drugSensitivitySig {Xeva} | R Documentation

## get drug sensitivity values

### Description

Given a Xeva object and drug name, this function will return sensitivity values for all the genes/features.

### Usage

    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)

### Arguments

`object` | The `Xeva` dataset.
`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 `NULL` will use all features.
`model.ids` | Set which `model.id` to use from the dataset. Default `NULL` will use all `model.id`s.
`model2bidMap` | A `data.frame` with `model.id` and `biobase.id`. Default `NULL` will use internal mapping.
`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 `lm`.
`standardize` | Default `SD`. Name of the method to use for data standardization before fitting.
`nthread` | number of threads
`tissue` | tissue type. Default `NULL` uses `'tissue'` from `object`.
`verbose` | Default `TRUE` will show information

### Details

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.

### Value

A `data.frame` with features and values.

### Examples

    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")

[Package Xeva version 1.6.0 Index]