Package 'CluMSID'

Accessor functions for individual slots of MS2spectrum and pseudospectrum objects

Description

Accessor functions for individual slots of MS2spectrum and pseudospectrum objects

Usage

accessID(x)

accessAnnotation(x)

accessPrecursor(x)

accessRT(x)

accessPolarity(x)

accessSpectrum(x)

accessNeutralLosses(x)

Arguments

x	An object of class MS2spectrum or pseudospectrum

Value

The value of the respective slot of the object (id, annotation, precursor, rt, spectrum, neutral_losses)

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessID(annotatedSpeclist[[1]])

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessAnnotation(annotatedSpeclist[[1]])

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessPrecursor(annotatedSpeclist[[1]])

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessRT(annotatedSpeclist[[1]])

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessPolarity(annotatedSpeclist[[1]])

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessSpectrum(annotatedSpeclist[[1]])

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

accessNeutralLosses(annotatedSpeclist[[1]])

---

Adding external annotations to list of MS2spectrum objects

Description

addAnnotations is used to add annotations that have been assigned externally, e.g. by library search, to a list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList.

Usage

addAnnotations(featlist, annolist, annotationColumn = 4)

Arguments

featlist	A list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList
annolist	A list of annotations, either as a data.frame or csv file. The order of features must be the same as in featlist. Please see the package vignette for a detailed example!
annotationColumn	The column of annolist were the annotation is found. Default is 4, which is the case if writeFeaturelist followed by manual addition of annotations, e.g. in Excel, is used to generate annolist.

Value

A list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList with external annotations added to the annotation slot of each MS2spectrum object.

Examples

load(file = system.file("extdata",
    "featlist.RData",
    package = "CluMSIDdata"))

addAnnotations(featlist, system.file("extdata",
                "post_anno.csv",
                package = "CluMSIDdata"),
                annotationColumn = 4)

---

Convert spectra from MSnbase classes

Description

Convert spectra from MSnbase classes

Usage

as.MS2spectrum(x)

Arguments

x	An object of class Spectrum or Spectrum2

Value

An object of class MS2spectrum

Examples

#Load a "Spectrum2" object from MSnbase
library(MSnbase)
sp <- itraqdata[["X1"]]
#Convert this object to "MS2spectrum" class
new_sp <- as.MS2spectrum(sp)
#Or alternatively:
new_sp <- as(sp, "MS2spectrum")

---

Calculate cosine similarity between two spectra

Description

cossim() calculates the cosine of the spectral constrast angle as a measure for the similarity of two spectra.

Usage

cossim(x, y, type = c("spectrum", "neutral_losses"),
    mzTolerance = 1e-05)

## S4 method for signature 'MS2spectrum,MS2spectrum'
cossim(x, y, type = c("spectrum",
    "neutral_losses"), mzTolerance = 1e-05)

## S4 method for signature 'pseudospectrum,pseudospectrum'
cossim(x, y,
    type = c("spectrum", "neutral_losses"), mzTolerance = 1e-05)

Arguments

x, y	MS2 spectra, either as matrix, MS2spectrum or pseudospectrum objects. x and y must have the same class.
type	Whether similarity between spectra ("spectrum", default) or neutral loss patterns ("neutral_losses") is to be compared
mzTolerance	The m/z tolerance used for merging. If two fragment peaks are within tolerance, they are regarded as the same. Defaults to 1e-5, i.e. 10ppm.

Value

The cosine similarity of x and y

Methods (by class)

• x = MS2spectrum,y = MS2spectrum: cossim method for MS2spectrum objects

• x = pseudospectrum,y = pseudospectrum: cossim method for pseudospectrum objects

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

cossim(annotatedSpeclist[[1]], annotatedSpeclist[[2]])

---

Create distance matrix from list of spectra

Description

distanceMatrix() creates a distance matrix from a list of MS2 spectra, MS1 pseudospectra or neutral loss patterns by pairwise comparison using the specified distance function. This distance matrix is the basis for CluMSID's data mining functions.

