Evaluation of wastewater ozonation with mass spectrometry
Ricardo Cunha
cunha@iuta.de01 August, 2024
Source:vignettes/articles/demo_ozonation_project.Rmd
demo_ozonation_project.RmdIntroduction
In this article, we demonstrate how the StreamFind R package can be used to evaluate ozonation of secondary wastewater effluent (i.e., effluent of the aerated biological treatment) using mass spectrometry (MS). A set of 18 mzML files, representing blank, influent and effluent measurements in triplicate for both positive and negative ionization mode, are used.
basename(files) [1] "01_tof_ww_is_neg_blank-r001.mzML"
[2] "01_tof_ww_is_neg_blank-r002.mzML"
[3] "01_tof_ww_is_neg_blank-r003.mzML"
[4] "01_tof_ww_is_pos_blank-r001.mzML"
[5] "01_tof_ww_is_pos_blank-r002.mzML"
[6] "01_tof_ww_is_pos_blank-r003.mzML"
[7] "02_tof_ww_is_neg_influent-r001.mzML"
[8] "02_tof_ww_is_neg_influent-r002.mzML"
[9] "02_tof_ww_is_neg_influent-r003.mzML"
[10] "02_tof_ww_is_pos_influent-r001.mzML"
[11] "02_tof_ww_is_pos_influent-r002.mzML"
[12] "02_tof_ww_is_pos_influent-r003.mzML"
[13] "03_tof_ww_is_neg_o3sw_effluent-r001.mzML"
[14] "03_tof_ww_is_neg_o3sw_effluent-r002.mzML"
[15] "03_tof_ww_is_neg_o3sw_effluent-r003.mzML"
[16] "03_tof_ww_is_pos_o3sw_effluent-r001.mzML"
[17] "03_tof_ww_is_pos_o3sw_effluent-r002.mzML"
[18] "03_tof_ww_is_pos_o3sw_effluent-r003.mzML"The showcase will use the StreamFind main class MassSpecEngine, which encapsulates all tools required for parsing, storing, processing and visualizing MS data. Note that not all methods/functions will be shown as the demonstration focuses of the workflow to assess wastewater ozonation. For a complete overview of the StreamFind R package for mass spectrometry, the article StreamFind for mass spectrometry is recommended.
MassSpecEngine
The main R6 MassSpecEngine
class object is created using the MassSpecEngine$new(), as
shown below. The argument files can be used to add directly
the set of mzML files. Note that the original vendor files were
converted to mzML format using the convert_ms_files()
function, which uses the msConvert command line from ProteoWizard.
# Create a MassSpecEngine from mzML files
ms <- MassSpecEngine$new(files = files)
# Print in console a summary of the MassSpecEngine
ms
MassSpecEngine
name NA
author NA
file NA
date 2024-08-01 10:04:40.137931
Workflow empty
Analyses
analysis replicate blank
<char> <char> <char>
1: 01_tof_ww_is_neg_blank-r001 01_tof_ww_is_neg_blank <NA>
2: 01_tof_ww_is_neg_blank-r002 01_tof_ww_is_neg_blank <NA>
3: 01_tof_ww_is_neg_blank-r003 01_tof_ww_is_neg_blank <NA>
4: 01_tof_ww_is_pos_blank-r001 01_tof_ww_is_pos_blank <NA>
5: 01_tof_ww_is_pos_blank-r002 01_tof_ww_is_pos_blank <NA>
6: 01_tof_ww_is_pos_blank-r003 01_tof_ww_is_pos_blank <NA>
7: 02_tof_ww_is_neg_influent-r001 02_tof_ww_is_neg_influent <NA>
8: 02_tof_ww_is_neg_influent-r002 02_tof_ww_is_neg_influent <NA>
9: 02_tof_ww_is_neg_influent-r003 02_tof_ww_is_neg_influent <NA>
10: 02_tof_ww_is_pos_influent-r001 02_tof_ww_is_pos_influent <NA>
11: 02_tof_ww_is_pos_influent-r002 02_tof_ww_is_pos_influent <NA>
12: 02_tof_ww_is_pos_influent-r003 02_tof_ww_is_pos_influent <NA>
13: 03_tof_ww_is_neg_o3sw_effluent-r001 03_tof_ww_is_neg_o3sw_effluent <NA>
14: 03_tof_ww_is_neg_o3sw_effluent-r002 03_tof_ww_is_neg_o3sw_effluent <NA>
15: 03_tof_ww_is_neg_o3sw_effluent-r003 03_tof_ww_is_neg_o3sw_effluent <NA>
16: 03_tof_ww_is_pos_o3sw_effluent-r001 03_tof_ww_is_pos_o3sw_effluent <NA>
17: 03_tof_ww_is_pos_o3sw_effluent-r002 03_tof_ww_is_pos_o3sw_effluent <NA>
18: 03_tof_ww_is_pos_o3sw_effluent-r003 03_tof_ww_is_pos_o3sw_effluent <NA>
type polarity spectra
<char> <char> <num>
1: MS/MS-DDA negative 1914
2: MS/MS-DDA negative 1932
3: MS/MS-DDA negative 1941
4: MS/MS-DDA positive 2504
