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 
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 
p.adjust.method 
method for pvalue adjustment, passed on to

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 Pvalue (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. 928934.
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