Usage

distanceMatrix(speclist, distFun = "cossim", type = c("spectrum",
    "neutral_losses"), mz_tolerance = 1e-05)

Arguments

speclist	A list of MS2spectrum or pseudospectrum objects as generated by extractMS2spectra or extractPseudospectra.
distFun	The distance function to be used. At the moment, only cossim is implemented.
type	"spectrum" (default) for MS2 spectra or MS1 pseudospectra or "neutral_losses" for neutral loss patterns.
mz_tolerance	The m/z tolerance to be used for merging, default is 1e-5, i.e. +/- 10ppm. If the mass-to-charge ratios of two peaks differ less than mz_tolerance, they are assumed to have the same m/z

Value

A numeric length(speclist) by length(speclist) matrix containing pairwise distances (1 - similarity) between all features in speclist. Row and column names are taken from the id slot or, if present, pasted from the id and annotation slots of the MS2spectrum or pseudospectrum objects.

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

distanceMatrix(annotatedSpeclist[1:20])

---

Extract MS2 spectra from raw data files

Description

extractMS2spectra() is used to extract MS2 spectra from raw data files, e.g. mzXML files.

Usage

extractMS2spectra(MSfile, min_peaks = 2, recalibrate_precursor = FALSE,
    RTlims = NULL)

Arguments

MSfile	An LC-MS/MS raw data file in one of the non-proprietary formats that can be parsed by mzR, e.g. mzXML or mzML.
min_peaks	Minimum number of peaks in MS2 spectrum, defaults to 2. Spectra with less than min_peaks fragment peaks will be ignored and not extracted.
recalibrate_precursor	Logical, defaults to FALSE. Applicable only for files that were exported to mzXML using a deprecated version of Bruker Compass Xport (< 3.0.13). If set to TRUE, the precursor m/z will be recalculated from the respective fragment m/z in the MS2 spectrum. For details, see Depke et al. 2017.
RTlims	Retention time interval for the extraction of spectra. Provide as numeric vector of length 2. Spectra with retention time < RTlims[1] or > RTlims[2] will be ignored.

Value

A list with objects of class MS2spectrum, containing MS2 spectra extracted from the raw data.

Examples

my_spectra <- extractMS2spectra(MSfile = system.file("extdata",
                                "PoolA_R_SE.mzXML",
                                package = "CluMSIDdata"),
                                min_peaks = 4, RTlims = c(0,10))

---

Extract pseudospectra

Description

extractPseudospectra() is used to extract MS1 pseudospectra from CAMERA output.

Usage

extractPseudospectra(x, min_peaks = 1, intensity_columns = NULL)

Arguments

x	CAMERA output that contains information on pseudospectra. Can either be of class data.frame or xsAnnotate. It is recommended to use either xsAnnotate objects or data.frames generated from XCMSonline results tables but other data.frames are possible.
min_peaks	Minimum number of peaks in pseudospectrum, defaults to 1. See extractMS2spectra.
intensity_columns	Numeric, defaults to NULL. If a data.frame is used as input which has not been generated from an XCMSonline results table, the indices of the columns that contain the peak intensities in the different samples have to be indicated as intensity_columns.

Value

A list of pseudospectra, stored as objects of class pseudospectrum, analogous to the output of extractMS2spectra.

Examples

pstable <- readr::read_delim(file = system.file("extdata",
                                "TD035_XCMS.annotated.diffreport.tsv",
                                package = "CluMSIDdata"), delim = "\t")

pseudospeclist <- extractPseudospectra(pstable, min_peaks = 2)

---

Generate a data.frame with feature information from list of MS2spectrum objects

Description

featureList generates a data.frame that contains feature ID, precurosur m/z and retention time for all features contained in a list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList. featureList is used internally by writeFeaturelist.

Usage

featureList(featlist)

Arguments

featlist

A list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList

Details

Although originally designed for lists of MS2spectrum objects, the function also works with lists of pseudospectrum objects. In this case, NA is given for precursor m/z.

Value

A data.frame that contains feature ID, precurosur m/z (if available) and retention time

Examples

load(file = system.file("extdata",
    "featlist.RData",
    package = "CluMSIDdata"))

pre_anno <- featureList(featlist)

---

Find spectra that contain a specific fragment

Description

findFragment is used to find spectra that contain a specific fragment ion. Its sister function is findNL, which finds specific neutral losses. Both functions work analogous to getSpectrum.

