drugSensitivitySig {PharmacoGx} | R Documentation |
Given a Pharmacoset of the sensitivity experiment type, and a list of drugs, the function will compute a signature for the effect 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.
drugSensitivitySig(pSet, mDataType, drugs, 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, standardize = c("SD", "rescale", "none"), molecular.cutoff = NA, molecular.cutoff.direction = c("less", "greater"), nthread = 1, verbose = TRUE, ...)
pSet |
[PharmacoSet] a PharmacoSet 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 |
drugs |
[character] a vector of drug names 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 drug dose 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 drug pair? |
sensitivity.cutoff |
[numeric] Allows the user to binarize the sensitivity data using this threshold. |
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. |
molecular.cutoff |
Allows the user to binarize the sensitivity data using this threshold. |
molecular.cutoff.direction |
[character] One of "less" or "greater", allows to set direction of binarization. |
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, drugs in the second, and return values in the third.
data(GDSCsmall) drug.sensitivity <- drugSensitivitySig(GDSCsmall, mDataType="rna", nthread=1, features = fNames(GDSCsmall, "rna")[1]) print(drug.sensitivity)