| Title: | Label-free data analysis pipeline for optimal identification and quantitation |
|---|---|
| Description: | The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. |
| Authors: | Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. |
| Maintainer: | Laurent Gatto <[email protected]> Sebastian Gibb <[email protected]> |
| License: | GPL-2 |
| Version: | 2.31.0 |
| Built: | 2024-11-19 04:31:33 UTC |
| Source: | https://github.com/bioc/synapter |
The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy.
Two pipelines of variying flexibility are proposed the preform data
analysis: (1) the synergise1 and synergise2 function are
single entry functions for a complete analysis and (2) low level,
step-by-step data processing can be achieved as described in
?Synapter.
A high-level overview of the package and how to operate it can be
found in the vignette, accessinble with
synapterGuide(). Detailed information about the data processing
can be found in the respective function and class manual pages
appropriately referenced in the vignette.
For questions, use the Biocondcutor mailing list or contact the author. The vignette has a section with details on where/how to get help.
Laurent Gatto, Pavel V. Shliaha, Nick J. Bond and Sebastian Gibb
Maintainer: Laurent Gatto <[email protected]> and Sebastian Gibb <[email protected]>
Improving qualitative and quantitative performance for MSE-based label free proteomics, N.J. Bond, P.V. Shliaha, K.S. Lilley and L. Gatto, J Proteome Res. 2013 Jun 7;12(6):2340-53. doi: 10.1021/pr300776t. PubMed PMID: 23510225.
The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies, P.V. Shliaha, N.J. Bond, L. Gatto and K.S. Lilley, J Proteome Res. 2013 Jun 7;12(6):2323-39. doi: 10.1021/pr300775k. PMID: 23514362.
This function creates an RDS file to store the
“Unique Peptides Database” for future runs of
synergise or Synapter.
createUniquePeptideDbRds(fastaFile, outputFile = paste0(fastaFile, ".rds"), missedCleavages = 0, IisL = FALSE, verbose = interactive())createUniquePeptideDbRds(fastaFile, outputFile = paste0(fastaFile, ".rds"), missedCleavages = 0, IisL = FALSE, verbose = interactive())
fastaFile |
file path of the input fasta file |
outputFile |
file path of the target RDS file; must have the file extension ".rds" |
missedCleavages |
Number of maximal allowed missed cleavages. Default is 0. |
IisL |
If |
verbose |
If |
Sebastian Gibb <[email protected]>
Synapter for details about the cleavage
procedure.
## Not run: createUniquePeptideDbRds("uniprot.fasta", "uniprot.fasta.rds") ## End(Not run)## Not run: createUniquePeptideDbRds("uniprot.fasta", "uniprot.fasta.rds") ## End(Not run)
This function takes all possible combination of pepfiles
of length greater or equal than 2 and computes the number of
estimated incorrect peptides, the number of unique peptides,
the number of unique protetypic peptides and the
false discovery rate after merging for each combination.
The best combination has an fdr lower than masterFdr
and the highest number of unique (proteotypic) peptides.
estimateMasterFdr(pepfiles, fastafile, masterFdr = 0.025, fdr = 0.01, proteotypic = TRUE, missedCleavages = 0, IisL = FALSE, maxFileComb = length(pepfiles), verbose = interactive())estimateMasterFdr(pepfiles, fastafile, masterFdr = 0.025, fdr = 0.01, proteotypic = TRUE, missedCleavages = 0, IisL = FALSE, maxFileComb = length(pepfiles), verbose = interactive())
pepfiles |
A |
fastafile |
A |
masterFdr |
A |
fdr |
Peptide FDR level for individual peptide files filtering. |
proteotypic |
Logical. Should number proteotypic peptides be used to choose best combination and plot results or total number of unique peptides. |
missedCleavages |
Number of maximal missed cleavage sites. Default is 0. |
IisL |
If |
maxFileComb |
A |
verbose |
Should progress messages be printed? |
The false discovery rate for the master (merged) file is calcualted
by summing the number of estimated false discoveries for each
individual final peptide file (number of unique peptides in that file
multiplied by fdr) divided by the total number of unique
peptides for that specific combination.
The function returns an instance of the class
"MasterFdrResults".
An instance of class "MasterFdrResults".
See details above.
Laurent Gatto
Bond N. J., Shliaha P.V., Lilley K.S. and Gatto L., (2013) J. Prot. Research.
The makeMaster function to combine
the peptide data as suggested by estimateMasterFdr into
one single master peptide file.
The vignette, accessible with synapterGuide() illustrates a
complete pipeline using estimateMasterFdr and
makeMaster.
This function takes a final peptide file and
returns information about the unique peptide scores
and their number in each peptide matchType (PepFrag1 and
PepFrag2) by protein dataBaseType (Random and Regular)
category. The function is a lightweight verion of
getPepNumbers and plotPepScores
(to be used with Synapter instances) for
individual files.
inspectPeptideScores(filename)inspectPeptideScores(filename)
filename |
The name of a final peptide file. |
A table of peptide counts in each peptide matchType * protein dataBasteType category. Also plots the distribution of respective peptide scores.
Laurent Gatto
This function combines a list of peptide final peptide files into
one single master file that is obtained by merging
the unique peptides from the filtered original peptide files.
Additionally it can combine multiple final fragment files into a fragment
library.
makeMaster(pepfiles, fragmentfiles, fdr = 0.01, method = c("BH", "Bonferroni", "qval"), span.rt = 0.05, span.int = 0.05, maxDeltaRt = Inf, removeNeutralLoss = TRUE, removePrecursor = TRUE, tolerance = 2.5e-05, verbose = interactive())makeMaster(pepfiles, fragmentfiles, fdr = 0.01, method = c("BH", "Bonferroni", "qval"), span.rt = 0.05, span.int = 0.05, maxDeltaRt = Inf, removeNeutralLoss = TRUE, removePrecursor = TRUE, tolerance = 2.5e-05, verbose = interactive())
pepfiles |
A |
fragmentfiles |
A |
fdr |
A |
method |
A |
span.rt |
A |
span.int |
A |
maxDeltaRt |
A |
removeNeutralLoss |
A |
removePrecursor |
A |
tolerance |
A |
verbose |
A |
The merging process is as follows:
Each individual peptide final peptide file is filtered to retain
(i) non-duplicated unique tryptic peptides, (ii) peptides with a
false discovery rate <= fdr and (iii) proteins with a false
positive rate <= fpr.
The filtered peptide files are ordered (1) according to their total number of peptides (for example [P1, P2, P3]) and (2) as before with the first item is positioned last ([P2, P3, P1] in the previous example). The peptide data are then combined in pairs in these respective orders. The first one is called the master file.
For each (master, slave) pair, the slave peptide file retention times are modelled according to the (original) master's retention times and slave peptides, not yet present in the master file are added to the master file.
The final master datasets, containing their own peptides and
the respective slave specific retention time adjusted peptides are returned
as a MasterPeptides instance.
The resulting MasterPeptides instance can be further used
for a complete master vs. peptides/Pep3D analysis, as described in
Synapter, synergise or using the GUI
(synapterGUI). To do so, it must be serialised (using the
saveRDS function) with a .rds file
extension, to be recognised (and loaded) as a R object.
When several quantitation (or identification) files are combined as a master set to be mapped back against the inidividual final peptide files, the second master [P2, P3, P1] is used when analysing the peptide data that was first selected in the master generation (P1 above). This is to avoid aligning two identical sets of peptides (those of P1) and thus not being able to generate a valid retention time model. This is detected automatically for the user.
The two master peptides dataframes can be exported to disk as
two csv files with writeMasterPeptides. The
MasterPeptides object returned by makeMaster can be
saved to disk (with save or saveRDS) and later reloaded
(with load or readRDS) for further analysis.
The fragment library generation works as follows:
Each individual final fragment file is imported and only peptides present in the master dataset are used.
The fragments are combined based on their precursor ions.
The intensities of identical fragments (seen in different runs) is summed and divided by the summed precursor intensity (of the same peptide in different runs).
Afterwards the intensities are normalized to the average precursor intensity of the different runs.
Finally a MSnExp object is created.
The fragment library dataframe can be exported to disk as
csv file with writeFragmentLibrary.
An instance of class "MasterPeptides".
Laurent Gatto, Sebastian Gibb
Shliaha P.V., Bond N. J., Lilley K.S. and Gatto L., in prep.
See the Synapter class manual page for
detailed information on filtering and modelling and the general
algorithm implemented in the synapter package.
The estimateMasterFdr function allows to control
false dicovery rate when combining several peptide files while
maximising the number of identifications and suggest which
combination of peptide files to use.
The vignette, accessible with synapterGuide()
illustrates a complete pipeline using estimateMasterFdr and
makeMaster.
