R/generics.R
, R/mspeaklists.R
, R/mspeaklists-set.R
, and 1 more
MSPeakLists-class.Rd
Contains all MS (and MS/MS where available) peak lists for a featureGroups
object.
peakLists(obj, ...)
averagedPeakLists(obj, ...)
spectrumSimilarity(obj, ...)
# S4 method for class 'MSPeakLists'
peakLists(obj)
# S4 method for class 'MSPeakLists'
averagedPeakLists(obj)
# S4 method for class 'MSPeakLists'
analyses(obj)
# S4 method for class 'MSPeakLists'
groupNames(obj)
# S4 method for class 'MSPeakLists'
length(x)
# S4 method for class 'MSPeakLists'
show(object)
# S4 method for class 'MSPeakLists,ANY,ANY,missing'
x[i, j, ..., reAverage = FALSE, drop = TRUE]
# S4 method for class 'MSPeakLists,ANY,ANY'
x[[i, j]]
# S4 method for class 'MSPeakLists'
x$name
# S4 method for class 'MSPeakLists'
as.data.table(x, fGroups = NULL, averaged = TRUE)
# S4 method for class 'MSPeakLists'
delete(obj, i = NULL, j = NULL, k = NULL, reAverage = FALSE, ...)
# S4 method for class 'MSPeakLists'
filter(
obj,
absMSIntThr = NULL,
absMSMSIntThr = NULL,
relMSIntThr = NULL,
relMSMSIntThr = NULL,
topMSPeaks = NULL,
topMSMSPeaks = NULL,
minMSMSPeaks = NULL,
isolatePrec = NULL,
deIsotopeMS = FALSE,
deIsotopeMSMS = FALSE,
withMSMS = FALSE,
annotatedBy = NULL,
retainPrecursorMSMS = TRUE,
reAverage = FALSE,
negate = FALSE
)
# S4 method for class 'MSPeakLists'
plotSpectrum(
obj,
groupName,
analysis = NULL,
MSLevel = 1,
title = NULL,
specSimParams = getDefSpecSimParams(),
xlim = NULL,
ylim = NULL,
...
)
# S4 method for class 'MSPeakLists'
spectrumSimilarity(
obj,
groupName1,
groupName2 = NULL,
analysis1 = NULL,
analysis2 = NULL,
MSLevel = 1,
specSimParams = getDefSpecSimParams(),
NAToZero = FALSE,
drop = TRUE
)
# S4 method for class 'MSPeakListsSet'
analysisInfo(obj)
# S4 method for class 'MSPeakListsSet'
show(object)
# S4 method for class 'MSPeakListsSet,ANY,ANY,missing'
x[i, j, ..., reAverage = FALSE, sets = NULL, drop = TRUE]
# S4 method for class 'MSPeakListsSet'
as.data.table(x, fGroups = NULL, averaged = TRUE)
# S4 method for class 'MSPeakListsSet'
delete(obj, ...)
# S4 method for class 'MSPeakListsSet'
filter(
obj,
...,
annotatedBy = NULL,
retainPrecursorMSMS = TRUE,
reAverage = FALSE,
negate = FALSE,
sets = NULL
)
# S4 method for class 'MSPeakListsSet'
plotSpectrum(
obj,
groupName,
analysis = NULL,
MSLevel = 1,
title = NULL,
specSimParams = getDefSpecSimParams(),
xlim = NULL,
ylim = NULL,
perSet = TRUE,
mirror = TRUE,
...
)
# S4 method for class 'MSPeakListsSet'
spectrumSimilarity(
obj,
groupName1,
groupName2 = NULL,
analysis1 = NULL,
analysis2 = NULL,
MSLevel = 1,
specSimParams = getDefSpecSimParams(),
NAToZero = FALSE,
drop = TRUE
)
# S4 method for class 'MSPeakListsSet'
unset(obj, set)
getDefIsolatePrecParams(...)
The MSPeakLists
object to access.
Further arguments passed to plot
.
1MSPeakLists
For [
/[[
: A numeric or character value which is used to select analyses/feature groups by
their index or name, respectively (for the order/names see analyses()/groupNames()
).
For [
: Can also be logical to perform logical selection
(similar to regular vectors). If missing all analyses/feature groups are selected.
For [[
: should be a scalar value. If j
is not specified, i
selects by feature groups instead.
For delete
: The data to remove from. i
are the
feature groups as numeric index, logical or character, j
the MS peaks as numeric indices (rows). If either is
NULL
then data for all is removed. j
may also be a function: it will be called for each feature group,
with the peak list table (a data.table
), feature group name, analysis (NULL
if averaged peak list), type ("MS"
or "MSMS"
) and any other arguments passed as ...
to delete
. The return value of this
function specifies the peak list indices (rows) to be removed (specified as an integer
or logical
vector).
