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\begin{document}
\title{A correction for the LPE statistical test}
\author{
        Carl Murie
        \email{<carl.murie@mcgill.ca>}, \\
        Robert Nadon
        \email{<robert.nadon@mcgill.ca>}
        }

\maketitle
\tableofcontents


\section{Introduction}

 LPEadj is a correction for the LPE statistical test \cite{Jain:2003} which
 consists of two additions to the LPE method.  The LPE package documentation
 is still correct with the exception of 
 the two additions listed below and should be consulted for more information
 on the LPE method. \\
\\
 The correction is in two parts.  See \cite{Mur:2008} for more information on
 the correction. 

 \begin{enumerate}
 \item The LPEadj method discontinues the LPE practice of setting all
   variances below the maximum variance in the ordered distribution of
   variances to the maximum variance.  In certain cases this practice can set
   many variances to the maximum and lower the performance of
   this algorithm.  If the assumption that there are only a few low variances
   to be adjusted is correct then it may be safe to use this procedure. This
   option is controlled by the doMax parameter (default is FALSE).
 \item  The LPEadj method replaces the $\pi/2$ adjustment of the variance
 with an empirically estimated adjustment based on the sample size of the
 data.   The empirical adjustment values have been estimated for replicate
 sizes up to 10. This option is controlled by the doAdj parameter (default is
 TRUE). 
 \end{enumerate}

 The top level function LPEadj executes the first and second
 step by default.  This is the recommended manner in which LPEadj should be
 run.  One can use steps 1 and 2 independently if desired.   \\ 
 
\section{LPE variance adjustment}

The LPE method pools variance estimates of genes with similar
intensities in order to gain an improved error estimate and increased
degrees of freedom.  A calibration 
curve of variance versus mean intensity is 
generated for each group and the gene specific median intensity is
used to obtain the gene's variance estimate from the calibration
curve.  It
has been shown that the sampling variability of the median is
proportionally higher (by $\pi/2$) than that of the mean
\cite{Moo:1974}.   Accordingly, a multiplicative adjustment of
$\pi/2$ is applied to the variance estimate obtained from the
calibration curve for the purpose of statistical testing. \\
\\

The LPE z-statistic is as follows:

\begin{equation}
z = \frac{Med_1 - Med_2}{\sigma_{pool}}
\end{equation}

\noindent
where

\begin{equation}
\sigma^2_{pool} = \frac{\pi}{2}(\frac{\sigma^2_1(Med_1)}{n_1} +
\frac{\sigma^2_2(Med_2)}{n_2})
\end{equation}

$\sigma^2_i(Med_i)$ are the variances derived from the calibration
curve using the median of the gene intensites for a particular group.
$n$ is the number of replicates in the groups (assuming equal sample
size).  The associated probability of the z-statistic
under the null hypothesis is calculated by reference to the standard
normal distribution.
\par


\begin{figure}[tbp]
\includegraphics[]{fig//MeanMedianVar3.jpg}
\caption{The ratio of the variance of sampling medians over sampling means
  across a range of sample sizes.
  The Sampling variance of mean line is the variance of taking the mean of a
  random sample from a standard normal distribution.    This line corresponds
  to the Central  Limit Theorem,
  which states that the sample variance of a distribution of means
  is $\sigma^2/N$.  The Sampling variance of median line
  is the variance of taking the median from the same random samples.
  The Sampling variance of median/Sampling variance of mean line is the
  variance of the median divided by 
  the variance of the mean at each sample size. The sampling was
  repeated 1000 times for each sample size (ranging from 3 to 1000).}
\label{fig:Ratio}
\end{figure}

The Mood \cite{Moo:1974} proof shows that with normal data the ratio of the
squared standard error of the median relative to that of the mean is
asymptotically $\pi/2$.
Figure \ref{fig:Ratio} shows that the ratio
converges to $\pi/2$ when the sample size is 
large, around 100,  but is less than $\pi/2$ when the sample sizes are small, 
from three to ten.  The ratio of variances at small sample sizes also 
oscillates lower to higher depending on whether the sample size is even or
odd.  This fluctutation 
is due to the difference in obtaining the median with even and
odd sample sizes.  The middle value of the ordered
distribution is used as the median with odd sample sizes while the mean of
the two middle values of the ordered distribution is used with even sample
sizes.   There is higher variability when taking the middle 
value of a distribution (with odd number of samples) than taking the
average of the two middle values (with even number of samples).  




\section{Modification of LPE Method}

The use of an empirically estimated variance ratio adjustment, $c_i$, based
on sample size can correct the bias caused by the $\pi/2$
adjustment.  The $\pi/2$ term in Equation 1 is replaced by the
empirically generated ratio of the variance of sampling a median over
the variance of sampling a mean.  Equation 1 then becomes:  

\begin{equation}
\sigma^2_{pool} = c_1\frac{\sigma^2_1(Med_1)}{n} +
c_2\frac{\sigma^2_2(Med_2)}{n}
\end{equation}
 

The parameters, $c_1$ and $c_2$, are the ratio of variances of sampling the
median and mean based on the number of replicates for each group.   $c_1$ and
$c_2$ are the adjust1 and adjust2 variables in the calculateLpeAdj function.

