r-bioc-genefilter 1.60.0-1 / usr / lib / R / site-library / genefilter / doc / independent_filtering.Rnw

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\documentclass[10pt]{article}

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\usepackage{xstring}
\newcommand{\thetitle}{Diagnostics for independent filtering: choosing filter statistic and cutoff}

\title{\textsf{\textbf{\thetitle}}}
\author{Wolfgang Huber\\[1em]European Molecular Biology Laboratory (EMBL)}

% The following command makes use of SVN's 'Date' keyword substitution
% To activate this, I used: svn propset svn:keywords Date independent_filtering.Rnw
\date{\Rpackage{genefilter} version \Sexpr{packageDescription("genefilter")$Version}  (Last revision \StrMid{$Date$}{8}{18})}


\begin{document}

<<options,results='hide',echo=FALSE>>=
options(digits=3, width=100)
library("pasilla") # make sure this is installed, since we need it in the next section
@

% Make title
\maketitle
\tableofcontents
\vspace{.25in}

\begin{abstract}
\noindent This vignette illustrates diagnostics that are intended to help with
\begin{itemize}
\item the choice of filter criterion and
\item the choice of filter cutoff
\end{itemize}
in independent filtering~\cite{Bourgon:2010:PNAS}. The package 
\Biocpkg{genefilter} provides functions that might be convenient for this purpose.
\end{abstract}

%-----------------------------------------------------------
\section{Introduction}
%-----------------------------------------------------------
Multiple testing approaches, with thousands of tests, are often used
in analyses of genome-scale data. For instance, in analyses of
differential gene expression based on RNA-Seq or microarray data, a
common approach is to apply a statistical test, one by one, to each of
thousands of genes, with the aim of identifying those genes that have
evidence for a statistical association of their expression
measurements with the experimental covariate(s) of interest.  Another
instance is differential binding detection from ChIP-Seq data.  The
idea of \emph{independent filtering} is to filter out those tests from
the procedure that have no, or little chance of showing significant
evidence, without even looking at their test statistic. Typically,
this results in increased detection power at the same experiment-wide
type I error, as measured in terms of the false discovery rate.  A
good choice for a filtering criterion is one that
\begin{enumerate}
  \item\label{it:indp} is statistically independent from the test statistic
    under the null hypothesis,
  \item\label{it:corr} is correlated with the test statistic under the
    alternative, and
  \item\label{it:joint} does not notably change the dependence
    structure --if there is any-- of the joint test statistics
    (including those corresponding to true nulls and to true
    alternatives).
\end{enumerate}
The benefit from filtering relies on property~\ref{it:corr}, and I will explore that
further in Section~\ref{sec:qual}. The statistical validity of filtering relies on
properties \ref{it:indp} and \ref{it:joint}.  For many practically useful combinations of
filter criteria with test statistics, property~\ref{it:indp} is easy to prove (e.\,g., through
Basu's theorem).  Property~\ref{it:joint} is more complicated, but rarely
presents a problem in practice: if, for the multiple testing procedure that is being used,
the correlation structure of the tests was acceptable without filtering, the filtering should 
not change that. Please see~\cite{Bourgon:2010:PNAS} for further discussion on the
mathematical and conceptual background.

%-----------------------------------------------------------
\section{Example data set}
%-----------------------------------------------------------
For illustration, let us use the \Robject{pasillaGenes} dataset from the
Bioconductor package \Rpackage{pasilla}; this is an RNA-Seq dataset
from which we extract gene-level read counts for two replicate samples
the were measured for each of two biological conditions: normally
growing cells and cells treated with dsRNA against the \emph{Pasilla}
mRNA, which led to RNAi interference (RNAi) mediated knockdown of the
Pasilla gene product.
%
<<libraries,results='hide'>>=
library("pasilla")
data("pasillaGenes")
@
%
We perform a standard analysis with \Rpackage{DESeq} to look for genes
that are differentially expressed between the normal and
Pasilla-knockdown conditions, indicated by the factor variable
\Robject{condition}. In the generalized linear model (GLM) analysis,
we adjust for an additional experimental covariate \Robject{type},
which is however not of interest for the differential expression. For
more details, please see the vignette of the \Rpackage{DESeq} package.
%
<<DESeq1,results='hide'>>=
library("DESeq")
<<DESeq2,cache=TRUE,results='hide'>>=
cds  = estimateSizeFactors( pasillaGenes )
cds  = estimateDispersions( cds )
fit1 = fitNbinomGLMs( cds, count ~ type + condition )
fit0 = fitNbinomGLMs( cds, count ~ type  )
<<DESeq3,cache=TRUE>>=
res = data.frame(
filterstat = rowMeans(counts(cds)),
pvalue    = nbinomGLMTest( fit1, fit0 ),
row.names = featureNames(cds) )
@
%
The details of the anove analysis are not important for the purpose of
this vignette, the essential output is contained in the columns of the
dataframe \Robject{res}:
\begin{itemize}
  \item \texttt{filterstat}: the filter statistic, here the average
    number of counts per gene across all samples, irrespective of
    sample annoation,
  \item \texttt{pvalue}: the test $p$-values,
\end{itemize}
Each row of the dataframe corresponds to one gene:
<<headres>>=
dim(res)
head(res)
@