"MasterFdrResults"
This class stored the results of the
Objects are created with the estimateMasterFdr function.
all:Object of class "data.frame" with results
of all possible combinations.
files:Object of class "character" with file
names used in combinations.
best:Object of class "data.frame" with results
of the best combination.
masterFdr: Object of class "numeric" storing
the best combination.
signature(object = "MasterFdrResults"): ...
signature(object = "MasterFdrResults"): ...
signature(object = "MasterFdrResults"): ...
signature(object = "MasterFdrResults"): ...
signature(x = "MasterFdrResults", y = "missing"): ...
signature(object = "MasterFdrResults"): ...
Laurent Gatto <[email protected]>
Improving qualitative and quantitative performance for MSE-based label free proteomics, N.J. Bond, P.V. Shliaha, K.S. Lilley and L. Gatto, Journal of Proteome Research, 2013, in press.
The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies, P.V. Shliaha, N.J. Bond, L. Gatto and K.S. Lilley, Journal of Proteome Research, 2013, in press.
## Not run: library(synapterdata) loadMaster() class(master) master ## End(Not run)## Not run: library(synapterdata) loadMaster() class(master) master ## End(Not run)
"MasterPeptides"
A class to store the results of makeMaster. This class
stored the 2 versions (orders) of the master final peptide data.
Objects can be created by calls of the form makeMaster.
masters:Object of class "list" storing the 2
master data.frame objects.
pepfiles:Object of class "character" with the
list of final peptide input files.
fdr:Object of class "numeric" with the
peptide false discovery applied when creating the filter.
method:Object of class "character" with the
peptide p-value adjustment method. One of BH (default),
qval or Bonferroni.
orders:Object of class "list" with the
numeric vectors specifying the order of pepfiles
used to generate the respective masters
data.frames.
fragmentfiles:Object of class "character" with the
list of final fragment input files.
fragments:Object of class data.frame storing the
combined final fragment data.
fragmentlibrary:Object of class "MSnExp" storing
the fragment library.
signature(object = "MasterPeptides"): to print a
textual representation of the instance.
Laurent Gatto
Improving qualitative and quantitative performance for MSE-based label free proteomics, N.J. Bond, P.V. Shliaha, K.S. Lilley and L. Gatto, Journal of Proteome Research, 2013, in press.
The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies, P.V. Shliaha, N.J. Bond, L. Gatto and K.S. Lilley, Journal of Proteome Research, 2013, in press.
This method plots the results of the fragment matching procedure
(fragmentMatching). A single plot contains two panels. The upper
panel shows the identification fragments and the lower one the MS2 spectrum of
the quantitation run. Common peaks are drawn in a slightly darker colour and
with a point on the top.
object |
Object of class |
key |
|
column |
|
... |
Further arguments passed to internal functions. |
signature(object = "Synapter", key = "character",
column = "character", verbose = "logical", ...)Plots identification fragments against quantitation spectra.
The ... arguments are passed to the internal functions.
Currently legend.cex, fragment.cex, and
most of the plot.default arguments
(like xlim, ylim, main, xlab, ylab,
...) are supported.
legend.cex and fragment.cex control the size of the legend
and fragments labels (default: 0.5).
Please see par for details about cex.
If verbose = TRUE a progress bar is shown.
Sebastian Gibb <[email protected]>
This method tries to remove saturation effects from the intensity counts.
## S4 method for signature 'MSnSet' requantify(object, saturationThreshold, method=c("sum", "reference", "th.mean", "th.median", "th.weighted.mean"), ...)## S4 method for signature 'MSnSet' requantify(object, saturationThreshold, method=c("sum", "reference", "th.mean", "th.median", "th.weighted.mean"), ...)
object |
An |
saturationThreshold |
|
method |
|
... |
further arguments passed to internal functions. Currently
|
Currently requantify supports 3 (5) different requantification
methods.
"sum" is the simplest requantification method. All ions of a
peptide below the saturation threshold are summed to get the new intensity.
This method accept an additional argument, namely onlyCommonIsotopes
If onlyCommonIsotopes=TRUE (default) all ions that are not seen in
all runs are removed and only the common seen ions are summed. In contrast
onlyCommonIsotopes=FALSE sums all ions regardless they are present
in all runs.
In "reference" the run that has the most unsaturated ions in common
with all the other runs. If there are more than one run, the most intense is
used as reference. The other runs are corrected as
follows:
Find common ions between current and the reference run.
Divide the intensities of the common ions and calculate the mean of these quotients as a run specific scaling factor.
Multiply the unsaturated ions of the current run by the scaling factor and replace the saturated ones by the product of the scaling factor and the intensities of their corresponding ions in the reference run.
Sum the rescaled ion intensities.
The "th.*" methods are nearly identical. All of them calculate the
theoretical isotopic distribution for the given sequence of the peptide.
Subsequently the unsaturated ions are divided by their theoretical
proportion and the mean/median/weighted.mean
(proportions are used as weights) of these intensities are calculated per
charge state. The sum of the charge state values is used as requantified
intensity for this peptide.
If requantifyAll=FALSE (default) just peptides with at least one
saturated ion are requantified (unsaturated peptides are unaffected). If
requantify=TRUE all peptides even these where all ions are below
saturationThreshold are requantified by their theoretical
distribution.
MSnSet where the
assayData are requantified.
Sebastian Gibb [email protected] and Pavel Shliaha
See discussion on github: https://github.com/lgatto/synapter/issues/39
MSnSet documentation: MSnSet
This method rescales the intensity values of an MSnSet
object to be suiteable for TOP3 quantification.
## S4 method for signature 'MSnSet,MSnSet' rescaleForTop3(before, after, saturationThreshold, onlyForSaturatedRuns=TRUE, ...)## S4 method for signature 'MSnSet,MSnSet' rescaleForTop3(before, after, saturationThreshold, onlyForSaturatedRuns=TRUE, ...)
before |
An |
after |
The same |
saturationThreshold |
|
onlyForSaturatedRuns |
|
... |
further arguments passed to internal functions. Currently ignored. |
If an MSnSet object was requantified using the
method="sum" requantification method
(see requantify,MSnSet-method) TOP3 is not valid anymore
because the most abundant proteins are penalised by removing high intensity
isotopes.
To overcome this rescaleForTop3 takes the proportion
isotope/sum(isotopes for each requantified peptide and calculates a
correction factor by comparing these proportions against the unsaturated
isotopes before requantification. The new rescale intensity values of the
isotopes are the mean correction factor multiplied with the corrected
intensity values (see
https://github.com/lgatto/synapter/issues/39#issuecomment-207987278 for
the complete explanation/discussion).
MSnSet where the
assayData are requantified.
Sebastian Gibb [email protected] and Pavel Shliaha
See discussion on github: https://github.com/lgatto/synapter/issues/39
MSnSet documentation: MSnSet
https://github.com/lgatto/synapter/issues/39#issuecomment-207987278
A reference class to store, manage and process Synapt G2 data to combine identification and quantitation results.
The data, intermediate and final results are stored together in such a ad-how container called a class. In the frame of the analysis of a set of 3 or 5 data files, namely as identification peptide, a quantitation peptide and a quantitation Pep3D, and identification fragments and quantitation spectra, such a container is created and populated, updated according to the user's instructions and used to display and export results.
The functionality of the synapter package implemented in the
Synapter class in described in the Details section
below. Documentation for the individual methods is provided in the
Methods section. Finally, a complete example of an analysis is
provided in the Examples section, at the end of this document.
See also papers by Shliaha et al. for details about ion mobility
separation and the manuscript describing the synapter
methodology.
Synapter(filenames, master) ## creates an instance of class 'Synapter'Synapter(filenames, master) ## creates an instance of class 'Synapter'
filenames |
A named |
master |
A |
A Synapter object logs every operation that is applied to
it. When displayed with show or when the name of the instance
is typed at the R console, the original input file names, all
operations and resulting the size of the respective data are
displayed. This allows the user to trace the effect of respective
operations.
The construction of the data and analysis container, technically
defined as an instance or object of class Synapter, is created
with the Synapter constructor.
This function requires 4 or 6 files as input, namely,
the identification final peptide csv file, the quantitation final peptide
csv file, the quantitation Pep3D csv file (as exported from the PLGS
software), the fasta file use for peptide identification, and optional
the identification fragments csv file and the quantitation
spectra xml file. The fasta file ('fasta') could be an RDS file generated by
link{createUniquePeptideDbRds}, too.
The file names need to be specified as a named list with names
'identpeptide', 'quantpeptide', 'quantpep3d', 'fasta', 'identfragments'
and 'quantspectra' respectively.
These files are read and the data is stored in the newly
created Synapter instance.
The final peptide files are filtered
to retain peptides with matchType corresponding to
PepFrag1 and PepFrag2, corresponding to unmodified
round 1 and 2 peptide identification. Other types, like
NeutralLoss_NH3, NeutralLoss_H20, InSource,
MissedCleavage or VarMod are not considered in the rest
of the analysis. The quantitation Pep3D data is filtered to retain
Function equal to 1 and unique quantitation spectrum ids,
i.e. unique entries for multiple charge states or isotopes of an EMRT
(exact mass-retention time features).