Usage

findFragment(featlist, mz, tolerance = 1e-05)

Arguments

featlist	a list that contains only objects of class MS2spectrum
mz	The mass-to-charge ratio of the fragment ion of interest.
tolerance	The m/z tolerance for the fragment ion search. Default is 1E-05, i.e. +/- 10ppm.

Value

If the respective fragment is only found in one spectrum, the output is an object of class MS2spectrum; if it is found in more than one spectrum, the output is a list of MS2spectrum objects.

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))
putativeAQs <- findFragment(annotatedSpeclist, 159.068)

---

Find spectra that contain a specific neutral loss

Description

findNL is used to find spectra that contain a specific neutral loss. Its sister function is findFragment, which finds specific fragment ions. Both functions work analogous to getSpectrum.

Usage

findNL(featlist, mz, tolerance = 1e-05)

Arguments

featlist	a list that contains only objects of class MS2spectrum
mz	The mass-to-charge ratio of the neutral loss of interest.
tolerance	The m/z tolerance for the neutral loss search. Default is 1E-05, i.e. +/- 10ppm.

Value

If the respective neutral loss is only found in one spectrum, the output is an object of class MS2spectrum; if it is found in more than one spectrum, the output is a list of MS2spectrum objects.

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))
findNL(annotatedSpeclist, 212.009)

---

Match one spectrum against a set of spectra

Description

getSimilarities calculates the similarities of one spectrum or neutral loss pattern to a set of other spectra or neutral loss patterns.

Usage

getSimilarities(spec, speclist, type = c("spectrum", "neutral_losses"),
    hits_only = FALSE)

Arguments

spec	The spectrum to be compared to other spectra. Can be either an object of class MS2spectrum or a two-column numerical matrix that contains fragment mass-to-charge ratios in the first and intensities in the second column.
speclist	The set of spectra to which spec is to be compared. Must be a list where every entry is an object of class MS2spectrum. Can be generated from an mzXML file with extractMS2spectra and mergeMS2spectra or constructed using new("MS2spectrum", ...) for every list entry (see vignette for details).
type	Specifies whether MS2 spectra or neutral loss patterns are to be compared. Must be either 'spectrum' (default) or 'neutral_losses'.
hits_only	Logical that indicates whether the result should contain only similarities greater than zero.

Value

A named vector with similarities of spec to all spectra or neutral loss patterns in speclist.

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))
getSimilarities(annotatedSpeclist[[137]],
                annotatedSpeclist, hits_only = TRUE)

---

Access individual spectra from a list of spectra by various slot entries

Description

As accessing S4 objects within lists is not trivial, getSpectrum can be used to access individual or several MS2spectrum objects by their slot entries.

Usage

getSpectrum(featlist, slot, what, mz.tol = 1e-05, rt.tol = 30)

Arguments

featlist	a list that contains only objects of class MS2spectrum
slot	The slot to be searched (invalid slot arguments will produce errors). Possible values are: 'id' 'annotation' 'precursor' (m/z of precursor ion) 'rt' (retention time of precursor)
what	the search term or number, must be character for 'id' and 'annotation' and numeric for 'precursor' and 'rt' See vignette for examples.
mz.tol	the tolerance used for precursor ion *m/z* searches, defaults to 1E-05 (+/- 10ppm)
rt.tol	the tolerance used for precursor ion retention time searches, defaults to 30s; high values can be used to specify retention time ranges (see vignette for example)

Value

If the only one spectrum matches the search criteria, the output is an object of class MS2spectrum; if more than one spectrum matches, the output is a list of MS2spectrum objects.

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

getSpectrum(annotatedSpeclist, "annotation", "pyocyanin")

getSpectrum(annotatedSpeclist, "id", "M244.17T796.4")

getSpectrum(annotatedSpeclist, "precursor", 286.18, mz.tol = 1E-03)

six_eight <- getSpectrum(annotatedSpeclist, "rt", 420, rt.tol = 60)

---

Generate cluster dendrogram or heatmap from spectral similarity data

Description

HCplot() performs hierarchical clustering of spectral similarity data using average linkage as agglomeration criterion like HCtbl and generates either a circular dendrogram or a combination of dendrogram and heatmap.

