| Title: | Peak Picking for High Resolution Mass Spectrometry Data |
|---|---|
| Description: | Sequential partitioning, clustering and peak detection of centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw data plot. |
| Authors: | Martin Loos |
| Maintainer: | Martin Loos <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.7 |
| Built: | 2024-11-09 02:35:26 UTC |
| Source: | https://github.com/blosloos/enviPick |
Peak picking for centroided and baseline-corrected high-resolution LC-MS data.mzXML. Built on a three-step approach of (1) data partitioning, (2) unsupervised clustering of extracted ion chromatograms (EIC) and (3) shape-independent peak detection wihtin individual EICs. Interactive plot access to all results and the underlying raw measurements. Browser UI for non-R users. Batch processing.
| Package: | enviPick |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2014-14-07 |
| License: | GPL-2 |
After initial upload of an .mzXML file with readMSdata, above steps (1) to (3) are
calculated by mzagglom, mzclust and mzpick, respectively.
The wrapper for joint upload and processing is enviPickwrap.
The raw data and the results of each step, a so-called MSlist object, can be viewed by plotMSlist, producing an
interactive plot that conveniently offers zoom, drag and select functionality and easy identification of individual partitions, EIC cluster or
peaks. Batch processing can be done via enviPickbatch.
To export a peak list from an MSlist object, use writePeaklist.
For converting LC-HRMS measurement files from various vendor formats or .mzML to centroided .mzXML we strongly recommend the MSConvert tool from ProteoWizard; for centroidization choose Filters -> Peak Picking -> Prefer Vendor -> Add.
This package has only been tested on HIGH-RESOLUTION Thermo Orbitrap and QExactive measurements processed (centroided) with ProteoWizard's MSConvert. It may not give satisfying results for chromatograms affected by mass shifts from centroid-centroid interferences prevalent at low resolutions.
In the package context, peak picking refers to extracting individual ion chromatograms (EICs) from centroided data and identifying peaks in these EICs. In the ProteoWizard MSConvert context, peak picking refers to identifying individual peaks within single HRMS scans, alias centroidization.
Martin Loos Maintainer: Martin Loos <[email protected]>
Loos, M. (XXXX). Extraction of ion chromatograms by unsupervised clustering of high-resolution mass spectrometry data. Some Journal. Sometime.
ProteoWizard: Open Source Software for Rapid Proteomics Tools Development Darren Kessner; Matt Chambers; Robert Burke; David Agus; Parag Mallick Bioinformatics 2008 http://proteowizard.sourceforge.net/
readMSdata
mzagglom
mzpart
mzclust
mzpick
plotMSlist
enviPickwrap
enviPickbatch
## Not run: ################################################## # (1) Define path to an LC-HRMS .mzML file (not provided with package): filepath.mzML<-"C:/.../2012_07_01.mzML" # (2) Initialize an MSlist object and load this .mzML file into it: MSlist<-readMSdata(filepath.mzML, MSlevel=c(1)) # (3) Partition the measurements now available in MSlist: MSlist<-mzagglom(MSlist,dmzgap=10,ppm=TRUE,drtgap=500,minpeak=4,maxint=1E7) # (4) EIC clustering of the partitions now available in MSlist: MSlist<-mzclust(MSlist,dmzdens=5,ppm=TRUE,drtdens=120,minpeak=4) # (5) Peak picking within the EIC clusters now available in MSlist: MSlist<-mzpick(MSlist, minpeak = 4, drtsmall = 50, drtfill = 10, drttotal = 200, recurs = 4, weight = 2, SB = 3, SN=2, minint = 1E4, maxint = 1e+07, ended = 2) # (6) Export a peak list now available in MSlist: writePeaklist(MSlist,"directory","filename") # (7) View your partitioning / EIC clustering / peak picking results: plotMSlist(MSlist,ppmbar=10); ################################################## ################################################## # Do above steps (1) to (5) in