1. Overview of LC-MS/MS raw data processing workflow
Global or untargeted lipidomics is increasingly used to investigate
changes of lipids in various biological or environmental systems in an
unbiased manner. Liquid chromatography coupled to high-resolution mass
spectrometry (LC-HRMS) has become the main workhorse for global
metabolomics. The typical LC-HRMS metabolomics workflow involves spectra
collection, raw data processing, statistical and functional
analysis.
MetaboAnalystR is a R package, synchronized with the popular
MetaboAnalyst website, designed for comprehensive metabolomic data
analysis, visualization, and interpretation.
Since the version 2, MetaboAnalystR has supported raw spectral data
processing, and linked the MS data processing results directly for
functional analysis. The raw spectral data procesing was depending on XCMS.
Given that there are many parameters to be optimized for centWave
algorithm, which is implemented in XCMS. In version 3, MetaboAnalystR
implemented an auto-optimized workflow to optimize all critical
parameters to obtain an optimal results. Meanwhile, all functions in
MetaboAnalystR for raw spectral processing have been well-organized into
a companion R package, OptiLCMS. In the latest
version (4.0), MetaboAnalystR starts supporting data deconvolution on
MS2 spectral data, including data-dependent acquisition (DDA) and
SWATH-data independent acquisition (DIA).
The aim of this vignette is to provide an overview on how to use
MetaboAnalystR (together with OptiLCMS) to perform LC-MS/MS raw spectral
metabolomics data processing. This vignette mainly includes the
installation of OptiLCMS, MS data import, Parameters’ optimization,
MS/MS data import, data deconvolution, spectral concensus of replicates,
MS/MS spectral database searching and results export.
The general workflow of MetaboAnalystR for raw LC-MS/MS data
processing is depicted below. The general data processing steps include
two parts: MS feature detection and MS/MS data based chemical
identification. The whole workflow are summarized in Figure 1.
1.0 Loading the package
After following the installation instructions on the OptiLCMS Github page, you will be
ready to use the package. Use the library() function to load the package
into R. Please make sure MetaboAnalystR has already been installed. If
not, please refer to the installation
guidance.
# Load MetaboAnalystR
library(MetaboAnalystR)
# Load OptiLCMS
library(OptiLCMS)
2. Example raw spectral data processing (MS1)
2.0 Download example spectra
In this example, we are going to use an experimental Malaria
metabolomics dataset (UPLC-Q/E-ESI+, HILIC) between two immune status
(Native vs. Semi-immune) from Li et al.
There are 12 samples (2 groups, 6 samples for each) and 3 QCs included
in this dataset. In this tutorial, we are going to use the
auto-optimized pipeline to process MS1 data step by step.
download.file("https://www.xialab.ca/api/download/metaboanalyst/malaria_r_example.zip",
destfile = "malaria_raw.zip",
method = "curl")
unzip("malaria_raw.zip", exdir = "upload")
MS1 raw spectral processing include the following four steps: 1.
(optional) automatic parameters’ optimization; 2. Raw spectral import;
3. MS1 data processing (peak picking, alignment and gap filling); 4.
Peak annotation (as adducts and isotopes).
2.2 Auto-optimization of parameters
In this example, we will execute
PerformParamsOptimization function to optimize the
critical parameters of peak picking and alignment for the following data
processing in an automatic way. It utilizes the extracted ROI data and
the internal instrument-specific parameters. Parallel computing can be
enabled. The number of cores user want to use could be specified. More
RAM will be consumed if the parallel core is set high.
Parameters optimization with a design of experiment (DoE) strategy.
Initial parameters are from the optimized parameters above or the
internal default parameters from embedded SetPeakParam function.
Here is some recommendations for parallel core setting, 64GB RAM ~ 6
cores; 32GB RAM ~ 4 cores; 16GB RAM ~ 2 cores. Minimum 16GB RAM is
required for raw spectral processing.