%--------------------------------------------------
\section{Qualitative assessment of the filter statistic}\label{sec:qual}
%--------------------------------------------------
<<pass,echo=FALSE,cache=TRUE>>=
theta = 0.4
pass = with(res, filterstat > quantile(filterstat, theta))
@
%
First, consider Figure~\ref{figscatterindepfilt}, which shows that
among the approximately \Sexpr{100*theta}\% of genes with lowest overall counts, \Robject{filterstat},
there are essentially none that achieved an (unadjusted) $p$-value less than
\Sexpr{signif(quantile(res$pvalue[!pass], 0.0001, na.rm=TRUE), 1)}
(this corresponds to about \Sexpr{signif(-log10(quantile(res$pvalue[!pass], 0.0001, na.rm=TRUE)), 2)} on the $-\log_{10}$-scale).
%
<<figscatterindepfilt>>=
with(res,
  plot(rank(filterstat)/length(filterstat), -log10(pvalue), pch=16, cex=0.45))
@
<<figecdffilt>>=
trsf = function(n) log10(n+1)
plot(ecdf(trsf(res$filterstat)), xlab=body(trsf), main="")
@
\begin{figure}[ht]
\centering
\includegraphics[width=.49\textwidth]{figure/figscatterindepfilt-1}
\includegraphics[width=.49\textwidth]{figure/figecdffilt-1}
\caption{Left: scatterplot of the rank (scaled to $[0,1]$) of the
  filter criterion \Robject{filterstat} ($x$-axis) versus the negative
  logarithm of the test \Robject{pvalue} ($y$-axis). Right: the
  empirical cumulative distribution function (ECDF) shows the
  relationships between the values of \Robject{filterstat} and its
  quantiles.}
\label{figscatterindepfilt}
\end{figure}
%
This means that by dropping the 40\% genes with lowest \Robject{filterstat},
we do not loose anything substantial from our subsequent
results.

For comparison, suppose you had chosen a less useful filter statistic,
say, the gene identifiers interpreted as a decimal number. The
analogous scatterplot to that of Figure~\ref{figscatterindepfilt} is
shown in Figure~\ref{figbadfilter}.
%
<<badfilter1,cache=TRUE>>=
badfilter = as.numeric(gsub("[+]*FBgn", "", rownames(res)))
@
<<badfilter2,echo=FALSE>>=
stopifnot(!any(is.na(badfilter)))
@
<<figbadfilter>>=
plot(rank(badfilter)/length(badfilter), -log10(res$pvalue), pch=16, cex=0.45)
@
\begin{figure}[ht]
\centering
\includegraphics[width=.49\textwidth]{figure/figbadfilter-1}
\caption{Scatterplot analogous to Figure~\ref{figscatterindepfilt}, but with \Robject{badfilter}.}
\label{figbadfilter}
\end{figure}