5: MS/MS-DDA positive 2491
6: MS/MS-DDA positive 2504
7: MS/MS-DDA negative 1984
8: MS/MS-DDA negative 1995
9: MS/MS-DDA negative 1993
10: MS/MS-DDA positive 2131
11: MS/MS-DDA positive 2126
12: MS/MS-DDA positive 2124
13: MS/MS-DDA negative 1909
14: MS/MS-DDA negative 1921
15: MS/MS-DDA negative 1924
16: MS/MS-DDA positive 2046
17: MS/MS-DDA positive 2052
18: MS/MS-DDA positive 2048
Results empty ProjectHeaders
Project headers (e.g., name, author and description) can be added to
the MassSpecEngine using the
MassSpecEngine$add_headers(). The headers are converted to
an S3 ProjectHeaders
class object in the MassSpecEngine, where it goes through a
validation to ensure conformity (more details here).
# Add headers to the MassSpecEngine
ms$add_headers(
name = "Wastewater Ozonation Showcase",
author = "Ricardo Cunha",
description = "Demonstration project"
)
# Get the headers as ProjectHeaders
ms$get_headers()
ProjectHeaders
file: NA
date: 2024-08-01 10:04:40.137931
name: Wastewater Ozonation Showcase
author: Ricardo Cunha
description: Demonstration project
# Get the MassSpecEngine date
ms$get_headers(value = "date")$date
[1] "2024-08-01 10:04:40 CEST"Replicates and blanks
The analysis replicate names and the associated blank replicate name
can be amended in the MassSpecEngine, as shown below.
Alternatively, a data.frame with column names
file, replicate and blank could be added as
the files argument in
MassSpecEngine$new(files = files) to have directly the
replicate and blank replicate names assigned (more details here).
# Character vector with analysis replicate names
rpls <- c(
rep("blank_neg", 3),
rep("blank_pos", 3),
rep("influent_neg", 3),
rep("influent_pos", 3),
rep("effluent_neg", 3),
rep("effluent_pos", 3)
)
# Character vector with associated blank replicate names
# Note that the order should match the respective replicate
blks <- c(
rep("blank_neg", 3),
rep("blank_pos", 3),
rep("blank_neg", 3),
rep("blank_pos", 3),
rep("blank_neg", 3),
rep("blank_pos", 3)
)
# Chaining add replicates and blanks
ms$add_replicate_names(rpls)$add_blank_names(blks)
# Replicates and blanks were amended
ms$get_overview()[, 1:5] analysis replicate blank type
<char> <char> <char> <char>
1: 01_tof_ww_is_neg_blank-r001 blank_neg blank_neg MS/MS-DDA
2: 01_tof_ww_is_neg_blank-r002 blank_neg blank_neg MS/MS-DDA
3: 01_tof_ww_is_neg_blank-r003 blank_neg blank_neg MS/MS-DDA
4: 01_tof_ww_is_pos_blank-r001 blank_pos blank_pos MS/MS-DDA
5: 01_tof_ww_is_pos_blank-r002 blank_pos blank_pos MS/MS-DDA
6: 01_tof_ww_is_pos_blank-r003 blank_pos blank_pos MS/MS-DDA
7: 02_tof_ww_is_neg_influent-r001 influent_neg blank_neg MS/MS-DDA
8: 02_tof_ww_is_neg_influent-r002 influent_neg blank_neg MS/MS-DDA
9: 02_tof_ww_is_neg_influent-r003 influent_neg blank_neg MS/MS-DDA
10: 02_tof_ww_is_pos_influent-r001 influent_pos blank_pos MS/MS-DDA
11: 02_tof_ww_is_pos_influent-r002 influent_pos blank_pos MS/MS-DDA
12: 02_tof_ww_is_pos_influent-r003 influent_pos blank_pos MS/MS-DDA
13: 03_tof_ww_is_neg_o3sw_effluent-r001 effluent_neg blank_neg MS/MS-DDA
14: 03_tof_ww_is_neg_o3sw_effluent-r002 effluent_neg blank_neg MS/MS-DDA
15: 03_tof_ww_is_neg_o3sw_effluent-r003 effluent_neg blank_neg MS/MS-DDA
16: 03_tof_ww_is_pos_o3sw_effluent-r001 effluent_pos blank_pos MS/MS-DDA
17: 03_tof_ww_is_pos_o3sw_effluent-r002 effluent_pos blank_pos MS/MS-DDA
18: 03_tof_ww_is_pos_o3sw_effluent-r003 effluent_pos blank_pos MS/MS-DDA
polarity
<char>
1: negative
2: negative
3: negative
4: positive
5: positive
6: positive
7: negative
8: negative
9: negative
10: positive
11: positive
12: positive
13: negative
14: negative
15: negative
16: positive
17: positive
18: positiveProcessingSettings
Data processing is performed by modules according to ProcessingSettings.