Then, p-values for Regular peptides are computed based on
the Regular and Random database types score
distributions, as described in Käll et al.,
2008a. Only unique peptide sequences are taken into account:
in case of duplicated peptides, only one entry is kept.
Empirical p-values are adjusted using Bonferroni
and Benjamini and Hochberg, 1995 (multtest package)
and q-values are computed using the qvalue package
(Storey JD and Tibshirani R., 2003 and Käll et
al., 2008b). Only Regular entries are stored in the
resulting data for subsequent analysis.
The data tables can be exported as csv spreadsheets with the
writeIdentPeptides and writeQuantPeptides methods.
The first step of the analysis aims to match reliable peptide.
The final peptide datasets are
filtered based on the FDR (BH is default) using the
filterQuantPepScore and filterIdentPepScore
methods. Several plots are provided to illustrate peptide score
densities (from which p-values are estimated, plotPepScores;
use getPepNumbers to see how many peptides were available) and
q-values (plotFdr).
Peptides matching to multiple proteins in the fasta file (non-unique
tryptic identification and quantitation peptides) can be
discarded with the filterUniqueDbPeptides method. One can
also filter on the peptide length using filterPeptideLength.
Another filtering criterion is mass accuracy. Error tolerance
quantiles (in ppm, parts per million) can be visualised with the
plotPpmError method. The values can be retrieved with
getPpmErrorQs. Filtering is then done separately for
identification and quantitation peptide data using
filterIdentPpmError and filterQuantPpmError
respectively. The previous plotting functions can be used again to
visualise the resulting distribution.
Filtering can also be performed at the level of protein false
positive rate, as computed by the PLGS application
(protein.falsePositiveRate column), which counts the
percentage of decoy proteins that have been identified prior to the
regular protein of interest. This can be done with the
filterIdentProtFpr and filterQuantProtFpr methods.
Note that this field is erroneously called a false positive rate in
the PLGS software and the associated manuscript; it is a false
discovery rate.
Common and reliable identification and quantitation peptides are
then matched based on their sequences and merged using the
mergePeptides method.
Systematic differences between identification features and
quantitation features retention times are modelled by
fitting a local regression (see the loess function for
details), using the modelRt method. The smoothing parameter,
or number of neighbour data points used the for local fit, is
controlled by the span parameter that can be set in the above
method.
The effect of this parameter can be observed with the plotRt
method, specifying what = "data" as parameters. The resulting
model can then be visualised with the above method specifying
what = "model", specifying up to 3 number of standard
deviations to plot. A histogram of retention time differences can
be produced with the plotRtDiffs method.
To visualise the feature space plotFeatures could be used. It
generates one or two (if ion mobility is available) plots of
retention time vs mass and mass vs ion mobility for each data source,
namely, Identification data, Quantitation data and Quantitation Pep3D data.
Systematic differences between intensities of identification features and
quantitation features depending on retention times are modelled by
fitting a local regression (see the loess function for
details), using the modelIntensity method. The smoothing parameter,
or number of neighbour data points used the for local fit, is
controlled by the span parameter that can be set in the above
method.
The effect of this parameter can be observed with the plotIntensity
method, specifying what = "data" as parameters. The resulting
model can then be visualised with the above method specifying
what = "model", specifying up to 3 number of standard
deviations to plot.
Matching of identification peptides and quantitation EMRTs is done within a mass tolerance in parts per million (ppm) and the modelled retention time +/- a certain number of standard deviations. To help in the choice of these two parameters, a grid search over a set of possible values is performed and performance metrics are recorded, to guide in the selection of a 'best' pair of parameters.
The following metrics are computed:
(1) the percentage of identification
peptides that matched a single quantitation EMRT (called prcntTotal),
(2) the percentage of identification peptides used in the retention time
model that matched the quantitation EMRT corresponding to the
correct quantitation peptide in ident/quant pair of the model
(called prcntModel)
and
(3) the detailed about the matching of the features used for
modelling (accessible with getGridDetails) and the
corresponding details grid that reports the percentage of
correct unique assignments.
The detailed grid results specify the number of non
matched identification peptides (0), the number of correctly (1) or
wrongly (-1) uniquely matched identification peptides, the number of
identification peptides that matched 2 or more peptides including
(2+) or excluding (2-) the correct quantitation equivalent are also
available.
See the next section for additional details about how matching.
The search is performed with the searchGrid method, possibly
on a subset of the data (see Methods and Examples sections for
further details).
The parameters used for matching can be set manually with
setPpmError, setRtNsd, setImDiff respectively,
or using setBestGridParams to apply best parameters as defined using
the grid search. See example and method documentation for details.
The identification peptide - quantitation EMRT matching, termed identification transfer, is performed using the best parameters, as defined above with a grid search, or using user-defined parameters.
Matching is considered successful when one and only one EMRT is found in the mass tolerance/retention time/ion mobility window defined by the error ppm, number of retention time standard deviations, and ion mobility difference parameters. The values of uniquely matched EMRTs are reported in the final matched dataframe that can be exported (see below). If however, none or more than one EMRTs are matched, 0 or the number of matches are reported.
As identification peptides are serially individually matched to 'close' EMRTs, it is possible for peptides to be matched the same EMRT independently. Such cases are reported as -1 in the results dataframes.
The results can be assess using the plotEMRTtable (or
getEMRTtable to retrieve the values) and performace
methods. The former shows the number of identification peptides assigned to
none (0), exactly 1 (1) or more (> 2) EMRTs.
The latter method reports matched identification peptides, the number of
(q-value and protein FPR filtered) identification and quantitation peptides.
Matched EMRT and quantitation peptide numbers are then compared
calculating the synapter enrichment (100 * ( synapter - quant ) / quant)
and Venn counts.
As an additional step it is possible to remove less intense peaks from the
spectra and fragment data. Use plotCumulativeNumberOfFragments to
plot the number of fragments vs the intensity and to find a good threshold.
The filterFragments method could remove peaks if the intensity is
below a specified threshold via the minIntensity argument. Set the
maxNumber argument to keep only the maxNumber highest
peaks/fragments. The what argument controls the data on which the
filter is applied. Use what = "fragments.ident" for the
identification fragments and what = "spectra.quant" for the
quantiation spectra data.
After importing fragment and spectra data it is possible to
match peaks between the identification fragments and the quantitation
spectra using the fragmentMatching method.
Use setFragmentMatchingPpmTolerance to set
the maximal allowed tolerance for considering a peak as identical.
There are two different methods to visualise the results of the fragment
matching procedure. plotFragmentMatching plots the fragments and
spectra for each considered pair.
plotFragmentMatchingPerformance draws two plots. On the left panel
you could see the performance of different thresholds for the number of
common peaks for unique matches. The right panel visualizes the performance
of different differences (delta) of common peaks between the best match
(highest number of common peaks) and the second best match in each non
unique match group. plotFragmentMatchingPerformance returns the
corresponding values invisible or use fragmentMatchingPerformance to
access these data.
Use filterUniqueMatches and filterNonUniqueMatches to remove
unique or non unique matches below the threshold of common peaks
respective the difference in common peaks from the MatchedEMRTs data.frame.
The merged identification and quantitation peptides can be exported
to csv using the writeMergedPeptides method. Similarly, the
matched identification peptides and quantitation EMRTs are exported
with writeMatchedEMRTs.
Complete Synapter instances can be serialised with
save, as any R object, and reloaded with load for
further analysis.
It is possible to get the fragment and spectra data from the identification
and quantitation run using getIdentificationFragments respectively
getQuantitationSpectra.
signature(object = "Synapter"): Merges
quantitation and identification final peptide data, used to
perform retention time modelling (see modelRt below).
signature(object = "Synapter",
span = "numeric"): Performs local polynomial regression
fitting (see loess) retention time alignment using
span parameter value to control the degree of smoothing.
signature(object = "Synapter",
span = "numeric"): Performs local polynomial regression
fitting (see loess) intensity values using
span parameter value to control the degree of smoothing.
signature(object = "Synapter", ppm =
"numeric", nsd = "numeric", imdiff = "numeric"):
Finds EMRTs matching identification peptides using ppm
mass tolerance, nsd number of retention time standard
deviations and imdiff difference in ion mobility.
The last three parameters are optional if previously
set with setPpmError, setRtNsd, setImDiff,
or, better, setBestGridParams (see below).
signature(object = "Synapter",
method = c("rescue", "copy")):
The method parameter defined the behaviour for those
high confidence features that where identified in both identification and
quantitation acquisitions and used for the retention time model
(see mergePeptides). Prior to version 1.1.1, these
features were transferred from the quantitation pep3d file if
unique matches were found, as any feature ("transfer").
