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\documentclass[11pt]{article}


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\usepackage[authoryear,round]{natbib}


\title{\Rpackage{MSnID} Package for Handling MS/MS Identifications}
\author{Vladislav A. Petyuk}

\begin{document}
\SweaveOpts{concordance=TRUE, eval=TRUE, prefix.string=graphics}

\maketitle
\tableofcontents



\section{Introduction}
MS/MS identification is a process with some uncertainty. Some peptide or
protein to spectrum matches are true and some are not. There are ways to
score how well the peptide/protein fragmentation pattern observed in
MS/MS spectrum matches the theoretical amino acid sequence. Other ways to
assess confidence of identification are:
\begin{enumerate}
    \item the difference in theoretical and experimental masses
    \item frequency of observation (true identifications tend to be more
    consistent)
    \item peptide sequence properties (only in the case of technique involving
    protein digestion) such as missed cleavages or presence of cleavages not
    typical for a given protease and chemical reagent.
\end{enumerate}
A typical and currently most reliable way to quantify uncertainty in the list
of identify spectra, peptides or proteins relies on so-called decoy database.
For bottom-up (i.e. involving protein digestion) approaches a common way to
construct a decoy database is simple inversion of protein amino-acid sequences.
The other approach commonly used in top-down (that is intact protein)
approaches is based on shuffling of amino acids within protein sequence.
Typically normal and decoy sequences are concatenated into one FASTA file.
Some software tools (e.g. MS-GF+) perform the task of constructing and
appending the decoy sequence internally on the fly. If the spectrum matches
to normal protein sequence it can be true or false match. Matches to decoy
part of the database are false only (excluding the palindromes). Therefore
the false discovery rate (FDR) of identifications can be estimated as ratio
of hits to decoy over normal parts of the protein sequence database.
\\
There are multiple levels of identification that FDR can be estimated for.
First, is at the level of peptide/protein-to-spectrum matches. Second is at
the level of unique peptide sequences. Note, true peptides tend to be
identified by more then one spectrum. False peptide tend to be sporadic.
Therefore, after collapsing the redundant peptide identifications from
multiple spectra to the level of unique peptide sequence, the FDR typically
increases. The extend of FDR increase depends on the type and complexity of
the sample. The same trend is true for estimating the identification FDR at
the protein level. True proteins tend to be identified with multiple peptides,
while false protein identifications are commonly covered only by one peptide.
Therefore FDR estimate tend to be even higher for protein level compare to
peptide level.
\\
The estimation of the FDR is also affected by the number of LC-MS (runs)
datasets in the experiment. Again, true identifications tend to be more
consistent from run to run, while false are sporadic. After collapsing the
redundancy across the runs, the number of true identification reduces much
stronger compare to false identifications. Therefore, the peptide and
protein FDR estimates need to be re-evaluated.
\\
The main objective of the MSnID package is to provide convenience tools for
handling tasks on estimation of FDR, defining and optimizing the filtering
criteria and ensuring confidence in MS/MS identification data. The user can
specify the criteria for filtering the data (e.g. goodness or p-value of
matching of experimental and theoretical fragmentation mass spectrum,
deviation of theoretical from experimentally measured mass, presence of
missed cleavages in the peptide sequence, etc), evaluate the performance of
the filter judging by FDRs at spectrum, peptide and protein levels, and
finally optimize the filter to achieve the maximum number of identifications
while not exceeding maximally allowed FDR upper threshold.


\section{Starting the project}
First, the \Robject{MSnID} object has to be initialized. The main argument is
path to the working directory. This directory will be used for storing cached
analysis results. Caching/memoisation mechanism is based on \CRANpkg{R.cache}.
<<>>=
library("MSnID")
msnid <- MSnID(".")
@