The S3 ProcessingSettings class objects are obtained via the
respective Settings_[module name]_[algorithm name]
constructor functions, attributing the respective subclass. Below we
obtain the ProcessingSettings for the module
find_features() using the algorithm openms. The
parameters for each processing module can be changed via the constructor
arguments. Documentation for each ProcessingSettings subclass
can be found in the StreamFind
reference documentation.
# Get ProcessingSettings for finding features using the openms algorithm
ffs <- MassSpecSettings_FindFeatures_openms()
# Print in console the details of the ProcessingSettings
ffs
ProcessingSettings
engine MassSpec
call FindFeatures
algorithm openms
version 0.2.0
software openms
developer Oliver Kohlbacher
contact oliver.kohlbacher@uni-tuebingen.de
link https://openms.de/
doi https://doi.org/10.1038/nmeth.3959
parameters:
- noiseThrInt 1000
- chromSNR 3
- chromFWHM 7
- mzPPM 15
- reEstimateMTSD TRUE
- traceTermCriterion sample_rate
- traceTermOutliers 5
- minSampleRate 1
- minTraceLength 4
- maxTraceLength 70
- widthFiltering fixed
- minFWHM 4
- maxFWHM 35
- traceSNRFiltering TRUE
- localRTRange 0
- localMZRange 0
- isotopeFilteringModel none
- MZScoring13C FALSE
- useSmoothedInts FALSE
- extraOpts
- intSearchRTWindow 3
- useFFMIntensities FALSE
- verbose FALSE
# List with other workflow module settings
other_settings <- list(
MassSpecSettings_AnnotateFeatures_StreamFind(),
MassSpecSettings_GroupFeatures_openms(),
# Modifying the parameters with the constructor arguments
# db_is is a data.table with the name, mass and expected retention time of spiked internal standards, as shown below
MassSpecSettings_FindInternalStandards_StreamFind(
database = db_is,
ppm = 8,
sec = 10
),
MassSpecSettings_FilterFeatures_StreamFind(excludeIsotopes = TRUE),
MassSpecSettings_FilterFeatures_patRoon(
absMinIntensity = 5000,
maxReplicateIntRSD = 30,
blankThreshold = 10,
absMinReplicateAbundance = 3
),
MassSpecSettings_LoadFeaturesEIC_StreamFind(
rtExpand = 60,
mzExpand = 0.0005
),
MassSpecSettings_LoadFeaturesMS1_StreamFind(),
MassSpecSettings_LoadFeaturesMS2_StreamFind(),
MassSpecSettings_CalculateQuality_StreamFind(),
MassSpecSettings_FilterFeatures_StreamFind(minSnRatio = 5),
# db_with_ms2 is a database with suspect chemical standards
# includes MS2 data (i.e., fragmentation pattern) from standards
MassSpecSettings_SuspectScreening_StreamFind(
database = db_with_ms2,
ppm = 10,
sec = 15,
ppmMS2 = 10,
minFragments = 3
)
)
# Printing the third ProcessingSettings object from the list
other_settings[[3]]
ProcessingSettings
engine MassSpec
call FindInternalStandards
algorithm StreamFind
version 0.2.0
software StreamFind
developer Ricardo Cunha
contact cunha@iuta.de
link https://odea-project.github.io/StreamFind
doi NA
parameters:
- database (only head rows)
name formula mass rt tag
<char> <char> <num> <int> <char>
1: Cyclophosphamide-D6 C7[2]H6H9Cl2N2O2P 266.0625 1007 IS
2: Ibuprofen-D3 C13[2]H3H15O2 209.1495 1150 IS
3: Diclophenac-D4 C14[2]H4H7Cl2NO2 299.0418 1253 IS
4: Metoprolol-D7 C15[2]H7H18NO3 274.2274 915 IS
5: Sulfamethoxazol-D4 C10[2]H4H7N3O3S 257.0772 1015 IS
6: Isoproturon-D6 C12[2]H6H12N2O 212.1796 1149 IS
- ppm 8
- sec 10 Then, all created ProcessingSettings can be added to the MassSpecEngine. The order will matter when the workflow is applied!
