ExpressionPhenoTest {phenoTest} | R Documentation

## Tests univariate association between a list of phenotype variables and gene expression.

### Description

Tests univariate association between a list of phenotype variables and gene expression.

### Usage

    ExpressionPhenoTest(x, vars2test, adjustVars,
    p.adjust.method='BH',continuousCategories=3,mc.cores,approach='frequentist')

### Arguments

`x` | ExpressionSet containing expression levels in `exprs(x)` and phenotype information in `pData(x)`.
`vars2test` | list with components 'continuous', 'categorical', 'ordinal' and 'survival' indicating which phenotype variables should be tested. 'continuous', 'categorical' and 'ordinal' must be character vectors, 'survival' a matrix with columns named 'time' and 'event'. The names must match names in names(pData(x)).
`adjustVars` | variables that will be used as adjustment variables when fitting linear models and/or cox models. This variables have to exist in `colnames(pData(x))`.
`p.adjust.method` | method for p-value adjustment, passed on to `p.adjust`. Valid values are c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none").
`continuousCategories` | number of categories used for continuous variables.
`mc.cores` | the number of cores to use, i.e. how many processes will be spawned (at most).
`approach` | this can be either 'frequentist' or 'bayesian'. With frequentist pvalues will be computed. With 'bayesian' posterior probabilities will be computed.

### Details

If approach is 'frequentist': -The effect of both continuous, categorical and ordinal phenotype variables on gene expression levels are tested via lmFit. -For ordinal variables a single coefficient is used to test its effect on gene expression (trend test), which is then used to obtain a P-value (means for each category are reported in the output). -Gene expression effects on survival are tested via Cox proportional hazards model, as implemented in function 'coxph'.

If approach is bayesian posterior probabilities are computed comparing the BIC of a model with the variable of interest as explanatory variable against the BIC of the same model without the variable of interest as explanatory variable.

### Value

The output is an `epheno` object, which basically extends an `ExpressionSet` object. The means, fold changes, standarized hazard ratios and pvalues are stored in the `experimentData` slot which is accessible with the `exprs` method. Information about the kind of information of each variable can be found in the `phenoData` slot which is accessible with the `pData` method.

There are several methods that can be used to access the information stored in an `epheno` object. For more information please type one of the following: `getFc(x), getHr(x), getMeans(x), getSignif, getPvals(x), getPostProbs, getSummaryDif(x), logFcHr(x), p.adjust.method(x), phenoClass(x), phenoNames(x)`.

### Author(s)

David Rossell

### References

Kass R.E. and Wasserman L. A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion. Journal of the American Statistical Association, 90, pp. 928-934.

### Examples

    #load eset
    data(eset)
    eset

    #prepare vars2test
    survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE)
    colnames(survival) <- c('event','time')
    vars2test <- list(survival=survival)

    #run ExpressionPhenoTest
    epheno <- ExpressionPhenoTest(eset,vars2test,p.adjust.method='none')
    epheno

[Package phenoTest version 1.42.0 Index]