one wrap, then export a peak list: MSlist<-enviPickwrap( filepath.mzML, MSlevel=c(1), dmzgap=10, dmzdens=5, ppm=TRUE, drtgap=1000, drtsmall=20, drtdens=250, drtfill=10, drttotal=200, minpeak=4, recurs=10, weight=2, SB=3, SN=2, minint=10E4, maxint=10E6, ended=2, progbar=TRUE ) writePeaklist(MSlist,"directory","filename") ################################################## ## End(Not run)## Not run: ################################################## # (1) Define path to an LC-HRMS .mzML file (not provided with package): filepath.mzML<-"C:/.../2012_07_01.mzML" # (2) Initialize an MSlist object and load this .mzML file into it: MSlist<-readMSdata(filepath.mzML, MSlevel=c(1)) # (3) Partition the measurements now available in MSlist: MSlist<-mzagglom(MSlist,dmzgap=10,ppm=TRUE,drtgap=500,minpeak=4,maxint=1E7) # (4) EIC clustering of the partitions now available in MSlist: MSlist<-mzclust(MSlist,dmzdens=5,ppm=TRUE,drtdens=120,minpeak=4) # (5) Peak picking within the EIC clusters now available in MSlist: MSlist<-mzpick(MSlist, minpeak = 4, drtsmall = 50, drtfill = 10, drttotal = 200, recurs = 4, weight = 2, SB = 3, SN=2, minint = 1E4, maxint = 1e+07, ended = 2) # (6) Export a peak list now available in MSlist: writePeaklist(MSlist,"directory","filename") # (7) View your partitioning / EIC clustering / peak picking results: plotMSlist(MSlist,ppmbar=10); ################################################## ################################################## # Do above steps (1) to (5) in one wrap, then export a peak list: MSlist<-enviPickwrap( filepath.mzML, MSlevel=c(1), dmzgap=10, dmzdens=5, ppm=TRUE, drtgap=1000, drtsmall=20, drtdens=250, drtfill=10, drttotal=200, minpeak=4, recurs=10, weight=2, SB=3, SN=2, minint=10E4, maxint=10E6, ended=2, progbar=TRUE ) writePeaklist(MSlist,"directory","filename") ################################################## ## End(Not run)
Given a folder of .mzXML input files and subsequent processing, .csv peak tables with picked peaks are written to an output folder
enviPickbatch(folderin, folderout, MSlevel=c(1), dmzgap=15, dmzdens=4, ppm=TRUE, drtgap=500, drtsmall=20, drtdens=250, drtfill=10, drttotal=200, minpeak=4, recurs=10, weight=2, SB=3, SN=2, minint=1E5, maxint=1E7, ended=2, ion_mode=FALSE, progbar=FALSE)enviPickbatch(folderin, folderout, MSlevel=c(1), dmzgap=15, dmzdens=4, ppm=TRUE, drtgap=500, drtsmall=20, drtdens=250, drtfill=10, drttotal=200, minpeak=4, recurs=10, weight=2, SB=3, SN=2, minint=1E5, maxint=1E7, ended=2, ion_mode=FALSE, progbar=FALSE)
folderin |
Folder containing .mzXML input files |
folderout |
Destination folder for .csv peak tables |
MSlevel |
numeric 1 (MS) or 2 (MS-MS), |
dmzgap |
m/z gap width for partitioning, |
dmzdens |
Maximum measurement deviation (+/-) of m/z within an EIC, |
ppm |
|
drtgap |
RT gap width for partitioning, |
drtsmall |
Peak definition - RT window of a peak; cp. |
drtdens |
Maximum length of EICs, |
drtfill |
RT gap length to be filled, |
drttotal |
Maximum RT length of a single peak, |
minpeak |
Peak definition - minimum number of measurements per peak to found in windows of |
recurs |
Maximum number of peaks within one EIC, |
weight |
Weight for assigning measurements to a peak, |
SB |
Signal-to-base ratio, |
SN |
Signal-to-noise ratio, |
minint |
Minimum intensity of a peak, |
maxint |
Peaks above this intensity are alwas included, regardless of checks for |
ended |
Within the peak detection recursion set by argument |
ion_mode |
"positive" or "negative" ionization. Otherwise set to FALSE, see details. |
progbar |
Show a progress bar (TRUE or FALSE)? May only work under Windows OS. |
For further details on the parameter settings, please refer to the arguments of the underlying functions readMSdata,
mzagglom, mzclust and mzpick.
ion_mode allows to filter scans of a specific polarity from .mzXML files, if not set to FALSE; useful for
files acquired under polarity switching.