# Here we use PerformParamsOptimization to optimize parameters based on
# the extracted ROI (stored in 'mSet') before process the entire dataset
best_params <- PerformParamsOptimization(mSet, param = SetPeakParam(platform = "UPLC-Q/E"), ncore = 4);
##
## Step 1/6: Start to optimize parameters!
##
## This step may take a long time...
## DoE Optimization Starting Now... (2023-10-02 13:42:23)
## Evaluating Noise level...
## ppm is estimated as : 4.3
## Done!
## Preparing Parameters for optimization finished !
## Data Spliting Finished !
## Peak Preparing Begin...
## Peak Preparing Done !
## Round:1
## DoE Running Begin...
## 100% |
## Round 1 Finished !
## Model Parsing...
## Gaussian peak ratio (%): 39.3
## Model Parsing Done !
## Round:2
## DoE Running Begin...
## 100% |
## Round 2 Finished !
## Model Parsing...
## Gaussian peak ratio (%): 42.6
## Model Parsing Done !
## Round:3
## DoE Running Begin...
## 100% |
## Round 3 Finished !
## Model Parsing...
## Gaussian peak ratio (%): 34.3
## Model Parsing Done !
## No Increase Stopping !
## best parameter settings:
## min_peakwidth: 5
## max_peakwidth: 15.75
## mzdiff: 0.036
## snthresh: 7.1
## bw: 2
## Peak_method: centWave
## RT_method: loess
## ppm: 4.3
## noise: 6772
## prefilter: 2
## value_of_prefilter: 11556.95
## minFraction: 0.8
## minSamples: 1
## maxFeatures: 100
## fitgauss: FALSE
## mzCenterFun: wMean
## integrate: 1
## extra: 1
## span: 0.4
## smooth: loess
## family: gaussian
## verbose.columns: FALSE
## polarity: negative
## perc_fwhm: 0.6
## mz_abs_iso: 0.005
## max_charge: 2
## max_iso: 2
## corr_eic_th: 0.85
## mz_abs_add: 0.001
## rmConts: TRUE
## BlankSub: TRUE
## Step 1/6: Parameters Optimization Finished ! (2023-10-02 14:27:32)
## Time Spent In Total:45.2mins
2.3 Importing example data
For raw spectral processing, MetaboAnalystR accepts open-source
formats (including mzML, NetCDF, mzDATA, mzXML) data. There are two
options for users to input the metadata information (grouping
information, e.g. diseases/healthy groups) on all files:
1). Save all files of the same group into a sub-folder. There must be
at least 2 sub folders (excluding QC sample);
2). Use a meta data file for parameter, metadata. A phenotype data
frame or a absolute path of the metadata file (.txt) for all samples. In
the option, first column should be the sample name, while second column
is the corresponding group name. If ommited, all samples in the same
sub-folder will be considered as one group.
# "path" is used to specify the path to the folder containing the raw MS spectra to be processed.
# BPI and TIC plotting can be enabled with parameter,
# "plotSettings = SetPlotParam(Plot = T)", or disabled by changing "T" into "F";
mSet <- ImportRawMSData(path = c("upload"), plotSettings = SetPlotParam(Plot = T))
## No initialized mSet found, will initialize one automatically !
## Warning in ImportRawMSData(path = c("upload"), plotSettings = SetPlotParam(Plot
## = T)): No metadata provided, all files in one sub-folder will be considered a
## group!
##
## Step 2/6: Start to import the spectrum!
## This step will take a short time...
## Raw file import begin...
## To reduce memory usage BPIS and TICS plots will be created using only 10 samples per group.
## Plotting BPIS and TICS.
Figure 3. Base peak ion (BPI) intensity
chromatogram and Total ion chromatogram (TIC) of all samples
(ESI+).
## Step 2/6: Successfully imported raw MS data! (2023-10-02 14:27:43)
## Going to the next step...
Figure 3. Base peak ion (BPI) intensity
chromatogram and Total ion chromatogram (TIC) of all samples
(ESI+).
2.4 Raw spectral data processing
The next step is to detect peaks (also known as peak picking) from
the centroid data. Multiple algorithms have been developed to identify
peaks in different dimensions such as retention time. Among them, the
centWave algorithm implemented in XCMS has been shown
to perform well in processing LC–HRMS spectra. After peak detection,
peak alignment is performed to address retention time variations across
spectra. These aligned peaks form a peak intensity table with varying
proportions of missing values. These missing values indicate that either
peak detection failed or the corresponding feature is absent from the
respective sample. Therefore, the final step is ‘gap filling’ by
reperforming direct peak extraction on corresponding regions in the raw
spectra.