%--------------------------------------------------
\section{How to choose the filter statistic and the cutoff?}\label{sec:indepfilterchoose}
%--------------------------------------------------
The \texttt{filtered\_p} function in the \Rpackage{genefilter} package
calculates adjusted $p$-values over a range of possible filtering
thresholds. Here, we call this function on our results from above and
compute adjusted $p$-values using the method of Benjamini and Hochberg (BH)
for a range of different filter cutoffs.
%
\begin{figure}[tb]
\begin{center}
\includegraphics[width=0.49\textwidth]{figure/figrejection-1}
\includegraphics[width=0.49\textwidth]{figure/fignumreject-1}
\caption{Left panel: the plot shows the number of rejections (i.\,e.\ genes detected as
  differentially expressed) as a function of the FDR threshold
  ($x$-axis) and the filtering cutoff $\theta$ (line colours,
  specified as quantiles of the distribution of the
  filter statistic). The plot is produced by the \texttt{rejection\_plot}
  function. Note that the lines for $\theta=0\%$ and
  $10\%$ are overplotted by the line for $\theta=20\%$, since for the
  data shown here, these quantiles correspond all to the same set of
  filtered genes (cf.~Figure~\ref{figscatterindepfilt}). Right panel:
  the number of rejections at FDR=10\% as a function of
  $\theta$.}
\label{figrej}
\end{center}
\end{figure}
%
<<genefilter,results='hide'>>=
library("genefilter")
<<pBH1,cache=TRUE>>=
theta = seq(from=0, to=0.5, by=0.1)
pBH = filtered_p(filter=res$filterstat, test=res$pvalue, theta=theta, method="BH")
<<pBH2>>=
head(pBH)
@
%
The rows of this matrix correspond to the genes (i.\,e., the rows of \Robject{res}) and
the columns to the BH-adjusted $p$-values for the different possible
choices of cutoff \Robject{theta}. A value of \Robject{NA} indicates
that the gene was filtered out at the corresponding filter cutoff.
The \Rfunction{rejection\_plot} function takes such a matrix and
shows how rejection count ($R$) relates to the choice of cutoff for the
$p$-values. For these data, over a reasonable range of FDR cutoffs, increased
filtering corresponds to increased rejections.
%
<<figrejection,fig.width=5.5,fig.height=5.5>>=
rejection_plot(pBH, at="sample",
               xlim=c(0, 0.5), ylim=c(0, 2000),
               xlab="FDR cutoff (Benjamini & Hochberg adjusted p-value)", main="")
@
The plot is shown in the left panel of Figure~\ref{figrej}.


%------------------------------------------------------------
\subsection{Choice of filtering cutoff}\label{choose:cutoff}
%------------------------------------------------------------
If we select a fixed cutoff for the adjusted $p$-values, we can also look more closely at
the relationship between the fraction of null hypotheses filtered and the total number of
discoveries. The \texttt{filtered\_R} function wraps \texttt{filtered\_p} and just returns
rejection counts. It requires you to choose a particular $p$-value cutoff, specified
through the argument \Robject{alpha}.
%
<<filtered_R1,cache=TRUE>>=
theta = seq(from=0, to=0.8, by=0.02)
rejBH = filtered_R(alpha=0.1, filter=res$filterstat, test=res$pvalue, theta=theta, method="BH")
@

Because overfiltering (or use of a filter which is inappropriate for the
application domain) discards both false and true null hypotheses, very large
values of $\theta$ reduce power in this example:

<<fignumreject,fig.width=5.5,fig.height=5.5>>=
plot(theta, rejBH, type="l",
     xlab=expression(theta), ylab="number of rejections")
@
The plot is shown in the right panel of Figure~\ref{figrej}.

%------------------------------------------------------------
\subsection{Choice of filtering statistic}\label{choose:filterstat}
%------------------------------------------------------------
We can use the analysis of the previous section~\ref{choose:cutoff} also to inform
ourselves about different possible choices of filter statistic. We construct a dataframe
with a number of different choices.

<<differentstats,cache=TRUE>>=
filterChoices = data.frame(
  `mean`   = res$filterstat,
  `geneID` = badfilter,
  `min`    = rowMin(counts(cds)),
  `max`    = rowMax(counts(cds)),
  `sd`     = rowSds(counts(cds))
)
rejChoices = sapply(filterChoices, function(f)
  filtered_R(alpha=0.1, filter=f, test=res$pvalue, theta=theta, method="BH"))
<<colours,results='hide'>>=
library("RColorBrewer")
myColours = brewer.pal(ncol(filterChoices), "Set1")
<<figdifferentstats,fig.width=5.5,fig.height=5.5>>=
matplot(theta, rejChoices, type="l", lty=1, col=myColours, lwd=2,
        xlab=expression(theta), ylab="number of rejections")
legend("bottomleft", legend=colnames(filterChoices), fill=myColours)
@
%
The result is shown in Figure~\ref{figdifferentstats}. It indicates that for the data at
hand, \Robject{mean}, \Robject{max} and \Robject{sd} provide similar performance, whereas
the other choices are less effective.
\begin{figure}[tb]
\begin{center}
\includegraphics[width=0.49\textwidth]{figure/figdifferentstats-1}
\caption{The number of rejections at FDR=10\% as a function of
  $\theta$ (analogous to the right panel in Figure~\ref{figrej}) for a number of different choices of the filter statistic.}
\label{figdifferentstats}
\end{center}
\end{figure}