Usage

HCplot(distmat, h = 0.95, type = c("dendrogram", "heatmap"), ...)

Arguments

distmat	A distance matrix as generated by distanceMatrix.
h	Height where the tree is to be cut, defaults to 0.95. See cutree for details.
type	Specifies which visualisation is to be generated: "dendrogram" (default) for a circular dendrogram or "heatmap" for a combination of dendrogram and heatmap.
...	Additional graphical parameters passed to plot.phylo (for type = "dendrogram") or gplots::heatmap.2 (for type = "heatmap")

Value

A plot as specified by type.

Examples

load(file = system.file("extdata",
    "distmat.RData",
    package = "CluMSIDdata"))

HCplot(distmat[1:50,1:50], h = 0.8, type = "heatmap")

---

Hierarchical clustering of spectral similarity data

Description

HCtbl() performs hierarchical clustering of spectral similarity data using average linkage as agglomeration criterion.

Usage

HCtbl(distmat, h = 0.95)

Arguments

distmat	A distance matrix as generated by distanceMatrix.
h	Height where the tree is to be cut, defaults to 0.95. See cutree for details.

Value

A data.frame with name and cluster ID for each feature in distmat.

See Also

HCplot

Examples

load(file = system.file("extdata",
    "distmat.RData",
    package = "CluMSIDdata"))

my_HCtbl <- HCtbl(distmat[1:50,1:50], h = 0.8)

---

Multidimensional scaling of spectral similarity data

Description

MDSplot() is used to generate multidimensional scaling plots from spectral similarity data. An interactive visualisation can be produced using plotly.

Usage

MDSplot(distmat, interactive = FALSE, highlight_annotated = FALSE, ...)

Arguments

distmat	A distance matrix as generated by distanceMatrix.
interactive	Logical, defaults to FALSE. If TRUE, an interactive visualisation is generated using plotly.
highlight_annotated	Logical, defaults to FALSE. If TRUE, points for features for which an annotation was added before using distanceMatrix are highlighted by red colour, while other points are grey in the MDS plot.
...	Additional arguments passed to geom_point(), e.g. pch, size or alpha.

Value

An MDS plot generated with the help of cmdscale, ggplot and, if interactive, ggplotly.

Examples

load(file = system.file("extdata",
    "distmat.RData",
    package = "CluMSIDdata"))

MDSplot(distmat, highlight_annotated = TRUE)

---

Merge MS2 spectra with or without external peak table

Description

mergeMS2spectra is used to merge MS2 spectra that come from the same precursor. It does so either by grouping spectra of the same precursor m/z that fall into a defined retention time window (rt_tolerance) or by grouping spectra to peaks from an externally supplied peak table. Please see the vignette for more details.

Usage

mergeMS2spectra(ms2list, mz_tolerance = 1e-05, rt_tolerance = 30,
    peaktable = NULL, exclude_unmatched = FALSE)

Arguments

ms2list	A list of MS2spectrum objects to be merged.
mz_tolerance	The m/z tolerance to be used for merging, default is 1e-5, i.e. +/- 10ppm. If the mass-to-charge ratios of two peaks differ less than mz_tolerance, they are assumed to have the same m/z
rt_tolerance	The retention time tolerance used for merging features. If used without a peak table, rt_tolerance is the maximum retention time difference between to subsequent spectra of the same precursor m/z with which they are still assumed to belong to the same feature If used with an external peak table, rt_tolerance is the maximum retention time difference between a spectrum and a peak in the peak table with which the spectrum is still considered to belong to that peak.
peaktable	An external peak table, e.g. from XCMS, that serves as a template for grouping spectra. The peaktable must be a three-column data.frame with feature ID, m/z and retention time for each peak/feature.
exclude_unmatched	If an external peak table is used: Should spectra that do not match to any peak/feature in the peak table be exclude from the resulting list?

Value

A merged list of MS2spectrum objects.