# Add the settings as a list. The order matters!
ms$add_settings(settings = c(list(ffs), other_settings))
# Printing the data processing workflow
ms$print_workflow()
Workflow
1: FindFeatures (openms)
2: AnnotateFeatures (StreamFind)
3: GroupFeatures (openms)
4: FindInternalStandards (StreamFind)
5: FilterFeatures (StreamFind)
6: FilterFeatures (patRoon)
7: LoadFeaturesEIC (StreamFind)
8: LoadFeaturesMS1 (StreamFind)
9: LoadFeaturesMS2 (StreamFind)
10: CalculateQuality (StreamFind)
11: FilterFeatures (StreamFind)
12: SuspectScreening (StreamFind)
run_workflow()
The modules assigned by the added ProcessingSettings can be
processed by run_workflow(), as demonstrated below. Note
that with run_workflow(), the processing modules are
applied with the same order as they were added.
# Run all ProcessingSettings added to the MassSpecEngine
ms$run_workflow()Verifying if your data is centroided... Done!
Applying replicate abundance filter... Done! Filtered 2968 (30.68%) features and 1432 (49.88%) feature groups. Remaining: 6705 features in 1439 groups.
Applying blank filter... Done! Filtered 4634 (69.11%) features and 888 (61.71%) feature groups. Remaining: 2071 features in 551 groups.
Applying intensity filter... Done! Filtered 509 (24.58%) features and 107 (19.42%) feature groups. Remaining: 1562 features in 444 groups.
Applying replicate abundance filter... Done! Filtered 179 (11.46%) features and 93 (20.95%) feature groups. Remaining: 1383 features in 351 groups.Results
The created features and feature groups can be inspected as
data.table objects or plotted by dedicated modules in the
MassSpecEngine.
data.table objects
The features and feature groups can be obtained as
data.table with the
MassSpecEngine$get_features() and
MassSpecEngine$get_groups() methods. The methods also allow
to look for specific features/feature groups using mass, mass-to-charge
ratio, retention time and drift time targets, as show below for a small
set of compound targets where mass and retention time expected value are
known. Note that drift time is only applicable for MS data with ion
mobility separation.
db name formula mass rt tag
<char> <char> <num> <int> <char>
1: 4N-Acetylsulfadiazine C12H12N4O3S 292.0630 905 S
2: Metoprolol C15H25NO3 267.1834 915 S
3: Sulfamethoxazole C10H11N3O3S 253.0521 1015 S
4: Bisoprolol C18H31NO4 325.2253 955 S
5: 4N-Acetylsulfamethoxazole C12H13N3O4S 295.0627 1011 S
6: Carbamazepine C15H12N2O 236.0950 1079 S
7: Terbutryn C10H19N5S 241.1361 1126 S
8: Losartan C22H23ClN6O 422.1622 1095 S
9: Candesartan C24H20N6O3 440.1597 1097 S
10: Isoproturon C12H18N2O 206.1419 1152 S
11: Diuron C9H10Cl2N2O 232.0170 1160 S
12: Bezafibrat C19H20ClNO4 361.1081 1164 S
13: Valsartan C24H29N5O3 435.2270 1177 S
14: Tebuconazole C16H22ClN3O 307.1451 1267 S
15: Diclofenac C14H11Cl2NO2 295.0167 1255 S
16: Propiconazole C15H17Cl2N3O2 341.0698 1308 S
17: Flufenacet C14H13F4N3O2S 363.0665 1296 S
18: Ibuprofen C13H18O2 206.1307 1152 S
19: CBZD C15H14N2O3 270.1004 936 S
# Compounds are searched by monoisotopic mass and retention time
# ppm and sec set the mass (im ppm) and time (in seconds) allowed deviation, respectively
# average applies a mean to the intensities in each analysis replicate group
ms$get_groups(mass = db, ppm = 5, sec = 10, average = TRUE) group name effluent_neg effluent_pos influent_neg
<char> <char> <num> <num> <num>
1: M236_R1079_324 Carbamazepine 0.000 0.00 0.000
2: M253_R1015_555 Sulfamethoxazole 0.000 0.00 0.000
3: M267_R916_735 Metoprolol 0.000 15409.08 0.000
4: M295_R1256_1274 Diclofenac 0.000 0.00 7786.458
5: M325_R957_1778 Bisoprolol 0.000 13762.82 0.000
6: M440_R1097_3164 Candesartan 6750.804 22512.84 34920.953
influent_pos
<num>
1: 66019.721
2: 9679.874
3: 49655.948
4: 19602.254
5: 48375.419
6: 112540.651Already by inspection of the data.table, it is possible
to see compounds detected in the influent but not in the effluent (e.g.,
Carbamazepine) or compounds that are appear to be reduced during
ozonation (e.g., Metoprolol). Since positive and negative ionization
mode were combined, there are compounds that appear in both polarities
and are grouped by neutral monoisotopic mass (e.g., Diclofenac).