Folder with .csv peak tables, each containing columns with: m/z (mean m/z of peak measurements), var_m/z (m/z variation of peak measurements), max_int (base-line corrected maximum intensity), sum_int (sum of all base-line corrected peak measurement intensities), RT (retention time at maximum intensity), minRT (start peak RT), maxRT (end peak RT), peak# (peak ID number), EIC# (EIC ID number), Score (not yet implemented)
Martin Loos
readMSdata
mzagglom
mzclust
mzpick
plotMSlist
A wrapper combining data upload, partitioning, EIC clustering and EIC peak detection from readMSdata,
mzagglom, mzclust and mzpick.
enviPickwrap(filepath.mzXML, MSlevel=c(1), dmzgap=15, dmzdens=4, ppm=TRUE, drtgap=500, drtsmall=20, drtdens=250, drtfill=10, drttotal=200, minpeak=4, recurs=3, weight=2, SB=3, SN=2, minint=1E5, maxint=1E7, ended=2, ion_mode=FALSE, progbar=FALSE)enviPickwrap(filepath.mzXML, MSlevel=c(1), dmzgap=15, dmzdens=4, ppm=TRUE, drtgap=500, drtsmall=20, drtdens=250, drtfill=10, drttotal=200, minpeak=4, recurs=3, weight=2, SB=3, SN=2, minint=1E5, maxint=1E7, ended=2, ion_mode=FALSE, progbar=FALSE)
filepath.mzXML |
Path to the .mzXML file to be read, |
MSlevel |
numeric 1 (MS) or 2 (MS-MS), |
dmzgap |
m/z gap width for partitioning, |
dmzdens |
Maximum measurement deviation (+/-) of m/z within an EIC, |
ppm |
|
drtgap |
RT gap width for partitioning, |
drtsmall |
Peak definition - RT window of a peak; cp. |
drtdens |
Maximum length of EICs, |
drtfill |
RT gap length to be filled, |
drttotal |
Maximum RT length of a single peak, |
minpeak |
Peak definition - minimum number of measurements per peak to found in windows of |
recurs |
Maximum number of peaks within one EIC, |
weight |
Weight for assigning measurements to a peak, |
SB |
Signal-to-base ratio, |
SN |
Signal-to-noise ratio, |
minint |
Minimum intensity of a peak, |
maxint |
Peaks above this intensity are alwas included, regardless of checks for |
ended |
Within the peak detection recursion set by argument |
ion_mode |
"positive" or "negative" ionization. Otherwise set to FALSE, see details. |
progbar |
Show a progress bar (TRUE or FALSE)? May only work under Windows OS. |
For further details on the parameter settings, please refer to the arguments of the underlying functions readMSdata,
mzagglom, mzclust and mzpick.
ion_mode allows to filter scans of a specific polarity from .mzXML files, if not set to FALSE; useful for
files acquired under polarity switching.
MSlist
State |
MSlist[[1]]: tags the individual steps the MSlist has undergone for peak picking. |
Parameters |
MSlist[[2]]: saves the parameter settings. |
Results |
MSlist[[3]]: saves the result summary values |
Scans |
MSlist[[4]]: matrix with raw measurements (m/z, intensity, RT) and tags for partitions, EIC cluster or individual peaks. |
Partition_Index |
MSlist[[5]]: Index assigning partitions to sections in the raw measurment of MSlist[[4]]. Required for fast (random) access, e.g., plotting. |
EIC_index |
MSlist[[6]]: Index assigning EIC clusters to sections in the raw measurment of MSlist[[4]]. Required for fast access. |
Peak_index |
MSlist[[7]]: Index assigning picked peaks to sections in the raw measurment of MSlist[[4]]. Required for fast access. |
Peaklist |
MSlist[[8]]: Final peak list, cp. |
Martin Loos
readMSdata
mzagglom
mzclust
mzpick
plotMSlist
Agglomerative partitioning of LC-HRMS measurements.
Preparatory step for mzclust and mzpick.