The PerformPeakProfiling function is wrapper of raw spectral
processing functions that performs peak detection, alignment, and
grouping in an automatical step. The parameters from optimization step
above can be used directly in this step.
The function also generates two diagnostic plots including statistics
on the total intensity of peaks in different samples, a retention time
adjustment map, and a PCA plot showing the overall sample clustering
prior to data cleaning and statistical analysis.
# "mSet" include complete raw MS spectra to be processed.
# "params" is using the "best_params" generated above
# Plotting functions can be enabled with parameter,
# "plotSettings = SetPlotParam(Plot = T)", or disabled by changing "T" into "F";
mSet <- PerformPeakProfiling(mSet, Params = best_params, plotSettings = SetPlotParam(Plot=TRUE))
## 6 CPU Threads will be used for peak profiling !
##
## Step 3/6: Started peak picking! This step will take some time...
## Step 3/6: Peak picking finished ! (2023-10-02 14:28:39)
##
## Step 4/6: Started peak alignment! This step is running...
## Total of 9517 slices detected for processing... Done !
## Going to the next step...
## No blank sample included based on grouping info. Skipped!
## PeakGroup -- loess is used for retention time correction.....
## Performing retention time correction using 1321 peak groups.
## Applying retention time adjustment to the identified chromatographic peaks ... Done !
## Total of 9517 slices detected for processing... Done !
## Going to the next step...
## Step 4/6: Peak alignment finished ! (2023-10-02 14:29:28)
##
## Step 5/6: Started peak filling! This step may take some time...
## Starting peak filling!
## Defining peak areas for filling-in....OK
## Start integrating peak areas from original files...
## Step 5/6: Peak filing finished ! (2023-10-02 14:29:58)
## Peak Profiling finished successfully !
## Begin to plotting figures...
## Done !
Figure 4. Raw spectral profiling results
(Feature stats; Retention time adjustment; BPI after RT
adjustment).
2.5 Feature annotation
A typical LC–HRMS spectrum of common biofluids (such as blood or
urine samples) can often produce >10,000 peaks. However, this number
is not equivalent to the number of compounds detected. The
correspondence between the number of peaks and the number of actual
metabolites remains elusive38. Multiple peaks can be derived from the
same compounds. These are real, biologically relevant peaks and might
result from the formation of adducts, incorporation of isotopes, or
fragmentation during sample preparation or LC–MS analysis. The remaining
peaks are now considered to be largely from background noise (artifacts
or noise peaks)39. Therefore, the first step in peak annotation aims to
identify real peaks, and to clarify the relationships among them. Many
empirical and statistical rules have been developed to address this
problem, including CAMERA40 and CliqueMS41, which are two popular R
packages. Peak annotation in MetaboAnalyst 5.0 is currently based on
CAMERA.
The PerformPeakAnnotation function annotates isotope and adduct peaks
with the method from CAMERA (PMID: 22111785). It outputs the result as a
CSV file (“annotated_peaklist.csv”) and saves the annotated peaks to
mSet object. Finally, the peak list is formatted to the correct
structure for MetaboAnalystR and filtered based upon user’s
specifications using the FormatPeakList function. This function permits
the filtering of adducts (i.e. removal of all adducts except for
[M+H]+/[M-H]-) and filtering of isotopes (i.e. removal of all isotopes
except for monoisotopic peaks). The goal of filtering peaks is to remove
degenerative signals and reduce the file size.
