| Package: |
| SigCheck |
| Type: |
| Package |
| Version: |
| 2.0 |
| Date: |
| 2015-05-18 |
| License: |
| Artistic-2.0 |
SigCheck, first create anew SigCheck object using the
function sigCheck.
This will establish the baseline performance
of the signature.
Next, either run specific checks, or use the high level function
sigCheckAll to run all the core functions in turn.
The three core functions enable
1) comparison of baseline performance against signatures
composed of random genes (sigCheckRandom);
2) comparison of baseline performance against known, and mostly unrelated,
gene signatures (sigCheckKnown); and
3) comparison of baseline performance against randomly
permuted data and/or metadata (sigCheckPermuted).At a minimum, SigCheck requires a data set (as an ExpressionSet),
metadata indicating the membership of each sample in one of two classes,
and a signature (a subset of features in the ExpressionSet).
If survival data are available, survival analyses are carried out.
Validation samples can be divided into two classes using one of the simple
default methods (based on overall expression value
or their first principal component).
Alternatively, more sophisticated classification algorithms can be
deployed, using the
MLearn function from the MLInterfaces package
to build a classifier (using link{smvI} by default).
If no validation samples are
specified, leave-one-out (LOO) cross-validation is utilized to build multiple
classifiers, each predicting one sample.
If no survival data are provided, signatures are evaluated
based on classification performance.
Output of each check includes the distribution of random performance scores (either survival p-value or classification performance) and the ranking of the passed signature within this distribution. An empirical p-value calculation based on this rank is also returned indicating confidence that the performance of the signature being checked has unique power.