%\VignetteIndexEntry{OOMPA Mahalanobis Distance}
%\VignetteKeywords{OOMPA, PCA, Mahalanobis Distance, Outliers}
%\VignetteDepends{oompaBase,ClassDiscovery}
%\VignettePackage{ClassDiscovery}
\documentclass{article}
\usepackage{graphicx}
\usepackage{hyperref}
\usepackage{cite}
\pagestyle{myheadings}
\markright{maha-test}

\setlength{\topmargin}{0in}
\setlength{\textheight}{8in}
\setlength{\textwidth}{6.5in}
\setlength{\oddsidemargin}{0in}
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\def\rcode#1{\texttt{#1}}
\def\fref#1{\textbf{Figure~\ref{#1}}}
\def\tref#1{\textbf{Table~\ref{#1}}}
\def\sref#1{\textbf{Section~\ref{#1}}}

\title{PCA, Mahalanobis Distance, and Outliers}
\author{Kevin R. Coombes}
\date{4 November 2011}

\begin{document}

<<echo=FALSE>>=
options(width=88)
options(SweaveHooks = list(fig = function() par(bg='white')))
#if (!file.exists("Figures")) dir.create("Figures")
@ 
%\SweaveOpts{prefix.string=Figures/02-AML-27plex, eps=FALSE}

\maketitle
\tableofcontents

\section{Simulated Data}

We simulate a dataset.
<<simdata>>=
set.seed(564684)
nSamples <- 30
nGenes <- 3000
dataset <- matrix(rnorm(nSamples*nGenes), ncol=nSamples, nrow=nGenes)
dimnames(dataset) <- list(paste("G", 1:nGenes, sep=''),
                          paste("S", 1:nSamples, sep=''))
@ 
Now we make two of the entries into distinct outliers.
<<liars>>=
nShift <- 300
affected <- sample(nGenes, nShift)
dataset[affected,1] <- dataset[affected,1] + rnorm(nShift, 1, 1)
dataset[affected,2] <- dataset[affected,2] + rnorm(nShift, 1, 1)
@ 

\section{PCA}
We start with a principal components analysis (PCA) of this dataset. A
plot of the samples against the first two principal components (PCs)
shows two very clear outliers (\fref{spca1}).
<<spca>>=
library(ClassDiscovery)
spca <- SamplePCA(dataset)
@ 

\begin{figure}
<<fig=TRUE,echo=FALSE>>=
plot(spca)
@ 
\caption{Principal components plot of the samples.}
\label{spca1}
\end{figure}

We want to explore the possibility of an outlier more formally. First,
we look at the cumulative amount of variance explained by the PCs:
<<pc>>=
round(cumsum(spca@variances)/sum(spca@variances), digits=2)
@ 
We see that we need $20$ components in order to explain $70\%$ of the
variation in the data.  Next, we compute the Mahalanobis distance of
each sample from the center of an $N$-dimensional principal component
space.   We apply the \texttt{mahalanobisQC} function using different
numbers of components between 
$2$ and $20$.
<<maah20>>=
maha2 <- mahalanobisQC(spca, 2)
maha5 <- mahalanobisQC(spca, 5)
maha10 <- mahalanobisQC(spca, 10)
maha20 <- mahalanobisQC(spca, 20)
myd <- data.frame(maha2, maha5, maha10, maha20)
colnames(myd) <- paste("N", rep(c(2, 5, 10, 20), each=2),
                      rep(c(".statistic", ".p.value"), 4), sep='')
@ 
The theory says that, under the null hypothesis that all samples
arise from the same multivariate normal distribution,  the distance
from the center of a $d$-dimensional PC space should follow a
chi-squared distribution with $d$ degrees of freedom. This theory lets
us compute $p$-values associated with the Mahalanobis distances for
each sample (\tref{maha}).
<<results=tex, echo=FALSE>>=
library(xtable)
xtable(myd, digits=c(0, rep(c(1, 4),4)), 
       align=paste("|l|",paste(rep("r",8), collapse=''),"|",sep=''), 
       label="maha", 
       caption=paste("Mahalanobis distance (with unadjusted p-values)",
         "of each sample from the center of",
         "N-dimensional principal component space."))
       
@ 

We see that the samples S1 and S2 are outliers, at least when we look
at the first $2$, $5$, or, $10$ components.  However, sample S2 is
not quite significant (at the $5\%$ level) when we get out to $20$
components.  This can occur when there are multiple outliers because
of the ``inflated'' variance estimates coming from the outliers
themselves. 

\clearpage
\section{A Second Round}
Now we repeat the PCA after removing the one definite outlier.  Sample
S2 still stands out as ``not like the others'' (\fref{spca2}).
<<spca>>=
reduced <- dataset[,-1]
dim(reduced)
spca <- SamplePCA(reduced)
round(cumsum(spca@variances)/sum(spca@variances), digits=2)
@ 

\begin{figure}
<<fig=TRUE,echo=FALSE>>=
plot(spca)
@ 
\caption{Principal components plot of the normal control samples,
  after omitting an extreme outlier.}
\label{spca2}
\end{figure}

And we can recompute the mahalanobis distances (\tref{maha2}).  Here
we see that even out at the level of $20$ components, this sample
remains an outlier.
<<redmaha>>=
maha20 <- mahalanobisQC(spca, 20)
@ 
<<echo=FALSE,results=tex>>=
xtable(maha20, digits=c(0, 1, 4), 
       align="|l|rr|",
       label="maha2", 
       caption=paste("Mahalanobis distance (with unadjusted p-values)",
         "of each sample from the center of",
         "20-dimensional principal component space."))
@ 

\clearpage
\section{A Final Round}
We repeat the analysis after removing one more outlier.
<<spca>>=
red2 <- reduced[,-1]
dim(red2)
spca <- SamplePCA(red2)
round(cumsum(spca@variances)/sum(spca@variances), digits=2)
@ 

\begin{figure}
<<fig=TRUE,echo=FALSE>>=
plot(spca)
@ 
\caption{Principal components plot of the normal control samples,
  after omitting an extreme outlier.}
\label{spca3}
\end{figure}

And we can recompute the mahalanobis distances (\tref{maha3}).  At
this point, there are no outliers.
<<redmaha>>=
maha20 <- mahalanobisQC(spca, 20)
@ 
<<echo=FALSE,results=tex>>=
xtable(maha20, digits=c(0, 1, 4), 
       align="|l|rr|",
       label="maha3", 
       caption=paste("Mahalanobis distance (with unadjusted p-values)",
         "of each sample from the center of",
         "20-dimensional principal component space."))
@ 



\section{Appendix}

This analysis was performed in the following directory:
<<getwd>>=
getwd()
@ 
This analysis was performed in the following software environment:
<<si>>=
sessionInfo()
@ 

\end{document}