# We firstly define the parameters for feature annotation
# 'polarity' is required, can be either 'negative' or 'positive';
# 'perc_fwhm' is used to set the percentage of the width of the FWHM for peak grouping.
# Default is set to 0.6;
# 'mz_abs_iso' is used to set the allowed variance for the search (for isotope annotation).
# The default is set to 0.005;
# 'max_charge' is set the maximum number of the isotope charge.
# For example, the default is 2, therefore the max isotope charge is 2+/-;
# 'max_iso' is used to set the maximum number of isotope peaks.
# For example, the default is 2, therefore the max number of isotopes per peaks is 2;
# 'corr_eic_th' is used to set the threshold for intensity correlations across samples.
# Default is set to 0.85.
# 'mz_abs_add' is used to set the allowed variance for the search (for adduct annotation).
# Default is set to 0.001.
# 'adducts' is used to specify the adducts based on your instrument settings.
annParams <- SetAnnotationParam(polarity = 'positive', mz_abs_add = 0.015);
# "mSet" include processed raw MS spectra to be processed.
# "annParams" is the parameters used for annotation
mSet <- PerformPeakAnnotation(mSet, annParams)
##
## Step 6/6: Starting Peak Annotation...
## Start grouping after retention time.
## Created 109 pseudospectra.
## Generating peak matrix...
## Run isotope peak annotation..
## Found isotopes:716
## Start grouping after correlation...
## Generating EIC's....
## Detecting Naive_007.mzML ... 477 peaks found!
## Detecting Naive_027.mzML ... 24 peaks found!
## Detecting Naive_071.mzML ... 50 peaks found!
## Detecting Naive_109.mzML ... 60 peaks found!
## Detecting Naive_127.mzML ... 159 peaks found!
## Detecting Naive_139.mzML ... 0 peaks found!
## Detecting QC_001.mzML ... 1299 peaks found!
## Detecting QC_003.mzML ... 2 peaks found!
## Detecting QC_005.mzML ... 25 peaks found!
## Detecting Semi_025.mzML ... 651 peaks found!
## Detecting Semi_045.mzML ... 404 peaks found!
## Detecting Semi_061.mzML ... 708 peaks found!
## Detecting Semi_091.mzML ... 553 peaks found!
## Detecting Semi_143.mzML ... 687 peaks found!
## Detecting Semi_157.mzML ... 88 peaks found!
## Warning: Found NA peaks in selected sample.
## Calculating peak correlations in 109 Groups...
## Calculating graph cross linking in 109 Groups...
## New number of ps-groups: 2589
## mSet has now 2589 groups, instead of 109 !
## Generating peak matrix for peak annotation!
## Polarity is set in annotaParam: positive
## Ruleset could not read from object! Recalculating...
## Calculating possible adducts in 2589 Groups...
## Step 6/6: Successfully performed peak annotation! (2023-10-02 14:31:02)
## Going to the final step...
2.6 Feature table generation
# Here we format and filter the peak list for following analysis with MetaboAnalystR
# Parameters are explained as below,
# annParams, is the object created using the SetAnnotationParam function above;
# filtIso, is used to decide to filter out all isotopes (TRUE) or not (FALSE);
# filtAdducts, is used to decide to filter out all adducts (TRUE) or not (FALSE);
# missPercent, specify the threshold to remove features missing in a certain percentage
# of all samples in a group.
mSet <- FormatPeakList(mSet, annParams, filtIso = FALSE, filtAdducts = FALSE, missPercent = 1);
Figure 5. 2D PCA score plot of all features
(colored based on group information)
Here, we need to export all results for following analysis, such as
statistic analysis, functional analysis etc.
# Export annotation results, the annotation will be save as "annotated_peaklist.csv";
Export.Annotation(mSet);
# Export complete feature table. It will be saved as "metaboanalyst_input.csv";
# This table can be used for statistic analysis, functional analysis, biomarker analysis module directly.
Export.PeakTable(mSet);
## Done!
##
## Everything has been finished Successfully ! (2023-10-02 14:31:02)
# Export a summary table (peak_result_summary.txt) to summarize the information of all peaks in
# different samples. The columns are sample names, groups, retention time range, m/z range of all peaks,
# number of all peaks and percentage of missing features.
Export.PeakSummary(mSet)
3. Example of DDA MS/MS data processing
The wide application of high-resolution MS has greatly improved the
accuracy of mass measurement, but it is still inadequate to identify all
ions at the MS1 level. Therefore, LC-MS is typically performed with MS1
full scan coupled with MS/MS to achieve high-throughput quantification
and compound annotation. MS/MS spectra can be generated using
data-dependent acquisition (DDA) or data-independent acquisition (DIA)
methods. DDA acquires MS/MS spectra by fragmentation of precursor ions
selected using a relatively narrow MS/MS isolation window (e.g., 1 m/z).