%--------------------------------------------------
\section{Some more plots pertinent to multiple testing}
%--------------------------------------------------
%--------------------------------------------------
\subsection{Joint distribution of filter statistic and  $p$-values}\label{sec:pvalhist}
%--------------------------------------------------
The left panel of Figure~\ref{figscatterindepfilt} shows the joint distribution of filter
statistic and $p$-values. An alternative, perhaps simpler view is provided by the
$p$-value histograms in Figure~\ref{fighistindepfilt}.  It shows how the filtering ameliorates the multiple testing
problem -- and thus the severity of a multiple testing adjustment -- by removing a
background set of hypotheses whose $p$-values are distributed more or less uniformly in
$[0,1]$.
<<histindepfilt, fig.width=7, fig.height=5>>=
h1 = hist(res$pvalue[!pass], breaks=50, plot=FALSE)
h2 = hist(res$pvalue[pass], breaks=50, plot=FALSE)
colori <- c(`do not pass`="khaki", `pass`="powderblue")
<<fighistindepfilt, dev="pdf">>=
barplot(height = rbind(h1$counts, h2$counts), beside = FALSE,
        col = colori, space = 0, main = "", ylab="frequency")
text(x = c(0, length(h1$counts)), y = 0, label = paste(c(0,1)),
     adj = c(0.5,1.7), xpd=NA)
legend("topright", fill=rev(colori), legend=rev(names(colori)))
@
\begin{figure}[ht]
\centering
\includegraphics[width=.5\textwidth]{figure/fighistindepfilt-1}
\caption{Histogram of $p$-values for all tests.
  The area shaded in blue indicates the subset of those that pass the filtering,
  the area in khaki those that do not pass.}
\label{fighistindepfilt}
\end{figure}