\section{Reading MS/MS data}
One way to read in peptide/protein to MS/MS spectrum matching (PSM) results as a
table from a text file and assing the \Rclass{data.frame} object.
<<>>=
PSMresults <- read.delim(system.file("extdata", "human_brain.txt",
                                            package="MSnID"),
                                stringsAsFactors=FALSE)
psms(msnid) <- PSMresults
show(msnid)
@
Alternative and currently the preferred way to read MS/MS results is by
parsing mzIdentML files (*.mzid or *.mzid.gz extensions). The \Rcode{read\_mzIDs}
function leverages \Biocpkg{mzID} package facilities.
<<>>=
mzids <- system.file("extdata", "c_elegans.mzid.gz", package="MSnID")
msnid <- read_mzIDs(msnid, mzids)
show(msnid)
@
Internally PSMs stored as \CRANpkg{data.table} object.
\\
The example file \texttt{"c\_elegans.mzid.gz"} is based on MS-GF+ search engine.
The \Rcode{read\_mzIDs} function reads results of any MS/MS search engine as
long as it compliant with mzIdentML standard. In general case, use aforementioned
\Rcode{psms<-} function.


\section{Updating columns}
Note, to take a full advantage of the \Biocpkg{MSnID}, the
the following columns have to be present. Checking of columns happens
internally.
<<echo=FALSE>>=
sort(MSnID:::.mustBeColumns)
@
Check what are the current column names in the MS/MS search results table.
<<>>=
names(msnid)
@


\section{Basic info on the \Robject{MSnID} object instance}
Printing the \Robject{MSnID} object returns some basic information such as
\begin{itemize}
    \item Working directory.
    \item Number of spectrum files used to generate data.
    \item Number of peptide-to-spectrum matches and corresponding FDR.
    \item Number of unique peptide sequences and corresponding FDR.
    \item Number of unique proteins or amino acid sequence accessions and
            corresponding FDR.
\end{itemize}
False discovery rate or FDR is defined here as a ratio of hits to decoy
accessions to the non-decoy (normal) accessions. In terms of forward and revese
protein sequences that would mean ratio of \#reverse/\#forward.
While computing FDRs of PSMs and unique peptide sequences is trivial,
definition of protein (accession) FDR is a subject for discussion in the field
of proteomics. Here, protein (accession) FDR is computed the same way as in
IDPicker software \cite{Zhang2007} and simply constitutes a ratio of
unique accessions from decoy component to non-decoy component of the
sequence database.
<<>>=
show(msnid)
@



\section{Analysis of peptide sequences}
A particular properties of peptide sequences we are interested in are
\begin{enumerate}
    \item irregular cleavages at the termini of the peptides and
    \item missing cleavage site within the peptide sequences.
\end{enumerate}
The default regular expressions of valid and missed cleavage
patterns correspond to trypsin.
Counting the number of irregular cleavage termimi (0,1 or 2) in peptides
sequence creates a new column \texttt{numIrregCleavages}.
<<>>=
msnid <- assess_termini(msnid, validCleavagePattern="[KR]\\.[^P]")
@
Counting the number of missed cleavages in peptides sequence creates a new
column \texttt{numMissCleavages}.
<<>>=
msnid <- assess_missed_cleavages(msnid, missedCleavagePattern="[KR](?=[^P$])")
@
Now the object has two more columns, \texttt{numIrregCleavages} and
\texttt{numMissCleavages}, evidently corresponding to the number
of termini with irregular cleavages and number of missed cleavages
within the peptide sequence.
<<label=missedCleavages, fig=TRUE, include=FALSE, width=9>>=
pepCleav <- unique(psms(msnid)[,c("numMissCleavages", "isDecoy", "peptide")])
pepCleav <- as.data.frame(table(pepCleav[,c("numMissCleavages", "isDecoy")]))
library("ggplot2")
ggplot(pepCleav, aes(x=numMissCleavages, y=Freq, fill=isDecoy)) +
    geom_bar(stat='identity', position='dodge') +
    ggtitle("Number of Missed Cleavages")
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-missedCleavages}
\end{center}
\pagebreak[0]
Peptide sequences, as any other column, can by accessed by
directly using \Rcode{\$} operator. For example:
Counting number of cysteins per peptide sequence
<<>>==
msnid$numCys <- sapply(lapply(strsplit(msnid$peptide,''),'==','C'),sum)
@
Calculating peptide lengths. Note, -4 decrements the AA count by two
the flanking AAs and the two dots separating them from the
actual peptide sequence.
<<label=lengths, fig=TRUE, include=FALSE, width=9>>=
msnid$PepLength <- nchar(msnid$peptide) - 4
pepLen <- unique(psms(msnid)[,c("PepLength", "isDecoy", "peptide")])
ggplot(pepLen, aes(x=PepLength, fill=isDecoy)) +
    geom_histogram(position='dodge', binwidth=3) +
    ggtitle("Distribution on of Peptide Lengths")
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-lengths}
\end{center}
\pagebreak[0]