Although DDA spectra are directly linked to precursors, recent studies
show that > 50% of them are ‘chimeric’ and need to be deconvolved
before searching any reference database.
In this case study, we are implementing a small DDA dataset from a
COVID-19 metabolomics study.
3.0 Download example DDA MS/MS spectra
In this example, we are including a small sub-dataset from a COVID-19
metabolomics study (Peng W. et
al). This small dataset includes 6 samples (3 Mild samples and 3
fetal samples) for quick learning and demo purpose. User could download
the compelete
dataset.
# There are 6 raw spectral files (.mzML) and 1 MS feature table included in the downloaded folder
# The MS feature table was generated with the MS1 data processing pipeline (as described in section 2)
download.file("https://www.xialab.ca/api/download/metaboanalyst/ms2_dda_example.zip",
destfile = "ms2_dda_example.zip",
method = "curl")
unzip("ms2_dda_example.zip", exdir = "ms2_dda")
3.1 Download MS/MS spectra reference database
MeaboAnalystR includes comprehensive MS/MS reference database. The
reference spectra database is not contained in the R package itself. But
it is fully accessible and can be downloaded from MetaboAnalyst server.
MetaboAnalystR offers comprehensive database options to facilitate
high-throughput MS/MS spectra processing and compound annotation. A
total of five databases are provided, including pathway compound
database, biological compound database, lipids database, exposomics
database and the complete database. All these databases are curated from
public MS and MS/MS data, including HMDB,
MoNA Series, LipidBlast,
MassBank, GNPS,
LipidBank, MINEs, LipidMAPs, KEGG.
All public MS/MS reference databases have been curated and organized
into different options for MetaboAnalystR:
1). Complete Database, including all MS/MS reference records (~7.2GB)
Download,
Neutral
Loss;
2). Biology Database, including compounds have been reported to
participate biological mechanism (744MB) Download,
Neutral
Loss;
3). Pathway Database, including KEGG pathways related compounds from
120 common species (138MB) Download,
Neutral
Loss;
4). Lipids Database, including Lipids and lipid-like compounds
(1.9GB) Download,
Neutral
Loss;
5). Exposomics Database, including currently reported exposome
related compounds (1.5GB) Download,
Neutral
Loss;
6). HMDB experimental Database, including only HMDB experimental
database (for learning purpose, 13MB) Download,
Neutral
Loss;
## Here we are downloading biology MS/MS reference database.
download.file("https://www.xialab.ca/api/download/metaboanalyst/MS2ID_Bio_v09102023.zip",
destfile = "MS2ID_Bio.zip",
method = "curl")
unzip("MS2ID_Bio.zip", exdir = "ms2_db")
3.2 Importing DDA MS/MS spectra
## We load MetaboAnalystR and OptiLCMS
library(MetaboAnalystR)
library(OptiLCMS)
## Clean the environment first
rm(list = ls())
## Read the MS1 feature table as the target features for MS/MS data processing
## This table include four columns (mzmin, mzmax, rtmin, rtmax)
## mzmin and mzmax is the minimum and the maximum value of m/z for the feature;
## rtmin and rtmax is the minimum and the maximum value of retention time for the feature;
ft_dt <- qs::qread("ms2_dda/ms2_dda_example/ft_dt.qs")
## Here we use function PerformMSnImport to read MS/MS data
## This step may take seconds to minutes depending on the size of your dataset
mSet <- PerformMSnImport(filesPath = c(list.files("ms2_dda/ms2_dda_example/",
pattern = ".mzML",
full.names = T, recursive = T)),
targetFeatures = ft_dt,
acquisitionMode = "DDA")
## Importing raw MS data .. done.
3.3 DDA MS/MS spectra Deconvolution
For DDA spectral data deconvolution, MetaboAnalystR assigns all MS/MS
spectra of an individual spectral data into different feature groups
based on precursors’ information (m/z, retention time), and chimeric
status is evaluated based on the nearest MS scan.
The MS/MS spectra of all ions (including main precursor and other
contaminating ions within the isolation window and above the intensity
threshold) are extracted from reference libraries as candidate spectra.
If any reference spectrum is missing, a predicted spectrum will be
generated using a similarity-network model. All candidates are then used
to obtain the deconvolved spectrum based on elastic-net regression with
extra penalties to the predicted spectra.