%-----------------------------------------------------
\subsection{Illustration of the Benjamini-Hochberg method}
%------------------------------------------------------
The Benjamini-Hochberg multiple testing adjustment
procedure \cite{BH:1995} has a simple graphical illustration, which is
produced in the following code chunk. Its result is shown in the left
panel of Figure \ref{figmulttest}.
%
<<sortP, cache=TRUE>>=
resFilt = res[pass,]
orderInPlot = order(resFilt$pvalue)
showInPlot = (resFilt$pvalue[orderInPlot] <= 0.06)
alpha = 0.1
<<sortedP, fig.width=4.5, fig.height=4.5>>=
plot(seq(along=which(showInPlot)), resFilt$pvalue[orderInPlot][showInPlot],
     pch=".", xlab = expression(rank(p[i])), ylab=expression(p[i]))
abline(a=0, b=alpha/length(resFilt$pvalue), col="red3", lwd=2)
@
<<doBH, echo=FALSE, results='hide'>>=
whichBH = which(resFilt$pvalue[orderInPlot] <= alpha*seq(along=resFilt$pvalue)/length(resFilt$pvalue))
## Test some assertions:
## - whichBH is a contiguous set of integers from 1 to length(whichBH)
## - the genes selected by this graphical method coincide with those
##   from p.adjust (i.e. padjFilt)
stopifnot(length(whichBH)>0,
          identical(whichBH, seq(along=whichBH)),
          resFilt$FDR[orderInPlot][ whichBH] <= alpha,
          resFilt$FDR[orderInPlot][-whichBH]  > alpha)
@
%
%-----------------------------------------------------
\subsection{Schweder and Spj\o{}tvoll plot}
%------------------------------------------------------
Schweder and Spj\o{}tvoll \cite{SchwederSpjotvoll1982} suggested a diagnostic plot
of the observed $p$-values which permits estimation of the fraction of true null
hypotheses. For a series of hypothesis tests $H_1, \ldots, H_m$ with $p$-values
$p_i$, they suggested plotting
%
\begin{equation}
  \left( 1-p_i, N(p_i) \right) \mbox{ for } i \in 1, \ldots, m,
\end{equation}
%
where $N(p)$ is the number of $p$-values greater than $p$. An application of
this diagnostic plot to \Robject{resFilt\$pvalue} is shown in the right panel of
Figure \ref{figmulttest}.
When all null hypotheses are true, the $p$-values are each uniformly distributed
in $[0,1]$, Consequently, the cumulative distribution function of $(p_1, \ldots,
p_m)$ is expected to be close to the line $F(t)=t$. By symmetry, the same
applies to $(1 - p_1, \ldots, 1 - p_m)$.
When (without loss of generality) the first $m_0$ null hypotheses are true and
the other $m-m_0$ are false, the cumulative distribution function of $(1-p_1,
\ldots, 1-p_{m_0})$ is again expected to be close to the line $F_0(t)=t$. The
cumulative distribution function of $(1-p_{m_0+1}, \ldots, 1-p_{m})$, on the
other hand, is expected to be close to a function $F_1(t)$ which stays below
$F_0$ but shows a steep increase towards 1 as $t$ approaches $1$.
In practice, we do not know which of the null hypotheses are true, so we can
only observe a mixture whose cumulative distribution function is expected to be
close to
%
\begin{equation}
  F(t) = \frac{m_0}{m} F_0(t) + \frac{m-m_0}{m} F_1(t).
\end{equation}
%
Such a situation is shown in the right panel of
Figure \ref{figmulttest}. If
$F_1(t)/F_0(t)$ is small for small $t$, then the mixture fraction
$\frac{m_0}{m}$ can be estimated by fitting a line to the left-hand portion of
the plot, and then noting its height on the right. Such a fit is shown by the
red line in the right panel of Figure \ref{figmulttest}.
%
<<SchwSpjot, echo=FALSE, results='hide'>>=
j  = round(length(resFilt$pvalue)*c(1, .66))
px = (1-resFilt$pvalue[orderInPlot[j]])
py = ((length(resFilt$pvalue)-1):0)[j]
slope = diff(py)/diff(px)
@
<<SchwederSpjotvoll, fig.width=4.5, fig.height=4.5>>=
plot(1-resFilt$pvalue[orderInPlot],
     (length(resFilt$pvalue)-1):0, pch=".", xaxs="i", yaxs="i",
     xlab=expression(1-p[i]), ylab=expression(N(p[i])))
abline(a=0, slope, col="red3", lwd=2)
abline(h=slope)
text(x=0, y=slope, labels=paste(round(slope)), adj=c(-0.1, 1.3))
@

\begin{figure}[ht]
\centering
\includegraphics[width=.49\textwidth]{figure/sortedP-1}
\includegraphics[width=.49\textwidth]{figure/SchwederSpjotvoll-1}
\caption{\emph{Left:} illustration of the Benjamini-Hochberg multiple testing
  adjustment procedure \cite{BH:1995}.  The black line shows the
  $p$-values ($y$-axis) versus their rank ($x$-axis), starting with
  the smallest $p$-value from the left, then the second smallest, and
  so on. Only the first \Sexpr{sum(showInPlot)} $p$-values are shown.
  The red line is a straight line with slope $\alpha/n$, where
  $n=\Sexpr{length(resFilt[["pvalue"]])}$ is the number of tests, and
  $\alpha=\Sexpr{alpha}$ is a target false discovery rate (FDR).  FDR
  is controlled at the value $\alpha$ if the genes are selected
  that lie to the left of the rightmost intersection between the red and black
  lines: here, this results in \Sexpr{length(whichBH)} genes.
  \emph{Right:} Schweder and Spj\o{}tvoll plot, as described in the text.}
\label{figmulttest}
\end{figure}


%--------------------------------------------------
\section*{Session information}
%--------------------------------------------------
<<sessionInfo, results='asis', echo=FALSE>>=
si = as.character( toLatex( sessionInfo() ) )
cat( si[ -grep( "Locale", si ) ], sep = "\n" )
@


\bibliography{library}

\end{document}