\section{Trimming the data}
The main way for trimming or filtering the data is
\Rfunction{apply\_filter} function. The second argument can be either
1) a string representing expression that will be evaluated in the context of
data.frame containing MS/MS results or 2) \Rclass{MSnFilter} class object
(explained below). Note, the reduction in FDR. Assuming that the sample
has been digested with trypsin, the true identifications tend to be
fully tryptic and contain fewer missed cleavages.
Original FDRs.
<<>>=
show(msnid)
@
Leaving only fully tryptic peptides.
<<>>=
msnid <- apply_filter(msnid, "numIrregCleavages == 0")
show(msnid)
@
Filtering out peptides with more then 2 missed cleavages.
<<>>=
msnid <- apply_filter(msnid, "numMissCleavages <= 2")
show(msnid)
@



\section{Parent ion mass measurement error}
Assuming both \Rcode{calculatedMassToCharge} and
\Rcode{experimentalMassToCharge} are present in \Rcode{names(msnid)},
one can access parent ion mass measurement in points per million (ppm) units.
<<label=ppmOriginal, fig=TRUE, include=FALSE, width=9>>=
ppm <- mass_measurement_error(msnid)
ggplot(as.data.frame(ppm), aes(x=ppm)) +
    geom_histogram(binwidth=100)
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-ppmOriginal}
\end{center}
\pagebreak[0]
Note, although the MS/MS search was done with $\pm$ 20ppm parent ion mass
tolerance, error stretch over 1000 in ppm units. The reason is that the
settings of the MS/MS search engine MS-GF+ (used for the analysis of this
LC-MS dataset) fairly assumed that the instrument could have picked
non-monoisotopic peaks of parent ion for fragmentation
and thus considered peptides that were off by $\pm$ 1 Da
(\textsuperscript{13}C-\textsuperscript{12}C to be exact). Similar settings
can be found in other search engines (e.g X!Tandem).
<<label=deltaMass, fig=TRUE, include=FALSE, width=9>>=
dM <- with(psms(msnid),
    (experimentalMassToCharge-calculatedMassToCharge)*chargeState)
x <- data.frame(dM, isDecoy=msnid$isDecoy)
ggplot(x, aes(x=dM, fill=isDecoy)) +
    geom_histogram(position='stack', binwidth=0.1)
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-deltaMass}
\end{center}
\pagebreak[0]
Ideally, to avoid this problem, the MS/MS datasets have to be either aquired
in MIPS (monoisotopic ion precurson selection) mode or preprocessed with
DeconMSn \cite{Mayampurath2008} tools that identifies the monoisotipic peaks
post-experimentally. The \Biocpkg{MSnID} package provide a simple
\Rcode{correct\_peak\_selection} function that simply adds or subtracts
the difference between \textsuperscript{13}C and \textsuperscript{12}C
to make the error less then 1 Dalton.
<<label=ppmCorrectedMass, fig=TRUE, include=FALSE, width=9>>=
msnid.fixed <- correct_peak_selection(msnid)
ppm <- mass_measurement_error(msnid.fixed)
ggplot(as.data.frame(ppm), aes(x=ppm)) +
    geom_histogram(binwidth=0.25)
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-ppmCorrectedMass}
\end{center}
\pagebreak[0]
Alternatively, one can simply apply a filter to remove any matches that
do not fit the $\pm$ 20 ppm tolerance.
<<label=ppmFiltered20, fig=TRUE, include=FALSE, width=9>>=
msnid.chopped <- apply_filter(msnid, "abs(mass_measurement_error(msnid)) < 20")
ppm <- mass_measurement_error(msnid.chopped)
ggplot(as.data.frame(ppm), aes(x=ppm)) +
    geom_histogram(binwidth=0.25)
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-ppmFiltered20}
\end{center}
\pagebreak[0]