Requires an MSlist initilialized by readMSdata as input.
mzagglom(MSlist, dmzgap = 10, ppm = TRUE, drtgap = 500, minpeak = 4, maxint=1E7, progbar=FALSE)mzagglom(MSlist, dmzgap = 10, ppm = TRUE, drtgap = 500, minpeak = 4, maxint=1E7, progbar=FALSE)
MSlist |
MSlist generated by |
dmzgap |
m/z gap width for partitioning |
ppm |
|
drtgap |
RT gap width for partitioning |
minpeak |
Minimum number of measurements in a partition |
maxint |
Measurements equal or above this intensity will be retained even if ranging below |
progbar |
For debugging, ignore |
Partitioning of the full set of measurements into subsets is necessary to speed up the clustering procedure of mzclust.
To this end, an agglomerative partitioning approach is used, combining measurements that are linked by values smaller than drtgap and
dmzgap into single subsets. No measurements of two different subsets can be closer than drtgap and dmzgap to each other.
Returns the argument MSlist, with entries made:
Parameters |
MSlist[[2]]: saves the parameter settings. |
Scans |
MSlist[[4]]: matrix with raw measurements and tags resorted for partitions. |
Partition_Index |
MSlist[[5]]: Index assigning partitions to sections in the raw measurement of MSlist[[4]]; required for fast (random) access. |
Do not set minpeak bigger than its counterpart in mzclust or mzpick.
Too complicated? Then rather use enviPickwrap for adjusting all function arguments.
Despite optimized code, this function has a potential to run for a intolerable long time or out of memory if (a) the parameters are set wrongly, (b) the .mzML/.mzXML-file was not centroided or
(c) the underlying data is inadequate for this peak picker.
With regards to (a), do not assume gaps being larger than actually present. Instead, use plotMSlist to have a look at your
data contained in MSlist after upload with readMSdata.
Martin Loos
Based on the measurement partitions generated by mzagglom,
extracted ion chromatograms (EICs) are assigned by a clustering procedure. Preparatory step for mzpick.
mzclust(MSlist,dmzdens=10,ppm=TRUE,drtdens=60,minpeak=4,maxint=1E6, progbar=FALSE,merged=TRUE,from=FALSE,to=FALSE )mzclust(MSlist,dmzdens=10,ppm=TRUE,drtdens=60,minpeak=4,maxint=1E6, progbar=FALSE,merged=TRUE,from=FALSE,to=FALSE )
MSlist |
MSlist returned by |
dmzdens |
Maximum measurement deviation (+/-) of m/z from its mean within each EIC |
ppm |
|
drtdens |
Retention time (RT) tolerance for clustering; defined as (+/-) time units relative to the lowest and highest RT value in each cluster |
minpeak |
Minimum number of measurements expected in an EIC |
maxint |
EIC cluster with measurements above this intensity are kept, even if they do not fulfill |
progbar |
For debugging, ignore |
merged |
Merge EIC cluster of comparable m/z (TRUE or FALSE)? |
from |
For debugging, ignore |
to |
For debugging, ignore |
Within individual partitions calculated by mzagglom, an unsupervised clustering of measurements to individual ion chromatograms (EICs) is performed.
For this purpose, a first EIC cluster is initialized with the most intense measurement, given an m/z uncertainty of 2*dmzdens.
Along decreasing intensities, all other measurements are then sequentially either assigned to this cluster or used to define new clusters.
For assignment, measurements must range both within the current tolerances of dmzdens and drtdens of an existing cluster.
If several cluster are eligible for assignment, the one with the smallest mass difference between measurement m/z and cluster mean m/z will be used.
Each time a new assignment to an existing cluster is made, its m/z estimate can be improved, i.e., the dmzdens tolerance around its mean m/z gradually
shrinks from 2*dmzdens to dmzdens. In addition, dmzdens is used to update the RT tolerance of a cluster at each assignment.
With no measurements left, EIC clusters nested in m/z are then merged, relative to the m/z boundaries of the most intense cluster and stepwise along increasing mean m/z differences.
Finally, EIC cluster are filtered to fulfill either minpeak or maxint.