As a result, those matching 0 or > 1 EMRTs were
quantified as NA. The default is now to "rescue"
the quantitation values of these by directly retrieving the data
from the quantification peptide data. Alternatively, the
quantitation values for these features can be directly taken
from the quantitation peptide data using "copy", thus
effectively bypassing identification transfer.
signature(object="Synapter",
ppms="numeric", nsds="numeric", imdiffs = "numeric",
subset="numeric", n = "numeric", verbose="logical"):
Performs a grid search. The
grid is defined by the ppm, nsd and imdiffs
numerical vectors, representing the sequence of values to be
tested. Default are seq(5, 20, 2), seq(0.5, 5,
0.5), seq(0.2, 2, 0.2) respectively. To ignore ion mobility set
imdiffs = Inf.
subset and n allow to use a
randomly chosen subset of the data for the grid search to
reduce search time. subset is a numeric, between 0 and 1,
describing the percentage of data to be used; n specifies
the absolute number of feature to use. The default is to use all
data. verbose controls whether textual output should be
printed to the console. (Note, the mergedEMRTs value used
in internal calls to findEMRTs is "transfer" - see
findEMRTs for details).
signature(object="Synapter",
ppm = "numeric", verbose = "logical":
Performs a fragment matching between spectra and fragment data.
The ppm argument controls the tolerance that is used to consider
two peaks (MZ values) as identical. If verbose is TRUE
(default) a progress bar is shown.
signature(object = "Synapter"): Display
object by printing a summary to the console.
signature(x="Synapter"): Returns a list
of dimensions for the identification peptide, quantitation
peptide, merged peptides and matched features data sets.
signature(object="Synapter"): Returns a
character of length 6 with the names of the input files
used as identpeptide, quantpeptide,
quantpep3d, fasta, identfragments and
quantspectra.
signature(object="Synapter"): Returns a
character of variable length with a summary of processing
undergone by object.
signature(object="Synapter", digits =
"numeric"): Returns a named list of length 3 with the
precent of total (prcntTotal), percent of model
(prcntModel) and detailed (details) grid search
results. The details grid search reports the proportion
of correctly assigned features (+1) to all unique assignments
(+1 and -1). Values are rounded to 3 digits by default.
signature(object="Synapter"): Returns
a list of number of ..., -2, -1, 0, +1, +2, ... results
found for each of the ppm/nsd pairs tested during
the grid search.
signature(object="Synapter"):
Returns a named numeric of length 3 with best grid values
for the 3 searches. Names are prcntTotal,
prcntModel and details.
signature(object="Synapter"):
Returns a named list of matrices (prcntTotal,
prcntModel and details). Each matrix gives the
ppm and nsd pairs that yielded the best grid
values (see getBestGridValue above).
signature(object="Synapter",
what="character"): This methods set the best parameter pair,
as determined by what. Possible values are auto
(default), model, total and details. The 3
last ones use the (first) best parameter values as reported by
getBestGridParams. auto uses the best model
parameters and, if several best pairs exists, the one that
maximises details is selected.
signature(object="Synapter", fdr =
"numeric"): Sets the peptide score false discovery rate
(default is 0.01) threshold used by filterQuantPepScore
and filterIdentPepScore.
signature(object="Synapter"): Returns
the peptide false discrovery rate threshold.
signature(object="Synapter", ppm =
"numeric"): Set the identification mass tolerance to
ppm (default 10).
signature(object="Synapter"):
Returns the identification mass tolerance.
signature(object="Synapter", ppm =
"numeric"): Set the quantitation mass tolerance to ppm
(default 10).
signature(object="Synapter"):
Returns the quantitation mass tolerance.
signature(object="Synapter", ppm =
"numeric"): Sets the identification and quantitation mass
tolerance ppm (default is 10).
signature(object="Synapter", span =
"numeric"): Sets the loess span parameter;
default is 0.05.
signature(object="Synapter"): Returns
the span parameter value.
signature(object="Synapter", nsd =
"numeric"): Sets the retention time tolerance nsd,
default is 2.
signature(object="Synapter"): Returns the
value of the retention time tolerance nsd.
signature(object="Synapter", imdiff =
"numeric"): Sets the ion mobility tolerance imdiff,
default is 0.5.
signature(object="Synapter"): Returns the
value of the ion mobility tolerance imdiff.
signature(object="Synapter", qs =
"numeric", digits = "numeric"): Returns the mass tolerance
qs quantiles (default is c(0.25, 0.5, 0.75, seq(0.9,
1, 0.01)) for the identification and quantitation
peptides. Default is 3 digits.
signature(object="Synapter", qs =
"numeric", digits = "numeric"): Returns the retention time
tolerance qs quantiles (default is c(0.25, 0.5,
0.75, seq(0.9, 1, 0.01)) for the identification and
quantitation peptides. Default is 3 digits.
signature(object="Synapter"): Returns
the number of regular and random quantitation and identification
peptide considered for p-value calculation and used to plot the
score densities (see plotPepScores). Especially the
difference between random and regular entries are informative in
respect with the confidence of the random scores distribution.
signature(object="Synapter",
ppm = "numeric"): Sets the fragment matching mass tolerance ppm
(default is 25).
signature(object="Synapter"):
Returns the fragment matching mass tolerance in ppm.
signature(object="Synapter", k =
"numeric"): Returns a named list of length
2 with the proportion of identification and quantitation
peptides that are considered significant with a threshold of
k (default is c(0.001, 0.01, 0.5, 0.1)) using raw
and adjusted p-values/q-values.
signature(object="Synapter"): Returns a
table with the number of 0, 1, 2, ... assigned EMRTs.
signatute(object="Synapter", verbose =
TRUE): Returns (and displays, if verbose) the
performance of the synapter analysis.
signatute(object="Synapter", verbose =
TRUE): Returns (and displays, if verbose) information
about number of missing values and identification source of
transfered EMRTs.
signature(object="Synapter",
what = c("unique", "non-unique"):
Returns the performance of the fragment matching for unqiue or
non-unique matches. The return valus is a matrix with
seven columns. The first column ncommon/deltacommon
contains the thresholds. Column 2 to 5 are the true positives tp,
false positives fp, true negatives tn, false negatives
fn for the merged peptide data. The sixth column all shows
the corresponding number of peptides for all peptides
(not just the merged ones) and the last column shows the FDR fdr
for the current threshold (in that row) for the merged data.
Please note that the FDR is overfitted/underestimated because the merged peptides are the peptides from the highest quality spectra were PLGS could easily identify the peptides. The peptides that are not present in the merged data are often of lower quality hence the FDR would be higher by trend.
See plotFragmentMatchingPerformance for a graphical
representation.
signature(object="Synapter",
missedCleavages = 0, IisL = TRUE, verbose = TRUE):
This method first digests the fasta database file and keeps
unique tryptic peptides. (NOTE: since version 1.5.3, the tryptic
digestion uses the cleaver package, replacing the more
simplistic inbuild function. The effect of this change is
documented in https://github.com/lgatto/synapter/pull/47).
The number of maximal missed cleavages can be set as missedCleavages
(default is 0).
If IisL = TRUE Isoleucin and Leucin are treated as the same aminoacid.
In this case sequences like "ABCI", "ABCL" are removed
because they are not unqiue anymore. If IisL = FALSE (default)
"ABCI" and "ABCL" are reported as unique.
The peptide sequences are then used as a
filter against the identification and quantitation peptides,
where only unique proteotyptic instances (no miscleavage allowed
by default) are eventually kept in the object instance.
This method also removes any additional duplicated peptides,
that would not match any peptides identified in the fasta
database.
signature(object="Synapter",
missedCleavages = 0, IisL = TRUE, verbose = TRUE): As
filterUniqueDbPeptides for quantitation peptides only.
signature(object="Synapter",
missedCleavages = 0, IisL = TRUE, verbose = TRUE): As
filterUniqueDbPeptides for identification peptides
only.
signature(object="Synapter", fdr
= "numeric", method = "character"): Filters the quantitation
peptides using fdr false discovery rate. fdr is
missing by default and is retrieved with getPepScoreFdr
automatically. If not set, default value of 0.01 is
used. method defines how to performe p-value adjustment;
one of BH, Bonferrone or qval. See details
section for more information.
signature(object="Synapter", fdr
= "numeric", method = "charactet"): As
filterQuantPepScore, but for identification peptides.
signature(object="Synapter", fpr
= "numeric"): Filters quantitation peptides using the protein
false positive rate (erroneously defined as a FPR, should be
FDR), as reported by PLGS, using threshold set by fpr
(missing by default) or retrieved by getProtFpr.
signature(object="Synapter", fpr =
"numeric"): as filterQuantProtFpr, but for
identification peptides.
signature(object="Synapter", ppm
= "numeric"): Filters the quantitation peptides based on the
mass tolerance ppm (default missing) provided or
retrieved automatically using getPpmError.
signature(object="Synapter"): as
filterQuantPpmError, but for identification peptides.
signature(object = "Synapter",
what = c("fragments.ident", "spectra.quant"),
minIntensity = "numeric", maxNumber = "numeric", verbose = "logical"):
Filters the spectra/fragment data using a minimal intensity threshold
(minIntensity) or a maximal number of peaks/fragments threshold
(maxNumber). Please note that the maximal number is transfered to
an intensity threshold and the result could contain less peaks than
specified by maxNumber.