Set to TRUE
to regenerate group averaged MS peak lists. NOTE it is very important
that any annotation data relying on MS peak lists (formulae/compounds) are regenerated afterwards! Otherwise it is
likely that e.g. plotting methods will use wrong MS/MS data.
If set to TRUE
and if the comparison is made between two spectra then drop
is used
to reduce the matrix
return value to a numeric
vector.
The feature group name (partially matched).
The featureGroups
object that was used to
generate this object. If not NULL
it is used to add feature group
information (retention and m/z values).
If TRUE
then feature group averaged peak list data is
used.
A vector with analyses (character
with names or integer
with indices) for which the data
should be deleted. If k!=NULL
then deletions will not occur on group averaged peak lists. Otherwise,
if k=NULL
then deltion occurs on both group averaged and analysis specific peak lists.
Absolute/relative intensity threshold for MS or MS/MS peak
lists. NULL
for none.
Only consider this amount of MS or MS/MS peaks with highest intensity. NULL
to
consider all.
If the number of peaks in an MS/MS peak list (excluding the precursor peak) is lower
than this it will be completely removed. Set to NULL
to ignore.
If not NULL
then value should be a list
with parameters used for isolating the
precursor and its isotopes in MS peak lists (see Isolating precursor data
). Alternatively, TRUE
to
apply the filter with default settings (as given with getDefIsolatePrecParams
).
Remove any isotopic peaks in MS or MS/MS peak lists. This may improve data
processing steps which do not assume the presence of isotopic peaks (e.g. MetFrag for MS/MS). Note that
getMzRPeakLists
does not (yet) support flagging of isotopes.
If set to TRUE
then only results will be retained for which MS/MS data is available. if
negate=TRUE
then only results without MS/MS data will be retained.
Either a formulas
or compounds
object, or a list
with both. Any
MS/MS peaks that are not annotated by any of the candidates in the specified objects are removed.
If TRUE
then precursor peaks will never be filtered out from MS/MS peak lists (note
that precursors are never removed from MS peak lists). The negate
argument does not affect this setting.
If TRUE
then filters are applied in opposite manner.
The name of the feature group for which a plot should be made. To compare spectra, two group names can be specified.
The name of the analysis for which a plot should be made. If NULL
then data from the feature
group averaged peak list is used. When comparing spectra, either NULL
or the analyses for both spectra
should be specified.
The MS level: 1 for regular MS, 2 for MSMS.
The title of the plot. If NULL
a title will be automatically made.
A named list
with parameters that influence the calculation of MS spectra similarities.
See the spectral similarity parameters documentation for more details.
Sets the plot size limits used by
plot
. Set to NULL
for automatic plot sizing.
The names of the feature groups for which the comparison should be made. If both
arguments are specified then a comparison is made with the spectra specified by groupName1
vs those
specified by groupName2
. The length of either can be >1 to generate a comparison matrix.
Alternatively, if groupName2
is NULL
then all the spectra specified in groupName1
will be
compared with eachother, i.e. resulting in a square similarity matrix.
The name of the analysis (analyses) for the comparison. If NULL
then data from the
feature group averaged peak list is used. Otherwise, should be the same length as
groupName1
/groupName2
.
Set to TRUE
to convert NA
similarities (i.e. when no similarity could be
calculated) to zero values.
A character
with name(s) of the sets to keep (or remove if negate=TRUE
).
If perSet=TRUE
then the set specific mass peaks are annotated separately.
Furthermore, if mirror=TRUE
(and there are two sets in the object) then a mirror plot is generated.
The name of the set.
peakLists
returns a nested list containing MS (and MS/MS where
available) peak lists per feature group and per analysis. The format is:
[[analysis]][[featureGroupName]][[MSType]][[PeakLists]]
where
MSType
is either "MS"
or "MSMS"
and PeakLists
a
data.table
containing all m/z values (mz
column) and their intensities (intensity
column). In addition, the
peak list tables may contain a cmp
column which contains an unique
alphabetical identifier to which isotopic cluster (or "compound") a mass
belongs (only supported by MS peak lists generated by Bruker tools at the
moment).
averagedPeakLists
returns a nested list of feature group
averaged peak lists in a similar format as peakLists
.
delete
returns the object for which the specified data was removed.
Objects for this class are returned by generateMSPeakLists
.
The getDefIsolatePrecParams
is used to create a parameter
list for isolating the precursor and its isotopes (see Isolating precursor data
).
peakLists(MSPeakLists)
: Accessor method to obtain the MS peak lists.
averagedPeakLists(MSPeakLists)
: Accessor method to obtain the feature group averaged
MS peak lists.
analyses(MSPeakLists)
: returns a character
vector with the names of the
analyses for which data is present in this object.
groupNames(MSPeakLists)
: returns a character
vector with the names of the
feature groups for which data is present in this object.
length(MSPeakLists)
: Obtain total number of m/z values.
show(MSPeakLists)
: Shows summary information for this object.
x[i
: Subset on analyses/feature groups.
x[[i
: Extract a list with MS and MS/MS (if available) peak
lists. If the second argument (j
) is not specified the averaged peak
lists for the group specified by the first argument (i
) will be
returned.