Returns the argument MSlist, with entries made:
Parameters |
MSlist[[2]]: saves the parameter settings. |
Scans |
MSlist[[4]]: matrix with raw measurements and tags resorted for EIC clusters within the partition subsets. |
EIC_index |
MSlist[[6]]: Index assigning EIC cluster to sections in the raw measurement of MSlist[[4]]; required for fast (random) access. |
Too small values for dmzdens or too large drtdens may cause erratic EICs cluster
Martin Loos
Loos, M. (XXXX). Extraction of ion chromatograms by unsupervised clustering of high-resolution mass spectrometry data. Some Journal. Sometime.
Divisive recursive partition of LC-HRMS measurements.
Preparatory step for mzclust and mzpick;
altenative to mzagglom.
Requires an MSlist initilialized by readMSdata as input.
mzpart(MSlist, dmzgap = 10, drtgap = 500, ppm = TRUE, minpeak = 4, peaklimit = 2500, cutfrac = 0.1, drtsmall=50, progbar = FALSE, stoppoints = 2e+05)mzpart(MSlist, dmzgap = 10, drtgap = 500, ppm = TRUE, minpeak = 4, peaklimit = 2500, cutfrac = 0.1, drtsmall=50, progbar = FALSE, stoppoints = 2e+05)
MSlist |
MSlist generated by |
dmzgap |
m/z gap width for partitioning |
drtgap |
RT gap width for partitioning |
ppm |
|
minpeak |
Minimum number of measurements in a partition |
peaklimit |
Maximum number of measurements in a partition |
cutfrac |
Fraction of low density measurements to be discarded |
drtsmall |
RT tolerance used to estimate density |
progbar |
For debugging, ignore |
stoppoints |
For debugging, ignore |
This function searchs recursively for gaps in retention time (RT) and m/z in the LC-HRMS measurements and thus partitions (and resorts) the matrix contained in MSlist[[4]].
If neither partitioning by RT nor by m/z results in a small enough partition of <= peaklimit measurements, a fraction cutfrac of
lowest-density measurements is discarded and the partition procedure resumed. Measurement-wise density is based on a gaussian kernel density estimate
scaled to dmzgap and drtsmall, i.e., to the local neighbourhood of each measurement.
Partitioning is necessary to speed up the clustering procedure of mzclust. Hence, there is a trade-off:
large values of peaklimit leads to faster execution of
mzpart but to slower computation of mzclust and vice versa.
Returns the argument MSlist, with entries made:
Parameters |
MSlist[[2]]: saves the parameter settings. |
Scans |
MSlist[[4]]: matrix with raw measurements and tags resorted for partitions. |
Partition_Index |
MSlist[[5]]: Index assigning partitions to sections in the raw measurement of MSlist[[4]]; required for fast (random) access. |
Do not set minpeak bigger than its counterpart in mzclust or mzpick.
Too complicated? Then rather use enviPickwrap for adjusting all function arguments.
Despite optimized code, this function has a potential to run for a intolerable long time or out of memory if (a) the parameters are set wrongly, (b) the .mzML/.mzXML-file was not centroided or
(c) the underlying data is inadequate for this peak picker.
With regards to (a), do not assume gaps being larger than actually present. Instead, use plotMSlist to have a look at your
data contained in MSlist after upload with readMSdata;
set progbar=TRUE to monitor where a function fails. Once settled, set progbar=FALSE for faster execution.
To avoid running out of memory, stoppoints sets the maximum number of measurements that can be handled in the routines to delete
those of lowest intensity (in cases where peaklimit cannot be reached by partitioning by dmzgap and drtgap alone).
If above stoppoints, execution aborts.
Martin Loos
Peak-picking within individual EIC cluster formed by mzclust without assuming a certain peak shape.
Includes a baseline subtraction step.