For further processing we'll consider the \Rcode{msnid.chopped}
data that ignores matches with 1 Da errors. Note, if the center of
the histogram is significantly shifted from zero,
\Rcode{experimentalMassToCharge} can be post-experimentally recalibrated.
This MS/MS data was preprocessed with
DtaRefinery tool \cite{Petyuk2010} that post-experimentally eliminates
any systematic mass measurement error. At this point, the \Rcode{recalibrate}
function implements the most simplistic algorithm avalable in the
DtaRefinery tool.
<<label=ppmRecalibrated, fig=TRUE, include=FALSE, width=9>>=
msnid <- recalibrate(msnid.chopped)
ppm <- mass_measurement_error(msnid)
ggplot(as.data.frame(ppm), aes(x=ppm)) +
    geom_histogram(binwidth=0.25)
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-ppmRecalibrated}
\end{center}
\pagebreak[0]



\section{\Robject{MSnIDFilter} object for filtering MS/MS identifications}
The criteria that will be used for filtering the MS/MS data has to be present
in the \Robject{MSnID} object. We will use -log10 transformed MS-GF+
Spectrum E-value, reflecting the goodness of match experimental and
theoretical fragmentation patterns as one the filtering criteria.
Let's store it under the "msmsScore" name. The score density distribution
shows that it is a good discriminant between non-decoy (red)
and decoy hits (green).
\\
For alternative MS/MS search engines refer to the engine-specific manual for
the names of parameters reflecting the quality of MS/MS spectra matching.
Examples of such parameters are \Rcode{E-Value} for X!Tandem
and \Rcode{XCorr} and \Rcode{$\Delta$Cn2} for SEQUEST.
<<label=msmsScoreDistribution, fig=TRUE, include=FALSE, width=9>>=
msnid$msmsScore <- -log10(msnid$`MS-GF:SpecEValue`)
params <- psms(msnid)[,c("msmsScore","isDecoy")]
ggplot(params) +
    geom_density(aes(x = msmsScore, color = isDecoy, ..count..))
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-msmsScoreDistribution}
\end{center}
\pagebreak[0]
As a second criterion we will be using the absolute mass measurement
error (in ppm units) of the parent ion. The mass measurement errors tend to
be small for non-decoy (enriched with real identificaiton) hits (red line) and
is effectively uniformly distributed for decoy hits.
<<label=absPpmDistribution, fig=TRUE, include=FALSE, width=9>>=
msnid$absParentMassErrorPPM <- abs(mass_measurement_error(msnid))
params <- psms(msnid)[,c("absParentMassErrorPPM","isDecoy")]
ggplot(params) +
    geom_density(aes(x = absParentMassErrorPPM, color = isDecoy, ..count..))
@
\begin{center}
\includegraphics[width=0.8\textwidth]{graphics-absPpmDistribution}
\end{center}
\pagebreak[0]
MS/MS fiters are handled by a special \Rclass{MSnIDFilter} class objects.
Individual filtering criteria can be set by name
(that is present in \Rcode{names(msnid)}), comparison operator (>, <, = , ...)
defining if we should retain hits with higher or lower given the threshold and
finally the threshold value itself.
<<>>=
filtObj <- MSnIDFilter(msnid)
filtObj$absParentMassErrorPPM <- list(comparison="<", threshold=10.0)
filtObj$msmsScore <- list(comparison=">", threshold=10.0)
show(filtObj)
@
Let's evaluate the performace of the filter at three different levels of
confidence assessment.
<<>>=
evaluate_filter(msnid, filtObj, level="PSM")
evaluate_filter(msnid, filtObj, level="peptide")
evaluate_filter(msnid, filtObj, level="accession")
@


