ExpressionPhenoTest: Tests univariate association between a list of phenotype...
In phenoTest: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation.

Description Usage Arguments Details Value Author(s) References Examples

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

1 2	ExpressionPhenoTest(x, vars2test, adjustVars, p.adjust.method='BH',continuousCategories=3,mc.cores,approach='frequentist')

`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.

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.

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).

David Rossell

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.

#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