If both arguments are given, the more aggressive one is chosen.
Use the what argument to specify the data that should be filtered.
Set what = "fragments.ident" for the identification fragment data
or what = "spectra.quant" for the quantiation spectra.
If verbose is TRUE (default) a progress bar is shown.
signature(object="Synapter", minNumber =
"numeric"):
Removes all unique matches that have less than minNumber of
peaks/fragments in common. Use
fragmentMatchingPerformance(..., what="unique")/
plotFragmentMatchingPerformance
(left panel) to find an ideal threshold.
signature(object="Synapter", minDelta =
"numeric"):
Removes all non unique matches that have a difference between the best
match (highest number of common peaks/fragments, treated as true match)
and the second best match (second highest number of common
peaks/fragments) less than minDelta. For the matches above the
threshold only the one with the highest number of common peaks/fragments
in each match group is kept.
Use fragmentMatchingPerformance(..., what="non-unique")/
plotFragmentMatchingPerformance (right panel) to find an ideal
threshold.
signature(object="Synapter"):
Removes all non unique identification matches. In rare circumstances (if
the grid search parameters are to wide/relaxed or a fragment library is
used) it could happen that the searchGrid methods matches a single
quantification EMRT to multiple identification EMRTs. This methods removes
all these non unique matches.
signature(object="Synapter", what =
"character"): Plots the proportion of data against the mass
error tolerance in ppms. Depending on what, the data
for identification (what = "Ident"), quantitation
(what = "Quant") or "both" is plotted.
signature(object="Synapter", ...): Plots
a histogram of retention time differences after
alignments. ... is passed to hist.
signature(object="Synapter", what =
"character", f = "numeric", nsd = "numeric"): Plots the
Identification - Quantitation retention time difference as a
function of the Identification retention time. If what is
"data", two plots are generated: one ranging the full range
of retention time differences and one focusing on the highest data
point density and showing models with various span
parameter values, as defined by f (default is 2/3, 1/2,
1/4, 1/10, 1/16, 1/25, 1/50, passed as a numed numeric). If
what is "model", a focused plot with the applied
span parameter is plotted and areas of nsd (default is
x(1, 3, 5) number of standard deviations are shaded around
the model.
signature(object="Synapter", what =
"character", f = "numeric", nsd = "numeric"): Plots the (log2) ratio
of Identification and Quantitation intensities as a
function of the Identification retention time. If what is
"data", two plots are generated: one ranging the full range
of ratios and one focusing on the highest data
point density and showing models with various span
parameter values, as defined by f (default is 2/3, 1/2,
1/4, 1/10, 1/16, 1/25, 1/50, passed as a numed numeric). If
what is "model", a focused plot with the applied
span parameter is plotted and areas of nsd (default is
x(1, 3, 5) number of standard deviations are shaded around
the model.
signature(object="Synapter"): Plots the
distribution of random and regular peptide scores for
identification and quantitation features. This reflects how
peptide p-values are computed. See also getPepNumbers.
signature(object="Synapter", method =
"character"): Displays 2 plots per identification and
quantitation peptides, showing the number of significant
peptides as a function of the FDR cut-off and the expected false
number of false positive as a number of significant
tests. PepFrag 1 and 2 peptides are illustrated on the same
figures. These figures are adapted from plot.qvalue.
method, one of "BH", "Bonferroni" or
"qval", defines what identification statistics to use.
signature(object="Synapter"): Plots
the barchart of number or 0, 1, 2, ... assigned EMRTs (see
getEMRTtable) .
signature(object="Synapter", what =
"character"), maindim = "character":
Plots a heatmap of the respective grid search
results. This grid to be plotted is controlled by what:
"total", "model" or "details" are
available. If ion mobility was used in the grid search you can use
maindim to decided which dimensions should be shown. maindim
could be one of "im" (default), "rt" and "mz". If
maindim = "im" a heatmap for each ion mobility threshold is drawn.
For maindim = "rt" and maindim you get a heatmap for each
retention time respective mass threshold.
signature(object="Synapter", what =
"character", xlim = "numeric", ylim = "numeric", ionmobiltiy = "logical"):
Plots the retention time against precursor mass space.
If what is "all", three (six if ion mobility is available
and ionmobility = TRUE (default is FALSE);
three additional plots with precursor mass against ion mobility)
such plots are created side by side: for the
identification peptides, the quantitation peptides and the
quantitation Pep3D data. If what is "some", a
subset of the rt/mass space can be defined with xlim
(default is c(40, 60)) and ylim (default is
c(1160, 1165)) and identification peptide, quantitation
peptides and EMRTs are presented on the same graph as grey
dots, blue dots and red crosses respectively. In addition,
rectangles based on the ppm and nsd defined tolerances (see
setPpmError and setNsdError) are drawn and
centered at the expected modelled retention time. This last
figure allows to visualise the EMRT matching.
signature(object = "Synapter",
key = "character", column = "character",
verbose = "logical", ...):
Plots two spectra and fragments against each other. Please see
plotFragmentMatching for details.
signature(object = "Synapter",
showAllPeptides = FALSE):
Creates two plots. The left panel shows the performance of filtering the
unique matches of the merged peptides using a different number of common
peaks. The right panel shows the performance of filtering the non unique
matches of the merged peptides using different differences (delta) in
common peaks/fragments. These differences (delta) are
calculated between the match with the highest number of common
peaks/fragments and the second highest one.
Use filterUniqueMatches and filterNonUniqueMatches to filter
the MatchedEMRT data.frame using one of these thresholds.
This function returns a list with two named elements (unqiue
and nonunqiue invisibly. These are the same data as return by
fragmentMatchingPerformance.
Use showAllPeptides=TRUE to add a line for all peptides (not just
the merged onces) to both plots.
signature(object = "Synapter",
what = c("fragments.ident", "spectra.quant")):
Plots the cumulative number of the fragments/peaks vs their intensity (log10
scaled). Use the what argument to create this plot for the
identification fragments (what = "fragments.quant") or the
the quantitation spectra (what = "spectra.quant").
signature(object="Synapter", file
= "character", what = "character", ...): Exports the merged
peptide data to a comma-separated file (default name is
"Res-MergedPeptides.csv").
signature(object="Synapter", file =
"character", ...): As above, saving the
matched EMRT table.
signature(object="Synapter", file
= "character", ...): As above, exporting the identification
peptide data.
signature(object="Synapter", file
= "character", ...): A above, exporting the quantitation
peptide data.
signature(object="Synapter"):
returns the identification fragments as MSnExp.
signature(object="Synapter"):
returns the quantitation spectra as MSnExp.
signature(x = "Synapter"): Coerce
object from Synapter to MSnSet class.
signature(object = "Synapter"):
Test whether a given Synapter object is valid.
signature(object = "Synapter"): Updates an old
Synapter object.
Laurent Gatto [email protected]
Käll L, Storey JD, MacCoss MJ, Noble WS Posterior error probabilities and false discovery rates: two sides of the same coin. J Proteome Res. 2008a Jan; 7:(1)40-4
Bonferroni single-step adjusted p-values for strong control of the FWER.
Benjamini Y. and Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B., 1995, Vol. 57: 289-300.
Storey JD and Tibshirani R. Statistical significance for genome-wide experiments. Proceedings of the National Academy of Sciences, 2003, 100: 9440-9445.
Käll, Storey JD, MacCoss MJ, Noble WS Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. J Proteome Res. 2008b Jan; 7:(1)29-34
Improving qualitative and quantitative performance for MSE-based label free proteomics, N.J. Bond, P.V. Shliaha, K.S. Lilley and L. Gatto, Journal of Proteome Research, 2013, in press.
The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies, P.V. Shliaha, N.J. Bond, L. Gatto and K.S. Lilley, Journal of Proteome Research, 2013, in press.