\section{Optimizing the MS/MS filter to achieve the maximum number of
identifications within a given FDR upper limit threshold}
The threshold values in the example above are not necessarily optimal and set
just be in the range of probable values. Filters can be optimized to ensure
maximum number of identifications (peptide-to-spectrum matches,
unique peptide sequences or proteins) within a given FDR upper limit.
\\
First, the filter can be optimized simply by stepping through
individual parameters and their combinations. The idea has been described in
\cite{Piehowski2013a}. The resulting \Robject{MSnIDFilter} object can be
used for final data filtering or can be used as a good starting parameters for
follow-up refining optimizations with more advanced algorithms.
<<>>=
filtObj.grid <- optimize_filter(filtObj, msnid, fdr.max=0.01,
                                method="Grid", level="peptide",
                                n.iter=500)
show(filtObj.grid)
@
%# (absParentMassErrorPPM < 2) & (msmsScore > 7.8)

The resulting \Rcode{filtObj.grid} can be further fine tuned with such
optimization routines as simulated annealing or Nelder-Mead optimization.
<<>>=
filtObj.nm <- optimize_filter(filtObj.grid, msnid, fdr.max=0.01,
                                method="Nelder-Mead", level="peptide",
                                n.iter=500)
show(filtObj.nm)
@
%# (absParentMassErrorPPM < 3) & (msmsScore > 7.8)

Let's compare the original (good guess) and optimized fileters. Obviously the
latter yields much more peptide identifications, while not exceeding
the maximally allowed FDR threshold of 1%.
<<>>=
evaluate_filter(msnid, filtObj, level="peptide")
evaluate_filter(msnid, filtObj.nm, level="peptide")
@
Finally we'll apply the optimized filter to proceed with further
steps in the analysis pipeline.
<<>>=
msnid <- apply_filter(msnid, filtObj.nm)
show(msnid)
@
Identifications that matched decoy and contaminant protein sequences can be
removed by providing filters in the forms of text strings that will be
evaluated in the context of PSM table.
<<>>=
msnid <- apply_filter(msnid, "isDecoy == FALSE")
show(msnid)
msnid <- apply_filter(msnid, "!grepl('Contaminant',accession)")
show(msnid)
@



\section{Data output and interface with other Bioconductor packages}
One can extract the entire PSMs tables as
\Rcode{data.frame} or \Rcode{data.table}
<<>>=
psm.df <- psms(msnid)
psm.dt <- as(msnid, "data.table")
@
If only interested in the non-redundant list of confidently identified
peptides or proteins
<<>>=
peps <- peptides(msnid)
head(peps)
prots <- accessions(msnid)
head(prots)
prots <- proteins(msnid) # may be more intuitive then accessions
head(prots)
@
The \Biocpkg{MSnID} package is aimed at providing convenience functionality
to handle MS/MS identifications. Quantification \textit{per se} is outside of
the scope of the package. The only type of quantitation that can be seamlessly
tied with MS/MS identification analysis is so-called
\emph{spectral counting} approach. In such an approach a peptide abundance is
considered to be directly proportional to the number of matched MS/MS spectra.
In its turn protein abunance is proportional to the sum of the number of
spectra of the matching peptides. The \Rclass{MSnID} object can be converted
to an \Rclass{MSnSet} object defined in \Biocpkg{MSnbase} that extends generic
Bioconductor \Rclass{eSet} class to quantitative proteomics data.
The spectral count data can be analyzed with \Biocpkg{msmsEDA},
\Biocpkg{msmsTests} or \Biocpkg{DESeq} packages.
<<label=convertingToMSnSet>>=
msnset <- as(msnid, "MSnSet")
library("MSnbase")
head(fData(msnset))
head(exprs(msnset))
@
Note, the convertion from \Robject{MSnID} to \Robject{MSnSet} uses peptides
as features. The number of redundant peptide observations represent so-called
spectral count that can be used for rough quantitative analysis. Summing of
all of the peptide counts to a proteins level can be done with
\Rcode{combineFeatures} function from \Biocpkg{MSnbase} package.
<<>>=
msnset <- combineFeatures(msnset,
                            fData(msnset)$accession,
                            redundancy.handler="unique",
                            fun="sum",
                            cv=FALSE)
head(fData(msnset))
head(exprs(msnset))
@


% clean-up
<<eval=TRUE, echo=FALSE, results=hide>>=
unlink(".Rcache", recursive=TRUE)
@


\bibliographystyle{plainnat}
\bibliography{msnid}
\end{document}



