drugSensitivitySig {Xeva} | R Documentation |
Given a Xeva object and drug name, this function will return sensitivity values for all the genes/features.
drugSensitivitySig(object, drug, mDataType = NULL, molData = NULL, features = NULL, model.ids = NULL, model2bidMap = NULL, sensitivity.measure = "slope", fit = c("lm", "CI", "pearson", "spearman", NA), standardize = c("SD", "rescale", "none"), nthread = 1, tissue = NULL, verbose = TRUE)
object |
The |
drug |
Name of the drug. |
mDataType |
Molecular data type. |
molData |
External data matrix. Rows as features and columns as samples. |
features |
Set which molecular data features to use. Default |
model.ids |
Set which |
model2bidMap |
A |
sensitivity.measure |
Name of the sensitivity measure. |
fit |
Association method to use, can be 'lm', 'CI', 'pearson' or 'spearman'. If 'NA' only the data will be return. Default |
standardize |
Default |
nthread |
number of threads |
tissue |
tissue type. Default |
verbose |
Default |
Method to compute association can be specified by fit
. It can be one of the:
"lm" for linear models
"CI" for concordance index
"pearson" for Pearson correlation
"spearman" for Spearman correlation
If fit is set to NA, processed data (an ExpressionSet) will be returned.
A matrix of values can be directly passed to molData.
In case where a model.id
maps to multiple biobase.id
s, the first biobase.id
in the data.frame
will be used.
A data.frame
with features and values.
data(brca) senSig <- drugSensitivitySig(object=brca, drug="tamoxifen", mDataType="RNASeq", features=c(1,2,3,4,5), sensitivity.measure="slope", fit = "lm") ## example to compute the Pearson correlation between gene expression and PDX response senSig <- drugSensitivitySig(object=brca, drug="tamoxifen", mDataType="RNASeq", features=c(1,2,3,4,5), sensitivity.measure="slope", fit = "pearson")