%\VignetteIndexEntry{geneClassifiers introduction}
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\usepackage{hyperref}
\begin{document} 
\title{Package: geneClassifiers (Version 1.0.0)}
\author{R.Kuiper}
\date{October 27, 2016}

\maketitle

<<echo=FALSE>>=
library(geneClassifiers)
@

Combining gene expression profiling data with survival data has led to the 
development of robust outcome predictors (gene classifiers).This package 
provides a method for running gene classifiers generating patient specific 
predictive outcomes. This package is intended to support and enable 
research. The workflow is illustrated in Figure \ref{Fig:workflow}. The raw gene 
expression data obtained by microarray experiments is normalized using 
existing techniques (independent of this package). The choice of 
normalization method is dictated by the classifier. Some classifiers were 
developed using MAS5.0. In that case, the data to be classified should be 
normalized using MAS5.0. Normalization is followed by preprocessing 
(this package) and generating scores/classifications (this package). 
This package is suitable only for datasets of at least 20 patients.
\begin{figure}[h]
\centering
\includegraphics[trim=0cm 0cm 0cm 0cm, clip=true, scale=0.5]{Figures/WorkFlow1.pdf}
\caption{The workflow of the geneClassifiers package. The raw gene expression data 
is normalized. This normalized data is used as input in the geneClassifiers package. 
The processes with their relevant functions are shown. \label{Fig:workflow}}
\end{figure}

\section{Classifiers}
\label{sec:Classifiers}
The currently implemented list of classifiers can be obtained with the command:
<<>>=
showClassifierList()
@
To find more information on a specific classifier (e.g. EMC92),the classifier 
parameters can be obtained by
<<>>=
EMC92Classifier<-getClassifier("EMC92")
EMC92Classifier

HM19Classifier<-getClassifier("HM19")
HM19Classifier
@

This is an object of class 'ClassifierParameters' which stores classifier 
related information, such as probe-sets used and their weights, means, 
standard deviations and covariance structure as observed in the classifiers' 
training data, and the description of the procedure on how to preprocess 
new data prior to application of the classifier.

Further information can be obtained from this object e.g. obtaining the weights 
used in a classifier:

<<>>=
getWeights(EMC92Classifier)[1:10]
@
or the decision boundaries used to decide which class a sample score belongs 
to
<<>>=
getDecisionBoundaries(HM19Classifier)
@
or the 'eventChain' which gives information on preprocessing:
<<>>=
getEventChain(EMC92Classifier)
@
\section{Data to be classified}
\label{sec:data}
The input data for the 'geneClassifiers' package is a Bioconductor ExpressionSet 
which has been prenormalized using existing methods such as MAS5.0 or GCRMA. 
For more information on these methods see the Bioconductor 'affy' package. The 
'geneClassifiers' package contains an example dataset of MAS5.0 normalized 
(target value = 500) gene expression data of 25 multiple myeloma patients from 
the HOVON65/GMMG-HD4 trial (Pieter Sonneveld et al., J Clin Oncol, 2012)
<<>>=
library(Biobase)
data(exampleMAS5)
class(exampleMAS5) #an object of class ExpressionSet
dim(exampleMAS5) 
preproc(experimentData(exampleMAS5))
@

To import this data set into the 'geneClassifiers' package, the setNormalization 
function is used:
<<>>=
fixedData <- setNormalizationMethod( exampleMAS5, method="MAS5.0", targetValue = 500 )
fixedData
@


Nb the targetValue = 500 is only required in the example (see below).

To get reliable results in the classification, the function depends on 
unmanipulated output from the normalization methods, i.e. read in CEL 
files into affy functions, obtain ExpressionSet, and use this set (without
modification) for obtaining classifier scores. The function can detect 
deviations such as subsets of data sets or log transformed data, but 
detection of deviations is not guaranteed. When providing an ExpressionSet
with all probe-sets still included, the 'targetValue=500' argument is not 
necessary because the function is able to extract the value from the data.
In the example the number of probe-sets was reduced due to space 
considerations, the MAS5.0 target value cannot be obtained from the data
so that the argument has to be provided. See '?setNormalizationMethod' 
for more details. 


\section{Performing classifications}
To perform the classification using a classifier described in section
\ref{sec:Classifiers} on the data described in section \ref{sec:data}, 
the 'runClassifier' function is called using both arguments:

<<>>=
resultsEMC92  <- runClassifier( "EMC92" , fixedData )
resultsUAMS70 <- runClassifier( "UAMS70", fixedData )

resultsEMC92
resultsUAMS70
@

The scores and classifications can be extracted using the 'getScores' and 
'getClassifications' function

<<>>=
data.frame(
    "score_EMC92"  = getScores( resultsEMC92 ),
    "class_EMC92"  = getClassifications( resultsEMC92 ),
    "score_UAMS70" = getScores( resultsUAMS70 ),
    "class_UAMS70" = getClassifications( resultsUAMS70 )
)
@

\section{Caution: non standard situations}

The geneClassifiers package performs a batch correction by applying a linear
transformation of the probe-set means and standard deviations to the values
observed in the classifiers' training set. In order to accurately do this 
the data must contain a sufficient number of samples (n>=20) to estimate the
means and standard deviations. If less samples are available, the 
'runClassifier' function will give a warning and suggest to consider setting
'do.batchcorrection = FALSE'. Please note this will most likely result in 
invalid classifications (or certainly different classifications). 

Besides the requirements of a matching normalization method between data and
classifier and sufficient samples, the assumption is that the probe-sets 
needed for classification are present in the data. If this is not true, simply 
ignoring the missing probe-set may heavily bias the results. Therefore, when 
detecting missing probe-sets, the 'run-classifier' function will give an error 
message and suggest to consider using the argument 'allow.reweighted = TRUE'. 
This will reweight the weightings for the probe-sets which are present, based 
on the covariance structure of the classifiers' trainings data. See the 
vignette 'MissingCovariates' for more information. Please note this is not 
how the classifiers are intended and consequentially will result in different 
classifications. 

<<fig=TRUE>>=
resultsEMC92.reWeighted <- runClassifier( 
    "EMC92" , 
    fixedData[1:70,] ,
    allow.reweighted=TRUE
)

resultsEMC92.reWeighted

plot(
    x = getScores(resultsEMC92),
    y = getScores(resultsEMC92.reWeighted), 
    xlab = "complete", 
    ylab = "reweighted", 
    main = "EMC92 scores",
    pch = 21,
    bg  ='black'
)
lines(c(-10,10),c(-10,10),col=2,lty=2)
abline(
    v = getDecisionBoundaries( getClassifier( resultsEMC92           )),
    h = getDecisionBoundaries( getClassifier( resultsEMC92.reWeighted)),
    col='red'
)
@

<<>>=
sessionInfo()
@
\end{document}