Trypsin cleavage:
Glatter, Timo, et al. Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin proteolysis over trypsin digestion. Journal of proteome research 11.11 (2012): 5145-5156. http://dx.doi.org/10.1021/pr300273g
Rodriguez, Jesse, et al. Does trypsin cut before proline?. Journal of proteome research 7.01 (2007): 300-305. http://dx.doi.org/10.1021/pr0705035
Brownridge, Philip, and Robert J. Beynon. The importance of the digest: proteolysis and absolute quantification in proteomics. Methods 54.4 (2011): 351-360. http://dx.doi.org/10.1016/j.ymeth.2011.05.005
cleaver's rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html#Tryps
library(synapter) ## always needed ## Not run: ## (1) Construction - to create your own data objects synapterTiny <- Synapter() ## End(Not run) ## let's use synapterTiny, shipped with the package synapterTinyData() ## loads/prepares the data synapterTiny ## show object ## (2) Filtering ## (2.1) Peptide scores and FDR ## visualise/explore peptide id scores plotPepScores(synapterTiny) getPepNumbers(synapterTiny) ## filter data filterUniqueDbPeptides(synapterTiny) ## keeps unique proteotypic peptides filterPeptideLength(synapterTiny, l = 7) ## default length is 7 ## visualise before FDR filtering plotFdr(synapterTiny) setPepScoreFdr(synapterTiny, fdr = 0.01) ## optional filterQuantPepScore(synapterTiny, fdr = 0.01) ## specifying FDR filterIdentPepScore(synapterTiny) ## FDR not specified, using previously set value ## (2.2) Mass tolerance getPpmErrorQs(synapterTiny) plotPpmError(synapterTiny, what="Ident") plotPpmError(synapterTiny, what="Quant") setIdentPpmError(synapterTiny, ppm = 20) ## optional filterQuantPpmError(synapterTiny, ppm = 20) ## setQuantPpmError(synapterTiny, ppm = 20) ## set quant ppm threshold below filterIdentPpmError(synapterTiny, ppm=20) filterIdentProtFpr(synapterTiny, fpr = 0.01) filterQuantProtFpr(synapterTiny, fpr = 0.01) getPpmErrorQs(synapterTiny) ## to be compared with previous output ## (3) Merge peptide sequences mergePeptides(synapterTiny) ## (4) Retention time modelling plotRt(synapterTiny, what="data") setLowessSpan(synapterTiny, 0.05) modelRt(synapterTiny) ## the actual modelling getRtQs(synapterTiny) plotRtDiffs(synapterTiny) ## plotRtDiffs(synapterTiny, xlim=c(-1, 1), breaks=500) ## pass parameters to hist() plotRt(synapterTiny, what="model") ## using default nsd 1, 3, 5 plotRt(synapterTiny, what="model", nsd=0.5) ## better focus on model plotFeatures(synapterTiny, what="all") setRtNsd(synapterTiny, 3) ## RtNsd and PpmError are used for detailed plot setPpmError(synapterTiny, 10) ## if not set manually, default values are set automatically plotFeatures(synapterTiny, what="some", xlim=c(36,44), ylim=c(1161.4, 1161.7)) ## best plotting to svg for zooming set.seed(1) ## only for reproducibility of this example ## (5) Grid search to optimise EMRT matching parameters searchGrid(synapterTiny, ppms = 7:10, ## default values are 5, 7, ..., 20 nsds = 1:3, ## default values are 0.5, 1, ..., 5 subset = 0.2) ## default is 1 ## alternatively, use 'n = 1000' to use exactly ## 1000 randomly selected features for the grid search getGrid(synapterTiny) ## print the grid getGridDetails(synapterTiny) ## grid details plotGrid(synapterTiny, what = "total") ## plot the grid for total matching plotGrid(synapterTiny, what = "model") ## plot the grid for matched modelled feature plotGrid(synapterTiny, what = "details") ## plot the detail grid getBestGridValue(synapterTiny) ## return best grid values getBestGridParams(synapterTiny) ## return parameters corresponding to best values setBestGridParams(synapterTiny, what = "auto") ## sets RtNsd and PpmError according the grid results ## 'what' could also be "model", "total" or "details" ## setPpmError(synapterTiny, 12) ## to manually set values ## setRtNsd(synapterTiny, 2.5) ## (6) Matching ident peptides and quant EMRTs findEMRTs(synapterTiny) plotEMRTtable(synapterTiny) getEMRTtable(synapterTiny) performance(synapterTiny) performance2(synapterTiny) ## (7) Exporting data to csv spreadsheets writeMergedPeptides(synapterTiny) writeMergedPeptides(synapterTiny, file = "myresults.csv") writeMatchedEMRTs(synapterTiny) writeMatchedEMRTs(synapterTiny, file = "myresults2.csv") ## These will export the filter peptide data writeIdentPeptides(synapterTiny, file = "myIdentPeptides.csv") writeQuantPeptides(synapterTiny, file = "myQuantPeptides.csv") ## If used right after loading, the non-filted data will be exportedlibrary(synapter) ## always needed ## Not run: ## (1) Construction - to create your own data objects synapterTiny <- Synapter() ## End(Not run) ## let's use synapterTiny, shipped with the package synapterTinyData() ## loads/prepares the data synapterTiny ## show object ## (2) Filtering ## (2.1) Peptide scores and FDR ## visualise/explore peptide id scores plotPepScores(synapterTiny) getPepNumbers(synapterTiny) ## filter data filterUniqueDbPeptides(synapterTiny) ## keeps unique proteotypic peptides filterPeptideLength(synapterTiny, l = 7) ## default length is 7 ## visualise before FDR filtering plotFdr(synapterTiny) setPepScoreFdr(synapterTiny, fdr = 0.01) ## optional filterQuantPepScore(synapterTiny, fdr = 0.01) ## specifying FDR filterIdentPepScore(synapterTiny) ## FDR not specified, using previously set value ## (2.2) Mass tolerance getPpmErrorQs(synapterTiny) plotPpmError(synapterTiny, what="Ident") plotPpmError(synapterTiny, what="Quant") setIdentPpmError(synapterTiny, ppm = 20) ## optional filterQuantPpmError(synapterTiny, ppm = 20) ## setQuantPpmError(synapterTiny, ppm = 20) ## set quant ppm threshold below filterIdentPpmError(synapterTiny, ppm=20) filterIdentProtFpr(synapterTiny, fpr = 0.01) filterQuantProtFpr(synapterTiny, fpr = 0.01) getPpmErrorQs(synapterTiny) ## to be compared with previous output ## (3) Merge peptide sequences mergePeptides(synapterTiny) ## (4) Retention time modelling plotRt(synapterTiny, what="data") setLowessSpan(synapterTiny, 0.05) modelRt(synapterTiny) ## the actual modelling getRtQs(synapterTiny) plotRtDiffs(synapterTiny) ## plotRtDiffs(synapterTiny, xlim=c(-1, 1), breaks=500) ## pass parameters to hist() plotRt(synapterTiny, what="model") ## using default nsd 1, 3, 5 plotRt(synapterTiny, what="model", nsd=0.5) ## better focus on model plotFeatures(synapterTiny, what="all") setRtNsd(synapterTiny, 3) ## RtNsd and PpmError are used for detailed plot setPpmError(synapterTiny, 10) ## if not set manually, default values are set automatically plotFeatures(synapterTiny, what="some", xlim=c(36,44), ylim=c(1161.4, 1161.7)) ## best plotting to svg for zooming set.seed(1) ## only for reproducibility of this example ## (5) Grid search to optimise EMRT matching parameters searchGrid(synapterTiny, ppms = 7:10, ## default values are 5, 7, ..., 20 nsds = 1:3, ## default values are 0.5, 1, ..., 5 subset = 0.2) ## default is 1 ## alternatively, use 'n = 1000' to use exactly ## 1000 randomly selected features for the grid search getGrid(synapterTiny) ## print the grid getGridDetails(synapterTiny) ## grid details plotGrid(synapterTiny, what = "total") ## plot the grid for total matching plotGrid(synapterTiny, what = "model") ## plot the grid for matched modelled feature plotGrid(synapterTiny, what = "details") ## plot the detail grid getBestGridValue(synapterTiny) ## return best grid values getBestGridParams(synapterTiny) ## return parameters corresponding to best values setBestGridParams(synapterTiny, what = "auto") ## sets RtNsd and PpmError according the grid results ## 'what' could also be "model", "total" or "details" ## setPpmError(synapterTiny, 12) ## to manually set values ## setRtNsd(synapterTiny, 2.5) ## (6) Matching ident peptides and quant EMRTs findEMRTs(synapterTiny) plotEMRTtable(synapterTiny) getEMRTtable(synapterTiny) performance(synapterTiny) performance2(synapterTiny) ## (7) Exporting data to csv spreadsheets writeMergedPeptides(synapterTiny) writeMergedPeptides(synapterTiny, file = "myresults.csv") writeMatchedEMRTs(synapterTiny) writeMatchedEMRTs(synapterTiny, file = "myresults2.csv") ## These will export the filter peptide data writeIdentPeptides(synapterTiny, file = "myIdentPeptides.csv") writeQuantPeptides(synapterTiny, file = "myQuantPeptides.csv") ## If used right after loading, the non-filted data will be exported
Opens the relevant vignette; a shortcut to using vignette.
synapterGuide() gives access to the main overview vignette.
synapterGuide()synapterGuide()
Laurent Gatto
This method checks the agreement between synapter analysis and PLGS results.