Examples

my_spectra <- extractMS2spectra(MSfile = system.file("extdata",
                                "PoolA_R_SE.mzXML",
                                package = "CluMSIDdata"),
                                min_peaks = 4, RTlims = c(0,5))

my_merged_spectra <- mergeMS2spectra(my_spectra, rt_tolerance = 20)

---

A custom S4 class for MS2 spectra, neutral loss patterns and respective metainformation

Description

A custom S4 class for MS2 spectra, neutral loss patterns and respective metainformation

Usage

## S4 method for signature 'MS2spectrum'
show(object)

## S4 method for signature 'MS2spectrum'
precursorMz(object)

## S4 method for signature 'MS2spectrum'
rtime(object)

## S4 method for signature 'MS2spectrum'
intensity(object)

## S4 method for signature 'MS2spectrum'
mz(object)

## S4 method for signature 'MS2spectrum,ANY'
peaksCount(object)

Arguments

object

An object of class MS2spectrum

Value

Prints information from the object slots with exception of 'spectrum' and 'neutral_losses' where only a summary is given.

Methods (by generic)

• show: A show generic for MS2spectra.

• precursorMz: Method forMSnbase::precursorMz for MS2spectrum objects. Accesses precursor slot and returns precursor m/z as a numeric.

• rtime: Method forMSnbase::rtime for MS2spectrum objects. Accesses rt slot and returns retention time as a numeric.

• intensity: Method forMSnbase::intensity for MS2spectrum objects. Accesses spectrum slot and returns the intensity column as a numeric vector.

• mz: Method forMSnbase::mz for MS2spectrum objects. Accesses spectrum slot and returns the m/z column as a numeric vector.

• peaksCount: Method forMSnbase::mz for MS2spectrum objects. Accesses spectrum slot and returns the number of peaks as a numeric.

Slots

id

a character string similar to the ID used by XCMSonline or the ID given in a predefined peak list

annotation

a character string containing a user-defined annotation, defaults to empty

precursor

(median) m/z of the spectrum's precursor ion

rt

(median) retention time of the spectrum's precursor ion

polarity

the ionisation polarity, "positive" or "negative"

spectrum

the actual MS2 spectrum as two-column matrix (column 1 is (median) m/z, column 2 is (median) intensity of the product ions)

neutral_losses

a neutral loss pattern generated by subtracting the product ion mass-to-charge ratios from the precursor m/z in a matrix format analogous to the spectrum slot

---

Correlation network from spectral similarity data

Description

networkplot() is used to generate correlation networks from spectral similarity data. An interactive visualisation can be produced using plotly.

Usage

networkplot(distmat, interactive = FALSE, show_labels = FALSE,
    label_size = 1.5, highlight_annotated = FALSE,
    min_similarity = 0.1, exclude_singletons = FALSE)

Arguments

distmat	A distance matrix as generated by distanceMatrix.
interactive	Logical, defaults to FALSE. If TRUE, an interactive visualisation is generated using plotly.
show_labels	Logical, defaults to FALSE. If TRUE, feature IDs are printed as labels in the network plot. Argument has no effect if interactive is TRUE (because in this case, labels are displayed on mouse-over).
label_size	Numeric, defaults to 1.5. If show_labels is TRUE and interactive is FALSE, label_size defines the size of labels in the plot.
highlight_annotated	Logical, defaults to FALSE. If TRUE, points for features for which an annotation was added before using distanceMatrix are highlighted by red colour, while other points are grey in the network plot.
min_similarity	Numeric, defaults to 0.1. The minimum spectral contrast angle (seecossim) that is considered a spectral similarity and hence a connection in the network.
exclude_singletons	Logical, defaults to FALSE. If TRUE, features that have no connection to any other feature will not be displayed in the network plot.

Value

A network plot generated with the help of network, ggnet2 and, if interactive, ggplotly. Edge weights correspond to spectral similarities.

Examples

load(file = system.file("extdata",
    "distmat.RData",
    package = "CluMSIDdata"))

networkplot(distmat[1:50,1:50], show_labels = TRUE,
                exclude_singletons = TRUE)

---

Visualisation of density-based clustering of spectral similarity data

Description

OPTICSplot() performs density-based clustering of spectral similarity data using the OPTICS algorithm like OPTICStbl and creates a reachability distance plot.

Usage

OPTICSplot(distmat, eps = 10000, minPts = 3, eps_cl = 0.5, ...)