\begin{figure}[tbp]
\centering
  \includegraphics[]{fig//FPRPvalHist10Reps4.jpg}
\caption{(a) False positive rates for the LPE and adjusted LPE methods using
  simulated data with no differentially expressed genes evaluated at p $\le$ .05
  threshold.  The LPE showed variable and low false positive rates.  In contrast, the adjusted LPE showed
  appropriate false positive rate for all sample sizes. (b) The adjusted LPE,
  but not the LPE, shows the theoretically expected uniform p-value
  distribution.
  Each data set had 10000 genes with each gene's replicate
  intensity drawn from a $N(\mu, 0.1)$ distribution.  $\mu$ was drawn from a
  $N(7,1)$ distribution. } 
\label{fig:FPR}
\end{figure}

 

Figure \ref{fig:FPR} shows that the LPE test has a lower than expected false
positive rate (FPR) which 
fluctuates between even and odd sample sizes (average FPR with odd and even
samples sizes is 0.030 and 0.022 respectively)  in a similar manner as the
ratio of variances in Figure \ref{fig:Ratio}.  The LPE method also shows a 
non-uniform p-value distribution with fewer than expected small p-values.
The $\pi/2$ adjustment increases the variance by an overly large proportion
and causes the LPE test statistics to be smaller than they should be and
skews the p-value distribution leftward. 
In contrast, the adjusted LPE test produced theoretically expected values.
\par

\begin{figure}[tbp]
\centering
\includegraphics[]{fig//HGU95GreyScale1.jpg}
\caption{P-value histograms and boxplots of FPR, TPR, and pAUC from the
  LPE and adjusted LPE methods applied to the HGU95 latin square data set. The data were normalized using
  six different normalization methods (labeled by row). \vspace{-5mm}}
\label{fig:Power}
\end{figure}

Figure \ref{fig:Power} summarizes the results of the LPE and adjusted LPE
methods applied to the HGU95 Affymetrix spike-in data set (www.affymetrx.com). The HGU95 data is
based on a 14 x 14 Latin Square design of ``spiked-in'' 
transcripts (14 concentrations per microarray chip x 14 groups) with
three replicates for each group.  The
concentrations for the ``spiked-in'' transcripts were doubled for
each consecutive group (0 and 0.25 to 1024 pM inclusive).  To assess the
performance of the statistical tests we used the FPR, the true positive rate
(TPR, which is the proportion of transcipts correctly identified as being
differentially expressed), and the  
partial area under the curve (pAUC, which measures the area
under a Receiver Operator Characteristic curve (ROC) below a false positive
cutoff of 0.05). The pAUC has a value between 0 (worst performance) and 1 
(perfect performance).
\par


\section{Example}

The easiest way to apply the LPEadj statistical test is to use the LPEadj
function. Setting doMax to false (the default value) stops the LPE method
from setting the variances of low intensity genes to the maximum variance.
Setting doAdj to true (the default value) makes LPE use a variance adjustment
based on the number of replicates of each group rather than $\pi/2$.  The
code will print the values used for the variance adjustment.  The variance
adjustment values for replicate sizes up to 10 are precalculated 
within the lpeAdj function.  For example if 
there are two groups with three replicates each the following line will be
printed. \\ \\



<<echo=TRUE, eval=TRUE>>=
 # Loading the library and null dataset (two groups with three
 # replicates each).  
 library(LPEadj)
 dat <- matrix(rnorm(6000), ncol=6)
 
 # Applying LPE
 lpe.result <- lpeAdj(dat, labels=c(0,0,0,1,1,1), doMax=FALSE, doAdj=TRUE)
@ 

If you want more control over low level variables you can call
calculateLpeAdj directly.  Note that in this case the results of this call
are the same as the previous call to lpeAdj because the variance adjustment
values are identical (the variance adjustment values defined in ADJ.VALUES is
the same as the variance adjustment values defined in lpeAdj).

<<echo=TRUE, eval=TRUE>>=
# Loading the library and null dataset (two groups with three
 # replicates each)
 library(LPEadj)
 dat <- matrix(rnorm(6000), ncol=6)

 ADJ.VALUES <- c(1, 1, 1.34585905516761 ,1.19363228146169 ,1.436849413109
                  ,1.289652132873 ,1.47658053092781 ,1.34382984852146
                  ,1.49972130857404, 1.3835405678718)
            
 # calculate base line error distributions
 var1 <- adjBaseOlig.error(dat[,1:3], setMax1=FALSE, q=.05)
 var2 <- adjBaseOlig.error(dat[,4:6], setMax1=FALSE, q=.05)

 # The correct variance adjustments can be fetched using the replicate 
 # number for each group as in index for the ADJ.VALUES vector.
 # eg: ADJ.VALUES[n] if there are n replicates in a group 
 results <- calculateLpeAdj(dat[,1:3],dat[,4:6],var1,var2,
                  probe.set.name=c(1:1000), adjust1=ADJ.VALUES[3],
                  adjust2=ADJ.VALUES[3])
@ 

\begin{thebibliography}{10}
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\bibitem{Jain:2003}
Jain et.\ al.
\newblock {Local-pooled-error test for identifying 
differentially expressed genes with a small number of replicated
microarrays},
\newblock {\em Bioinformatics}, 2003, Vol 19, No. 15,  pp: 1945-1951.


\bibitem{Moo:1974}
   Mood et.\ al.
\newblock {Introduction to the theory of statistics}
\newblock{Mcgraw-Hill, New York, 3rd Ed, 1974}

\bibitem{Mur:2008}
Murie et.\ al.
\newblock {A correction for estimating error when using the Local Pooled Error
  statistical test},
\newblock {\em Bioinformatics}, in press.

\end{thebibliography}

\end{document}