## S4 method for signature 'MSnSet' synapterPlgsAgreement(object, ...)## S4 method for signature 'MSnSet' synapterPlgsAgreement(object, ...)
object |
An |
... |
further arguments, not used yet. |
Each synapter object has synapterPlgsAgreement column in its
MatchedEMRTs data.frame (see writeMatchedEMRTs).
After converting the synapter object into an MSnSet
instance via as(synapterobject, "MSnSet" this column could be find in
the feature data (fData(msnset)$synapterPlgsAgreement).
In the synapterPlgsAgreement each peptide is classified as:
"agree": EMRT identified in identification and quantitation run
by PLGS and same EMRT matched in synapter's grid search.
"disagree": EMRT identified in identification and quantitation
run by PLGS and a different EMRT was matched in synapter's grid search.
"no_plgs_id": EMRT was not identified in the
quantitation run by PLGS but matched in synapter's grid search.
"no_synapter_transfer": EMRT was identified in the
identification and quantitation run by PLGS but not matched in synapter's
grid search.
"no_id_or_transfer": EMRT was not identified in the
quantitation run by PLGS and not matched in synapter's grid search.
"multiple_ident_matches": a single quantitation EMRT was
matched by synapter to multiple identification EMRTs found by PLGS (could
happen if the grid search parameters are too relaxed).
After combining multiple MSnSet the method
synapterPlgsAgreement adds additional columns to the feature data:
nIdentified: how often a peptide was identified across
multiple runs?
nAgree: how often a peptide was identified by PLGS and synapter
across multiple runs (counts "agree" entries)?
nDisagree: how often a peptide was differently identified by
PLGS and synapter across multiple runs (counts "disagree" entries)?
synapterPlgsAgreementRatio: nAgree/(nAgree +
nDisagree).
MSnSet where the columns nIdentified,
nAgree, nDisagree and synapterPlgsAgreementRatio were
added to the feature data.
Sebastian Gibb [email protected]
See discussion on github: https://github.com/lgatto/synapter/issues/73
MSnSet documentation: MSnSet
Instead of using data to load the synapterTiny data set,
synapterTinyData will load it and initialise it for proper
downstream analysis, during which the 04_test_database.fasta
file, provided with the package and references inside the object
needs, to be accessed. However, as the exact location can not be known
in advance, the reference is updated with the file's correct local
path.
This data set has been generated with the Synapter
constructor. Note that the input data file sizes have been reduced by
depleting many rows (peptides and EMRTs) from the original
csv files.
In addition, several columns that where not necessary for
processing were also removed. As such, the data stored in
synapterTiny does not relect the data obtained when following
the section 'Preparing the input data' in the section, without however
affecting the processing and final results.
synapterTinyData()synapterTinyData()
A character vector with the data set name, "synapterTiny". Used
for its side effect of loading synapterTiny, an instance of
class Synapter, in .GlovalEnv.
Laurent Gatto
Improving qualitative and quantitative performance for MSE-based label free proteomics, N.J. Bond, P.V. Shliaha, K.S. Lilley and L. Gatto, Journal of Proteome Research, 2013, in press.
The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies, P.V. Shliaha, N.J. Bond, L. Gatto and K.S. Lilley, Journal of Proteome Research, 2013, in press.
synapterTinyData() synapterTinysynapterTinyData() synapterTiny
Performs a complete default analysis on the files defined
in filenames, creates a complete html report and
saves/exports all results as csv and rds files.
See details for a description of the pipeline and
Synapter for manual execution of individual steps.
synergise(filenames, master = FALSE, object, outputdir, outputfile = paste0("synapter_report_", strftime(Sys.time(), "%Y%m%d-%H%M%S"), ".html"), fdr = 0.01, fdrMethod = c("BH", "Bonferroni", "qval"), fpr = 0.01, peplen = 7, missedCleavages = 0, IisL = FALSE, identppm = 20, quantppm = 20, uniquepep = TRUE, span = 0.05, grid.ppm.from = 2, grid.ppm.to = 20, grid.ppm.by = 2, grid.nsd.from = 0.5, grid.nsd.to = 5, grid.nsd.by = 0.5, grid.subset = 1, grid.n = 0, grid.param.sel = c("auto", "model", "total", "details"), mergedEMRTs = c("rescue", "copy", "transfer"), template = system.file("reports", "synergise1.Rmd", package = "synapter"), verbose = interactive())synergise(filenames, master = FALSE, object, outputdir, outputfile = paste0("synapter_report_", strftime(Sys.time(), "%Y%m%d-%H%M%S"), ".html"), fdr = 0.01, fdrMethod = c("BH", "Bonferroni", "qval"), fpr = 0.01, peplen = 7, missedCleavages = 0, IisL = FALSE, identppm = 20, quantppm = 20, uniquepep = TRUE, span = 0.05, grid.ppm.from = 2, grid.ppm.to = 20, grid.ppm.by = 2, grid.nsd.from = 0.5, grid.nsd.to = 5, grid.nsd.by = 0.5, grid.subset = 1, grid.n = 0, grid.param.sel = c("auto", "model", "total", "details"), mergedEMRTs = c("rescue", "copy", "transfer"), template = system.file("reports", "synergise1.Rmd", package = "synapter"), verbose = interactive())
filenames |
A named |
master |
A |
object |
An instance of class |
outputdir |
A |
outputfile |
A |
fdr |
Peptide false discovery rate. Default is 0.01. |
fdrMethod |
P-value adjustment method. One of |
fpr |
Protein false positive rate. Default is 0.01. |
peplen |
Minimum peptide length. Default is 7. |
missedCleavages |
Number of allowed missed cleavages. Default is 0. |
IisL |
If |
identppm |
Identification mass tolerance (in ppm). Default is 20. |
quantppm |
Quantitation mass tolerance (in ppm). Default is 20. |
uniquepep |
A |
span |
The loess span parameter. Default is 0.05. |
grid.ppm.from |
Mass tolerance (ppm) grid starting value. Default is 2. |
grid.ppm.to |
Mass tolerance (ppm) grid ending value. Default is 20. |
grid.ppm.by |
Mass tolerance (ppm) grid step value. Default is 2. |
grid.nsd.from |
Number of retention time stdev grid starting value. Default is 0.5. |
grid.nsd.to |
Number of retention time stdev grid ending value. Default is 5. |
grid.nsd.by |
Number of retention time stdev grid step value. Default is 0.5. |
grid.subset |
Percentage of features to be used for the grid search. Default is 1. |
grid.n |
Absolute number of features to be used for the grid search. Default is 0, i.e ignored. |
grid.param.sel |
Grid parameter selection method. One of
|
mergedEMRTs |
One of |
template |
A |
verbose |
A |
Data can be input as a Synapter object if available or
as a list of files (see filenames) that will be used to read the
data in. The html report and result files will be created in the
outputdir folder. All other input parameters have default values.
The data processing and analysis pipeline is as follows:
If uniquepep is set to TRUE (default), only unique
proteotypic identification and quantitation peptides are retained.
Peptides are filtered for a FDR <= fdr (default is 0.01)
using the "BH" method (see fdr and fdrMethod
parameters for details).
Peptide with a mass tolerance > 20 ppms (see quantppm and
identppm) are filtered out.
Peptides with a protein false positive rate (as reported by the
PLGS software) > fpr are filtered out.
Common identification and quantitation peptides are merged and a retention time
model is created using the Local Polynomial Regression Fitting
(loess function for the stats package) using
a default span value of 0.05.
A grid search to optimise the width in retention time and mass
tolerance for EMRTs matching is performed. The default grid
search space is from 0.5 to 5 by 0.5 retention time model standard
deviations (see grid.nsd.from, grid.nsd.to and
grid.nsd.by parameters) and from 2 to 20 by 2 parts per
million (ppm) for mass tolerance (see grid.ppm.from,
grid.ppm.to and grid.ppm.by parameters).
The data can be subset using using an absolute number of features
(see grid.n) or a fixed percentage (see grid.subset).
The pair of optimal nsd and ppm is chosen
(see grid.param.sel parameter).
The quantitation EMRTs are matched using the optimised parameters.
If a master identification file is used (master is set to TRUE, default
is FALSE), the relevant actions that have already been executed when
the file was created with makeMaster are not repeated here.
Invisibly returns an object of class Synapter.
Used for its side effect of creating an html report of
the run in outputdir.