Arguments

distmat	A distance matrix as generated by distanceMatrix.
eps	OPTICS parameters, see optics.
minPts	OPTICS parameters, see optics.
eps_cl	The reachability distance used for cluster determination, see extractDBSCAN.
...	Additional graphical parameters to be passed to plot()

Details

The function internally uses optics and extractDBSCAN from the dbscan package.

Value

A reachability distance plot as visualisation of OPTICS clustering, see codeextractDBSCAN.

See Also

OPTICStbl

Examples

load(file = system.file("extdata",
    "distmat.RData",
    package = "CluMSIDdata"))

OPTICSplot(distmat[1:50,1:50], eps_cl = 0.7)

---

Density-based clustering of spectral similarity data

Description

OPTICStbl() performs density-based clustering of spectral similarity data using the OPTICS algorithm.

Usage

OPTICStbl(distmat, eps = 10000, minPts = 3, eps_cl = 0.5)

Arguments

distmat	A distance matrix as generated by distanceMatrix.
eps, minPts	OPTICS parameters, see optics.
eps_cl	The reachability distance used for cluster determination, see extractDBSCAN.

Details

The function internally uses optics and extractDBSCAN from the dbscan package.

Value

A data.frame with feature name, cluster ID and OPTICS order for each feature in distmat.

See Also

OPTICSplot

Examples

load(file = system.file("extdata",
    "distmat.RData",
    package = "CluMSIDdata"))

my_OTPICStbl <- OPTICStbl(distmat[1:50,1:50], eps_cl = 0.7)

---

A custom S4 class for MS1 pseudospectra and respective metainformation

Description

A custom S4 class for MS1 pseudospectra and respective metainformation

Slots

id

a the "pcgroup" number assigned by CAMERA

annotation

a character string containing a user-defined annotation, defaults to empty

rt

(median) retention time of the ions contained in the pseudospectrum

spectrum

the actual MS1 pseudospectrum as two-column matrix (column 1 is (median) m/z, column 2 is (median) intensity of the ions)

---

Create a basic plot of MS2 spectra

Description

specplot creates a very basic plot of MS2 spectra from MS2spectrum or pseudospectrum objects.

Usage

specplot(spec, ...)

Arguments

spec	An object of class MS2spectrum or pseudospectrum
...	Additional graphical parameters to be passed to plot()

Value

A plot of the MS2 spectrum saved in the spectrum slot of spec.

Examples

load(file = system.file("extdata",
    "annotatedSpeclist.RData",
    package = "CluMSIDdata"))

specplot(annotatedSpeclist[[1]])

---

Separate spectra with different polarities from the same run

Description

Using splitPolarities, spectra with different polarities from the same run can be separated, e.g. when processing spectra recorded with polarity-switching.

Usage

splitPolarities(ms2list, polarity = c("positive", "negative"))

Arguments

ms2list	A list of MS2spectrum objects as produced by extractMS2spectra.
polarity	The polarity of spectra to be analysed, must be "positive" or "negative".

Value

A list of MS2spectrum objects that contains only spectra with the given polarity.

Examples

my_spectra <- extractMS2spectra(MSfile = system.file("extdata",
                                "PoolA_R_SE.mzXML",
                                package = "CluMSIDdata"),
                                min_peaks = 4, RTlims = c(0,5))

my_positive_spectra <- splitPolarities(my_spectra, "positive")

---

Write feature information from list of MS2spectrum objects

Description

writeFeaturelist uses featureList to generate a data.frame that contains feature ID, precurosur m/z and retention time for all features contained in a list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList and writes it to a csv file.

Usage

writeFeaturelist(featlist, filename = "pre_anno.csv")

Arguments

featlist	A list of MS2spectrum objects as produced by extractMS2spectra and mergeSpecList
filename	The desired file name of the csv file, default is "pre_anno.csv"

Details

Although originally designed for lists of MS2spectrum objects, the function also works with lists of pseudospectrum objects. In this case, NA is given for precursor m/z.

Value

A csv file that contains feature ID, precurosur m/z and retention time. The file has a header but no row names and is separated by ','.

Examples

load(file = system.file("extdata",
    "featlist.RData",
    package = "CluMSIDdata"))

writeFeaturelist(featlist, filename = "pre_anno.csv")

---