mzpick(MSlist, minpeak = 4, drtsmall = 20, drtfill = 10, drttotal = 200, recurs = 4, weight = 2, SB = 3, SN=2, minint = 1E4, maxint = 1e+07, ended = 2, progbar = FALSE, from = FALSE, to = FALSE)mzpick(MSlist, minpeak = 4, drtsmall = 20, drtfill = 10, drttotal = 200, recurs = 4, weight = 2, SB = 3, SN=2, minint = 1E4, maxint = 1e+07, ended = 2, progbar = FALSE, from = FALSE, to = FALSE)
MSlist |
An MSlist returned by |
minpeak |
Peak definition - minimum number of measurements required within the RT window of |
drtsmall |
Peak definition - RT window of a peak; cp. |
drtfill |
Maximum RT gap length to be filled, cp. details |
drttotal |
Peak definition - Maximum RT length of a single peak |
recurs |
Maximum number of peaks within one EIC, cp. details |
weight |
Weight for assigning measurements to a peak, cp. details |
SB |
Peak definition - signal-to-base ratio |
SN |
Peak definition - signal-to-noise ratio |
minint |
Peak definition - minimum intensity of a peak |
maxint |
Peaks above this intensity are always retained, regardless of other checks |
ended |
Within the peak detection recursion set by argument |
get_mass |
Use default mean (mean) or intensity-weighted mean (wmean) of centroids to derive peak m/z? |
progbar |
For debugging, ignore |
from |
For debugging, ignore |
to |
For debugging, ignore |
In a first step, RT gaps between measurements in an EIC not larger than drtfill are filled by linear interpolation.
Subsequently, peaks are assigned over a number of recurs recursions not interrupted by more than ended times of failed peak detections.
At each recursion, the most intense EIC measurement not yet assigned to a peak is selected as peak apex and neighbouring unassigned measurements at lower and higher RT are evaluated
for forming the peak. To this end, increases (lower RT) and decreases (higher RT) in intensity of consecutive measurements over a maximum RT width of drtdens are summed and
penalized by a factor of weight for intensity reversions. The measurements with optimum values are then selected to define the start and end
measurement of the peak.
Thereupon, the candidate peak is checked to
(a) have at least minpeaks within a RT window of drtsmall,
(b) be larger than the minimum peak intensity minint and
(c) have a minimum SB ratio (the ratio between the most intense measurement and the mimimum intensity of the first or last peak measurement).
Candidate peaks failing in any of the aspects (a) to (c) are discarded (adding to ended), unless they are higher in intensity than maxint.
Next, all measurements assigned to peaks are removed from the EIC and the resulting gaps linearly interpolated and smoothed by a moving window average to form a baseline. The latter is then subtracted from the assigned peaks.
In a last step, peaks are checked for their signal-to-noise SN ratio in relation to the baseline measurements (if present).
Herein, SN is defined as the ratio between the most intense (baseline-corrected) peak measurement and the median of the difference
between the non-peak measurements (if any) and the baseline.
Returns the argument MSlist, with entries made:
Parameters |
MSlist[[2]]: saves the parameter settings. |
Scans |
MSlist[[4]]: matrix with raw measurements and tags for picked peaks within EICs within partitions. |
Peak_index |
MSlist[[7]]: Index assigning picked peaks to sections in the raw measurment of MSlist[[4]]. Required for fast access. |
Peaklist |
MSlist[[8]]: matrix with picked peak characteristics, with columns: m/z (mean m/z of peak measurements), var_m/z (m/z variation of peak measurements), max_int (base-line corrected maximum intensity), sum_int (sum of all base-line corrected peak measurement intensities), RT (retention time at maximum intensity), minRT (start peak RT), maxRT (end peak RT), peak# (peak ID number), EIC# (EIC ID number) and Score (not yet implemented). |
ended must be smaller than recurs.
minpeak and drtsmall should be congruent in both and mzclust and mzpick.
Martin Loos
View your centroided LC-HRMS data and partitioning / clustering / peak-picking results. Monitor what peak-picking produces and if it fails; get a grip on optimal parameter settings from comparison with underlying raw data.
plotMSlist(MSlist, RTlimit = FALSE, mzlimit = FALSE, shiny = FALSE, ppmbar = 8)plotMSlist(MSlist, RTlimit = FALSE, mzlimit = FALSE, shiny = FALSE, ppmbar = 8)
MSlist |
An MSlist returned by |
RTlimit |
Initialize plot: two-element vector of lower and upper RT plot limits. Set to FALSE to view full data. |
mzlimit |
Initialize plot: two-element vector of lower and upper m/z limits. Set to FALSE to view full data. |
shiny |
For debugging. Ignore. |
ppmbar |
Size of m/z bar (in ppm) shown at large zoom |
For more help, use the help button in the interactive plot. Based on low-level R plot functionality!
