
radiation.types |
|
features |
|
sensitivity.measure |
|
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 |
|
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 |
|
nthread |
|
verbose |
|
... |
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)