$
: Extract group averaged MS peaklists for a feature group.
as.data.table(MSPeakLists)
: Returns all MS peak list data in a table.
delete(MSPeakLists)
: Completely deletes specified peaks from MS peak lists.
filter(MSPeakLists)
: provides post filtering of generated MS peak lists, which may further enhance quality of
subsequent workflow steps (e.g. formulae calculation and compounds identification) and/or speed up these
processes. The filters are applied to all peak lists for each analysis. These peak lists are subsequently averaged
to update group averaged peak lists. However, since version 1.1, the resulting feature group lists are
not filtered afterwards.
plotSpectrum(MSPeakLists)
: Plots a spectrum using MS or MS/MS peak lists for a given feature group. Two spectra can be
compared when two feature groups are specified.
spectrumSimilarity(MSPeakLists)
: Calculates the spectral similarity between two or more spectra.
peakLists
Contains a list of all MS (and MS/MS) peak lists. Use the peakLists
method for access.
metadata
Metadata for all spectra used to generate peak lists. Follows the format of the peakLists
slot.
averagedPeakLists
A list
with averaged MS (and MS/MS) peak lists for each feature group.
avgPeakListArgs
A list
with arguments used to generate feature group averaged MS(/MS) peak lists.
origFGNames
A character
with the original input feature group names.
analysisInfo
Analysis information. Use the analysisInfo
method
for access.
Formula calculation typically relies on evaluating the measured isotopic pattern
from the precursor to score candidates. Some algorithms (currently only GenForm
) penalize candidates if
mass peaks are present in MS1 spectra that do not contribute to the isotopic pattern. Since these spectra are
typically very 'noisy' due to background and co-eluting ions, an additional filtering step may be recommended prior
to formula calculation. During this precursor isolation step all mass peaks are removed that are (1) not the
precursor and (2) not likely to be an isotopologue of the precursor. To determine potential isotopic peaks the
following parameters are used:
maxIsotopes
The maximum number of isotopes to consider. For instance, a value of 5 means that
M+0
(i.e. the monoisotopic peak) till M+5
is considered. All mass peaks outside this range are
removed.
mzDefectRange
A two-sized vector
specifying the minimum (can be negative) and maximum
m/z defect deviation compared to the precursor m/z defect. When chlorinated, brominated or other
compounds with strong m/z defect in their isotopologues are to be considered a higher range may be desired.
On the other hand, for natural compounds this range may be tightened. Note that the search range is propegated with
increasing distance from the precursor, e.g. the search range is doubled for M+2
, tripled for
M+3
etc.
intRange
A two-sized vector
specifying the minimum and maximum relative intensity range
compared to the precursor. For instance, c(0.001, 2)
removes all peaks that have an intensity below 0.1% or
above 200% of that of the precursor.
z
The z
value (i.e. absolute charge) to be considerd. For instance, a value of 2
would look for M+0.5
, M+1
etc. Note that the mzDefectRange
is adjusted accordingly
(e.g. halved if z=2
).
maxGap
The maximum number of missing adjacent isotopic peaks ('gaps'). If the (rounded) m/z
difference to the previous peak exceeds this value then this and all next peaks will be removed. Similar to
z
, the maximum gap is automatically adjusted for charge
.
These parameters should be in a list
that is passed to the isolatePrec
argument to filter
. The
default values can be obtained with the getDefIsolatePrecParams
function:
maxIsotopes=5
; mzDefectRange=c(-0.01, 0.01)
; intRange=c(0.001, 2)
; z=1
; maxGap=2
MSPeakLists
MSPeakListsSet
MSPeakListsUnset
spectrumSimilarity
: The principles of spectral binning and cosine similarity calculations
were loosely was based on the code from SpectrumSimilarity()
function of OrgMassSpecR.
MSPeakListsSetMSPeakLists
MSPeakListsUnsetOnly the MS peaks that are present in the specified set are kept. analysisInfo
Returns the analysis info for this object.
filter
and the subset operator ([
) Can be used to select data that is only present for selected
sets. The filter
method is applied for each set individually, and afterwards the results are combined again
(see generateMSPeakLists
). Note that this has important implications for e.g. relative
intensity filters (relMSIntThr
/relMSMSIntThr
), topMSPeaks
/topMSMSPeaks
and
minMSMSPeaks
. Similarly, when the annotatedBy
filter is applied, each set specific MS peak list is
filtered by the annotation results from only that set. plotSpectrum
Is able to highlight set specific mass peaks (perSet
and mirror
arguments). spectrumSimilarity
First calculates similarities for each spectral pair per set (e.g. all
positive mode spectra are compared and then all negative mode spectra are compared). This data is then combined
into an overall similarity value. How this combination is performed depends on the setCombineMethod
field of
the specSimParams
argument.