plot_groups methods
For a better overview of the results, the method
MassSpecEngine$plot_groups() or even more detailed the
method MassSpecEngine$plot_groups_overview() can be
used.
# set legendNames to TRUE for using the names in db as legend
ms$plot_groups(mass = db, ppm = 5, sec = 10, legendNames = TRUE)
ms$plot_groups_overview(mass = db, ppm = 5, sec = 10, legendNames = TRUE)Filtered not removed
The FilterFeatures module was applied to filter features out
according to defined conditions/thresholds. The filtered features were
not removed but just tagged as filtered and a filter tag was assigned.
For instance, when the method MassSpecEngine$get_features()
is run with filtered argument set to TRUE, the
filtered features are also shown. Below, we search for the internal
standards added to each analysis that were filtered when applying the
blank subtraction, receiving the tag blank in the
filter column and TRUE in the filtered
column.
db_is name formula mass rt tag
<char> <char> <num> <int> <char>
1: Cyclophosphamide-D6 C7[2]H6H9Cl2N2O2P 266.0625 1007 IS
2: Ibuprofen-D3 C13[2]H3H15O2 209.1495 1150 IS
3: Diclophenac-D4 C14[2]H4H7Cl2NO2 299.0418 1253 IS
4: Metoprolol-D7 C15[2]H7H18NO3 274.2274 915 IS
5: Sulfamethoxazol-D4 C10[2]H4H7N3O3S 257.0772 1015 IS
6: Isoproturon-D6 C12[2]H6H12N2O 212.1796 1149 IS
7: Diuron-D6 C9[2]H6H4Cl2N2O 238.0547 1157 IS
8: Carbamazepin-D10 C15[2]H10H2N2O 246.1577 1075 IS
9: Naproxen-D3 C14[2]H3H11O3 233.1131 1169 IS
# set filtered to TRUE for showing filtered features/feature groups
ms$get_groups(mass = db_is, ppm = 8, sec = 10, average = TRUE, filtered = TRUE) group name blank_neg blank_pos effluent_neg
<char> <char> <num> <num> <num>
1: M212_R1149_108 Isoproturon-D6 0.00 1198378.67 0.000
2: M238_R1157_345 Diuron-D6 14884.04 120342.36 15530.309
3: M246_R1075_442 Carbamazepin-D10 0.00 456753.78 0.000
4: M257_R1014_594 Sulfamethoxazol-D4 10741.03 193813.88 7969.246
5: M266_R1007_716 Cyclophosphamide-D6 0.00 51652.42 0.000
6: M274_R915_852 Metoprolol-D7 0.00 1581149.46 0.000
7: M299_R1254_1361 Diclophenac-D4 24035.19 51904.34 19708.235
effluent_pos influent_neg influent_pos
<num> <num> <num>
1: 1322755.92 0.000 1269561.25
2: 139259.80 14521.777 130401.27
3: 510457.02 0.000 532430.03
4: 204772.06 7087.166 189112.69
5: 60848.79 0.000 56586.21
6: 1571304.96 0.000 1497131.38
7: 51395.04 18130.792 48452.52Internal Stanards
The method FindInternalStandards was applied and the results
can be obtained with the dedicated method
MassSpecEngine$get_internal_standards() or plotted as a
quality overview using the method
MassSpecEngine$plot_internal_standards_qc(), as shown
below. The plot gives an overview of the mass, retention time and
intensity variance of the internal stadards across the analyses in the
project.