Laurent Gatto, Sebastian Gibb
Bond N. J., Shliaha P.V., Lilley K.S. and Gatto L. (2013) J. Prot. Research.
output <- tempdir() ## a temporary directory synapterTinyData() synergise(object = synapterTiny, outputdir = output, outputfile = "synapter.html", grid.subset = 0.2) htmlReport <- paste0("file:///", file.path(output, "synapter.html")) ## the result report ## Not run: browseURL(htmlReport) ## open the report with default browser ## End(Not run)output <- tempdir() ## a temporary directory synapterTinyData() synergise(object = synapterTiny, outputdir = output, outputfile = "synapter.html", grid.subset = 0.2) htmlReport <- paste0("file:///", file.path(output, "synapter.html")) ## the result report ## Not run: browseURL(htmlReport) ## open the report with default browser ## End(Not run)
Performs a complete default analysis on the files defined
in filenames, creates a complete html report and
saves/exports all results as csv and rds files.
See details for a description of the pipeline and
Synapter for manual execution of individual steps.
synergise2(filenames, master = FALSE, object, outputdir, outputfile = paste0("synapter_report_", strftime(Sys.time(), "%Y%m%d-%H%M%S"), ".html"), fdr = 0.01, fdrMethod = c("BH", "Bonferroni", "qval"), fpr = 0.01, peplen = 7, missedCleavages = 2, IisL = FALSE, identppm = 20, quantppm = 20, uniquepep = TRUE, span.rt = 0.05, span.int = 0.05, grid.ppm.from = 2, grid.ppm.to = 20, grid.ppm.by = 2, grid.nsd.from = 0.5, grid.nsd.to = 5, grid.nsd.by = 0.5, grid.imdiffs.from = 0.6, grid.imdiffs.to = 1.6, grid.imdiffs.by = 0.2, grid.subset = 1, grid.n = 0, grid.param.sel = c("auto", "model", "total", "details"), fm.ppm = 25, fm.ident.minIntensity = 70, fm.quant.minIntensity = 70, fm.minCommon = 1, fm.minDelta = 1, fm.fdr.unique = 0.05, fm.fdr.nonunique = 0.05, mergedEMRTs = c("rescue", "copy", "transfer"), template = system.file("reports", "synergise2.Rmd", package = "synapter"), verbose = interactive())synergise2(filenames, master = FALSE, object, outputdir, outputfile = paste0("synapter_report_", strftime(Sys.time(), "%Y%m%d-%H%M%S"), ".html"), fdr = 0.01, fdrMethod = c("BH", "Bonferroni", "qval"), fpr = 0.01, peplen = 7, missedCleavages = 2, IisL = FALSE, identppm = 20, quantppm = 20, uniquepep = TRUE, span.rt = 0.05, span.int = 0.05, grid.ppm.from = 2, grid.ppm.to = 20, grid.ppm.by = 2, grid.nsd.from = 0.5, grid.nsd.to = 5, grid.nsd.by = 0.5, grid.imdiffs.from = 0.6, grid.imdiffs.to = 1.6, grid.imdiffs.by = 0.2, grid.subset = 1, grid.n = 0, grid.param.sel = c("auto", "model", "total", "details"), fm.ppm = 25, fm.ident.minIntensity = 70, fm.quant.minIntensity = 70, fm.minCommon = 1, fm.minDelta = 1, fm.fdr.unique = 0.05, fm.fdr.nonunique = 0.05, mergedEMRTs = c("rescue", "copy", "transfer"), template = system.file("reports", "synergise2.Rmd", package = "synapter"), verbose = interactive())
filenames |
A named |
master |
A |
object |
An instance of class |
outputdir |
A |
outputfile |
A |
fdr |
Peptide false discovery rate. Default is 0.01. |
fdrMethod |
P-value adjustment method. One of |
fpr |
Protein false positive rate. Default is 0.01. |
peplen |
Minimum peptide length. Default is 7. |
missedCleavages |
Number of allowed missed cleavages. Default is 2. |
IisL |
If |
identppm |
Identification mass tolerance (in ppm). Default is 20. |
quantppm |
Quantitation mass tolerance (in ppm). Default is 20. |
uniquepep |
A |
span.rt |
The loess span parameter for retention time correction. Default is 0.05. |
span.int |
The loess span parameter for intensity correction. Default is 0.05. |
grid.ppm.from |
Mass tolerance (ppm) grid starting value. Default is 2. |
grid.ppm.to |
Mass tolerance (ppm) grid ending value. Default is 20. |
grid.ppm.by |
Mass tolerance (ppm) grid step value. Default is 2. |
grid.nsd.from |
Number of retention time stdev grid starting value. Default is 0.5. |
grid.nsd.to |
Number of retention time stdev grid ending value. Default is 5. |
grid.nsd.by |
Number of retention time stdev grid step value. Default is 0.5. |
grid.imdiffs.from |
Ion mobility difference grid starting value. value. Default is 0.6. |
grid.imdiffs.to |
Ion mobility difference grid ending value. Default is 1.6. |
grid.imdiffs.by |
Ion mobility difference grid step value. Default is 0.2. |
grid.subset |
Percentage of features to be used for the grid search. Default is 1. |
grid.n |
Absolute number of features to be used for the grid search. Default is 0, i.e ignored. |
grid.param.sel |
Grid parameter selection method. One of
|
fm.ppm |
Fragment Matching tolerance: Peaks in a range of |
fm.ident.minIntensity |
Minimal intensity of a Identification fragment to be not filtered prior to Fragment Matching. |
fm.quant.minIntensity |
Minimal intensity of a peaks in a Quantitation spectra to be not filtered prior to Fragment Matching. |
fm.minCommon |
Minimal number of peaks that unique matches need to have in common. Default 1. |
fm.minDelta |
Minimal difference in number of peaks that non-unique matches need to have to be considered as true match. Default 1. |
fm.fdr.unique |
Minimal FDR to select |
fm.fdr.nonunique |
Minimal FDR to select |
mergedEMRTs |
One of |
template |
A |
verbose |
A |
In contrast to synergise1 synergise2 extends the
default analysis and offers the follwing unique features:
Performing 3D grid search (M/Z, Retention Time, Ion Mobility) for HDMSE data.
Applying intensity correction.
Filtering results by fragment matching.
Data can be input as a Synapter object if available or
as a list of files (see filenames) that will be used to read the
data in. The html report and result files will be created in the
outputdir folder. All other input parameters have default values.
The data processing and analysis pipeline is as follows:
If uniquepep is set to TRUE (default), only unique
proteotypic identification and quantitation peptides are retained.
Peptides are filtered for a FDR <= fdr (default is 0.01)
using the "BH" method (see fdr and fdrMethod
parameters for details).
Peptide with a mass tolerance > 20 ppms (see quantppm and
identppm) are filtered out.
Peptides with a protein false positive rate (as reported by the
PLGS software) > fpr are filtered out.
Common identification and quantitation peptides are merged and a retention time
model is created using the Local Polynomial Regression Fitting
(loess function for the stats package) using
a default span.rt value of 0.05.
A grid search to optimise the width in retention time and mass
tolerance (and ion mobility for HDMSE) for EMRTs matching is performed.
The default grid search space is from 0.5 to 5 by 0.5 retention time
model standard deviations (see grid.nsd.from, grid.nsd.to and
grid.nsd.by parameters) and from 2 to 20 by 2 parts per
million (ppm) for mass tolerance (see grid.ppm.from,
grid.ppm.to and grid.ppm.by parameters). If HDMSE data
are used the search space is extended from ion mobility difference
0.6 to 1.6 by 0.2 (see grid.imdiffs.from, grid.imdiffs.to
and grid.imdiffs.by).
The data can be subset using using an absolute number of features
(see grid.n) or a fixed percentage (see grid.subset).
The pair of optimal nsd and ppm is chosen
(see grid.param.sel parameter).
Fragment Matching is used to filter false-positive matches from the
grid search using a default of 1 common peak for unique matches and
at least a difference of 1 common peaks to choose the correct match
out of non-unique matches (see fm.minCommon and
fm.minDelta).
The intensity is corrected by a Local Polynomial Regression Fitting
(loess function for the stats package) using a
default span.int value of 0.05.
The quantitation EMRTs are matched using the optimised parameters.
If a master identification file is used (master is set to TRUE, default
is FALSE), the relevant actions that have already been executed when
the file was created with makeMaster are not repeated here.
Invisibly returns an object of class Synapter.
Used for its side effect of creating an html report of
the run in outputdir.
Laurent Gatto, Sebastian Gibb
Bond N. J., Shliaha P.V., Lilley K.S. and Gatto L. (2013) J. Prot. Research.
## Not run: library(synapterdata) data(synobj2) output <- tempdir() ## a temporary directory synergise2(object = synobj2, outputdir = output, outputfile = "synapter.html") htmlReport <- paste0("file:///", file.path(output, "synapter.html")) ## the result report browseURL(htmlReport) ## open the report with default browser ## End(Not run)## Not run: library(synapterdata) data(synobj2) output <- tempdir() ## a temporary directory synergise2(object = synobj2, outputdir = output, outputfile = "synapter.html") htmlReport <- paste0("file:///", file.path(output, "synapter.html")) ## the result report browseURL(htmlReport) ## open the report with default browser ## End(Not run)