MSlist may contain a lot of data; rendering of measurements for plotting may thus decrease in speed when zooming out or using the full-view mode.
Martin Loos
readMSdata
mzagglom
mzclust
mzpick
Initiates an MSlist object and reads LC-HRMS measurement data from .mzXML files.
readMSdata(filepath.mzXML, MSlevel=c(1), progbar=FALSE, minRT=FALSE, maxRT=FALSE, minmz=FALSE, maxmz=FALSE, ion_mode=FALSE)readMSdata(filepath.mzXML, MSlevel=c(1), progbar=FALSE, minRT=FALSE, maxRT=FALSE, minmz=FALSE, maxmz=FALSE, ion_mode=FALSE)
filepath.mzXML |
Path to the .mzXML file to be read |
MSlevel |
numeric 1 (MS) or 2 (MS-MS) |
progbar |
Show a progress bar (TRUE or FALSE)? Might only work in Windows OS |
minRT |
Filter for measurements with retention time >= minRT. Otherwise set to FALSE. |
maxRT |
Filter for measurements with retention time <= maxRT. Otherwise set to FALSE. |
minmz |
Filter for measurements with m/z >= minmz. Otherwise set to FALSE. |
maxmz |
Filter for measurements with m/z <= maxmz. Otherwise set to FALSE. |
ion_mode |
"positive" or "negative" ionization. Otherwise set to FALSE, see details. |
The return value, a so-called MSlist object, is a simple R list object that contains (a) the raw measurement data, (b) intermediate/final results of the peak picking procedure and (c) indices for random access, to be passed among functions. Peaks are nested in EIC clusters which in turn are nested in partitions which in turn are subsets of measurements; MSlist[[4]] is resorted accordingly during all peak picking steps.
Setting minRT, maxRT, minmz or maxmz allows you to filter your .mzML data.
On the one hand, this may be very useful if only being interested in certain ranges of an experiment.
On the other hand, this allows you to upload subset data of an experiment too large to be loaded into R at once.
ion_mode allows to filter scans of a specific polarity from .mzXML files, if not set to FALSE; useful for
files acquired under polarity switching.
MSlist
State |
MSlist[[1]]: tags the individual steps the MSlist has undergone. |
Parameters |
MSlist[[2]]: saves parameter settings. |
Results |
MSlist[[3]]: saves a result summary. |
Scans |
MSlist[[4]]: matrix with raw measurements (m/z, intensity, RT) and tags for partitions, EIC cluster and individual peaks. |
Partition_Index |
MSlist[[5]]: Index assigning partitions to sections in the raw measurment of MSlist[[4]]. Needed for fast (random) access during, e.g., plotting. |
EIC_index |
MSlist[[6]]: Index assigning EIC clusters to sections in the raw measurment of MSlist[[4]]. Required for fast access. |
Peak_index |
MSlist[[7]]: Index assigning picked peaks to sections in the raw measurment of MSlist[[4]]. Required for fast access. |
Peaklist |
MSlist[[8]]: Final peak list, cp. |
Use plotMSlist to check your data in MSlist for consistency at an early stage before further processing.
It is your responsibility to ensure your input files are centroided. If not, R may freeze and the peak picker will not return valid results.
Martin Loos
Run enviPick conveniently from a web browser-based user interface
webpick()webpick()
check webpage
May not work with Microsoft Internet Explorer; better choose a different default browser (e.g., Google Chrome).
Martin Loos
Given an MSlist object containing peak picking results from mzpick, export a peak table.csv.
writePeaklist(MSlist, directory, filename, overwrite = FALSE)writePeaklist(MSlist, directory, filename, overwrite = FALSE)
MSlist |
A MSlist object generated by |
directory |
Character string with the directory to write to |
filename |
Name of the .csv file to create |
overwrite |
TRUE/FALSE |
.csv table, with columns:
m/z (mean m/z of peak measurements), var_m/z (m/z variation of peak measurements), max_int (base-line corrected maximum intensity), sum_int (sum of all base-line corrected peak measurement intensities), RT (retention time at maximum intensity), minRT (start peak RT), maxRT (end peak RT), peak# (peak ID number), EIC# (EIC ID number), Score (not yet implemented)
Martin Loos