ms$plot_internal_standards_qc()Isotopes
The method AnnotateFeatures was applied to annotate the
natural isotopes within the MS data. Implementation of adducts and in
source fragments annotation is planned but not yet available with the
StreamFind algorithm. The method
MassSpecEngine$get_isotopes() can be used to search for
isotopes, as shown below for the analysis number 11. Because the filter
excludeIsotopes was applied, the isotopic features are
filtered out with tag isotope or
minIntensity when filtered by
minIntensity, which was applied first. Although the first
isotope of Diclofenac (i.e., the 13C) was not found, the
second was annotated as the maxGaps argument in settings
was set to 1. This is a frequent case for compounds with halogens.
# Isotopes of Diclofenac and Candesartan in analysis 5
ms$get_isotopes(
analyses = 5,
mass = db[db$name %in% c("Diclofenac", "Candesartan"), ],
ppm = 5, sec = 10
) analysis feature iso_cluster
<char> <char> <int>
1: 02_tof_ww_is_pos_influent-r002 f_14378903822300977773 384
2: 02_tof_ww_is_pos_influent-r002 f_597963579288397561 933
3: 02_tof_ww_is_pos_influent-r002 f_8778520158543999941 384
4: 02_tof_ww_is_pos_influent-r002 f_4551345311938997883 933
5: 02_tof_ww_is_pos_influent-r002 f_98688234871004542 933
iso_cluster_size iso_cluster_feature iso_elements iso_carbons iso_charge
<int> <char> <char> <num> <int>
1: 2 f_14378903822300977773 0 1
2: 3 f_597963579288397561 24 1
3: 2 f_14378903822300977773 37Cl 0 1
4: 3 f_597963579288397561 13C 24 1
5: 3 f_597963579288397561 13C 13C 24 1
iso_tag iso_step iso_md_diff iso_md_hit iso_md_error iso_mass_deviation
<char> <int> <num> <num> <num> <num>
1: M+0 0 0.000000 0.000000 0.000000e+00 0.001028816
2: M+0 0 0.000000 0.000000 0.000000e+00 0.000898936
3: M+2 2 1.996954 1.997050 9.628567e-05 0.002194066
4: M+1 1 1.002572 1.003355 7.832827e-04 0.002757634
5: M+2 2 2.005729 2.006710 9.801857e-04 0.002757634
iso_relative_intensity rt mass mz intensity rtmin
<num> <num> <num> <num> <num> <num>
1: 1.00000000 1255.604 295.0173 296.0246 18776.840 1251.924
2: 1.00000000 1096.581 440.1609 441.1681 109887.297 1091.918
3: 0.75621288 1255.604 297.0142 298.0215 14199.288 1251.924
4: 0.25861065 1096.581 441.1634 442.1707 28418.025 1093.570
5: 0.04135803 1096.581 442.1666 443.1739 4544.722 1093.570
rtmax mzmin mzmax name
<num> <num> <num> <char>
1: 1264.436 296.0238 296.0256 Diclofenac
2: 1105.248 441.1674 441.1690 Candesartan
3: 1263.002 298.0208 298.0230 Diclofenac
4: 1105.248 442.1691 442.1712 Candesartan
5: 1104.001 443.1711 443.1762 CandesartanThe isotopes can also be visualized with the method
MassSpecEngine$map_isotopes(), as shown below for the
internal standards added to analysis 11. Note that again the
filtered argument was set to TRUE to return
also filtered features.
ms$map_isotopes(
analyses = 5,
mass = db_is,
ppm = 8, sec = 10,
filtered = TRUE,
legendNames = TRUE
)Suspects
The method MassSpecEngine$plot_suspects() is used to
inspect the suspect screening results. A second plot is added to compare
the experimental fragmentation pattern (top) with the fragmentation
pattern of the respective reference standard (down) added within the
database. The colorBy argument can be set to
targets+replicates to legend the plot with combined keys of
suspect target names and analysis replicate names.
ms$plot_suspects(colorBy = "targets+replicates")More to come
Future integration:
- Correcting matrix for better comparison between influent and effluent
- Annotation of adducts and in-source fragments
- Formula/compound assignment via patRoon
- Screening of transformation products using the biotransformer tool via patRoon
- Fold-change and other statistic analysis for inter-sample correlation