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\title{\bf Bioconductor's maPredictDSC package}
\author{Adi L. Tarca$^{1,2,3}$}

\maketitle

$^1$Department of Computer Science, Wayne State University\\
$^2$Bioinformatics and Computational Biology Unit of the NIH Perinatology Research Branch\\
$^3$Center for Molecular Medicine and Genetics, Wayne State University \\


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\section{Overview}

This package implements the classification pipeline of the best overall
team (Team221) (see \cite{Tarca:2013SB}) in the IMPROVER Diagnostic Signature Challenge described
in \cite{ Meyer:2012}. Additional capability is added to explore other combinations 
of methods for data preprocessing, feature ranking and classification 
described in \cite{Tarca:2013NM}. In a nutshell, with this package
one starts with Affymetrix .CEL expression files (all platforms supported) some
of which correspond to a set of training samples (class is
required, 2 classes only) while some other correspond to test samples for which the class will be predicted. One or more
models are built on the training data, and predictions are made on the
test samples. Several performance metrics used in the IMPROVER DSC can
be computed for the fitted models if the class of the test samples is
known including the Area Under the Precision-Recall Curve (AUPR), 
Belief Confusion Metric (BCM) and Correct Class Enrichment Metric (CCEM). 
Note that the sample size for this example as well as the arguments in the 
function calls below were chosen to limit the amount of time required to run the example on a decent computer (max 5 mins, as required by the Bioconductor standards).
 See the cited references for results on several datasets of much larger 
 sample size and more appropriate values for the arguments in the function calls.


\section{Developing prediction models with maPredictDSC package}

This document provides basic introduction on how to use the 
{\tt maPredictDSC} package. For extended description of the methods 
used in this package please consult these references: 
\cite{Tarca:2013SB} and \cite{Tarca:2013NM}.\\ 

We demonstrate the functionality of this package using a 
set of lung cancer samples obtained using Affymetrix HG-U133 Plus 2.0 
technology that are available from GEO. In this example we use 
7 Adenocarcinoma (AC) and 8 Squamous cell carcinoma (SCC) samples taken
at random from 3 GEO datasets (GSE10245, GSE18842 and GSE2109) and 
15 samples used for testing purpose from a dataset produced by the 
organizers of the IMPROVER Diagnostic Signature Challenge also 
available from GEO (GSE43580). The data is available in the {\tt LungCancerACvsSCCGEO} package.
The assignment of the samples into groups is defined in the
{\tt anoLC} data frame available by loading the {\tt LungCancerACvsSCCGEO}
datset as shown below:  
 
<<eval=TRUE, echo=TRUE>>=
library(maPredictDSC)
library(LungCancerACvsSCCGEO)
data(LungCancerACvsSCCGEO)
anoLC
gsLC
@

The data frame {\tt gsLC} included also in this dataset gives 
the class of the test samples that we will use later to assess
the predictions of different models produced by the {\tt predictDSC}
function which is the main function of the package.   
The {\tt predictDSC} function takes as input a folder of raw 
Affymetrix CEL files and explores a set of combinations of data 
preprocessing (rma, gcrma, mas5), feature ranking methods 
(t-test, moderated t-test, wilcoxon test) and classifier types 
(LDA, SVM, kNN). For each such combination, the optimal number of 
genes to be used in the model is automatically determined by 
optimizing the AUC statistic computed  via cross-validation on the training data. Also, 
for each combination, a final model is fitted using all training data, 
and predictions on the "Test" samples (defined as such in the 
{\tt ano} data frame) are computed.  

<<eval=TRUE, echo=TRUE>>=
modlist=predictDSC(ano=anoLC,
celfile.path=system.file("extdata/lungcancer",package="LungCancerACvsSCCGEO"),
annotation="hgu133plus2.db", preprocs=c("rma"),
filters=c("mttest","ttest"),classifiers=c("LDA","kNN"),
CVP=2,NF=4, NR=1,FCT=1.0)
@
In addition to the 27 models that can be fitted with the simple call of the function 
above, one can obtain 27 additional models by changing the FCT (fold change threshold)
from 1.0 to say 1.25 or 1.5 fold. This will exclude genes from being potential candidate 
to be included in the model if the change in expression on the current training data fold
is not above FCT. Note, if there are not at least NF features meeting the fold change required
threshold, the threshold will be ignored and features will be selected from the top ones sorted by p-values.    

We can explore the details recorded for each methods combination stored in the elements of modlist:
<<eval=TRUE, echo=TRUE>>=
modlist[["rma_ttest_LDA"]]
@
Note that the names of the features selected for this model which correspond to Affymetrix probesets
have an "F" suffix added to their names since LDA does not like variable neames to start with a number. 

The different combinations of methods can be ranked using the 
cross-validated AUC on the training data using:
 
<<eval=TRUE,echo=TRUE,results=verbatim>>=
trainingAUC=sort(unlist(lapply(modlist,"[[","best_AUC")),decreasing=TRUE)
cbind(trainingAUC)
@

Now the model that apears to be best using the AUC on the training data will
not necessarily be best according to the same or other statistics on the test data.
To illustrate this, we will compute various metrics such as BCM, CCEM and AUPR implemented
in the {\tt perfDSC} function for these models on the test data: 

<<eval=TRUE,echo=TRUE,results=verbatim>>=
perF=function(out){
perfDSC(pred=out$predictions,gs=gsLC)
}
testPerf=t(data.frame(lapply(modlist,perF)))
testPerf=testPerf[order(testPerf[,"AUC"],decreasing=TRUE),]
testPerf
@ 

We can also combine the predictions from several models aka "wisdom of
crowds" by using the {\tt aggregateDSC} function:

<<eval=TRUE,echo=TRUE,results=verbatim>>=
best3=names(trainingAUC)[1:3]
aggpred=aggregateDSC(modlist[best3])
#test the aggregated model on the test data
perfDSC(aggpred,gsLC)
@
In this example combining the predictions from the best 3 models (as apparent on the training data) 
leads to better prediction on the test data compared to using the single best model chosen 
according to the training performance.  

\bibliography{maPredictDSC} 
\end{document}