# In this step, we are processing the DDA spectra deconvolution;
# The deconvolution is based on the Biology MS/MS spectral database;
# Parallel computing is supported, users are encouraged to use multiple cores to speed up;
# This step may take minutes to hours to finish, depending on the size of datasets
system.time(mSet <- PerformDDADeconvolution(mSet,
ppm1 = 5,
ppm2 = 10,
sn = 12,
filtering = 0,
window_size = 1.5,
intensity_thresh = 1.6e5,
database_path = "ms2_db/MS2ID_Bio_v09102023.sqlite",
ncores = 6L))
## Loading required package: parallel
##
## Deconvolution executed successfully!
## user system elapsed
## 0.888 0.172 163.956
3.4 Spectrum consensus of replicates
MS/MS data acquisition with multiple replicates is common. In such
cases, all deconvolved M/MS spectra corresponding to the same MS1 peak
must be processed to generate a single consensus spectrum. If there are
no replicates, this step is skipped. All MS/MS fragments across
different replicates are initially summarized by count.
If the frequency of an individual fragment is above a user-defined
threshold (e.g., 50%), it is kept; otherwise, the fragment is removed.
Optionally, a database-assisted spectrum consensus in MetaboAnalystR can
be used to avoid potential over-deletion. If database-assisted option is
enabled by users, all spectra of the precursor are extracted as referent
list (L) from the reference library. All fragments not meeting the
(user-defined) frequency threshold are then searched against L.
If this fragment can be found from L and the frequency of the
fragment across the replicates is over 2, the fragment is kept;
otherwise, it is discarded. All kept fragments are normalized and merged
to generate a consensus spectrum for database searching in the next
step. The spectrum consensus can be achieved with the function,
PerformSpectrumConsenus.
mSet <- PerformSpectrumConsenus (mSet,
ppm2 = 15,
concensus_fraction = 0.2,
database_path = "",
use_rt = FALSE,
user_dbCorrection = FALSE)
3.5 Reference database searching
Reference library searching in MetaboAnalystR is based on the m/z
(and optionally, the information of retention time) of precursors. All
matches are extracted from database for formula prediction.
MetaboAnalystR uses the same scoring rule as MS-DIAL. The matching score
is calculated using the following formula:
Similarity_score = (MS/MS similarity + m/z similarity + RT similarity
+ Isotope Similarity x 0.5)/3.5.
where the MS/MS similarity (ranging from 0 to 1) is calculated using
the popular dot
product similarity or spectral entropy
similarity algorithms. MS1 similarity and retention time (RT)
similarity (both ranging from 0 to 1) are calculated with exponential
distribution method based on deviation. Isotope similarity is also
calculated using a similar method as implemented in MS-DIAL. However,
the calculation of isotope distribution similarity only considers [M+n]
(n<3), as the intensity of isotopes [M+n] (n≥3) is very low and
highly variant to be considered. Briefly, the isotope distribution
similarity evaluation is performed based on the experimental isotope
distribution and the theoretical distribution of formulas extracted from
the reference library. Isotope elements considered here include carbon
(C13), hydrogen (H2), nitrogen (N15), oxygen (O17, O18) and sulfur (S33,
S34). Other elements are not considered due to their extremely low
abundance in nature. Retention time is optionally used based on users’
request. If retention time is disabled (by default), the retention time
similarity is not calculated, and denominator is modified to 2.5.
The database searching is performed based on SQLite query, and can be
achieved with the function,
PerformDBSearchingBatch.
# PerformDBSearchingBatch is used to seatching MS/MS reference library
# Results will be scored based on the similarity rules above
# Parallel computing is allowed. CPU cores used are controlled by argument "ncores".
mSet <- PerformDBSearchingBatch (mSet,
ppm1 = 10,
ppm2 = 25,
rt_tol = 5,
database_path = "ms2_db/MS2ID_Bio_v09102023.sqlite",
use_rt = FALSE,
enableNL = FALSE,
ncores = 6L)
## ==== Database searching against MS2ID_Bio_v09102023.sqlite started ====
##
## ============ Searching finished successfully! ============
3.6 Results export
After the reference library searching, all compound identification
results can be exported as a data frame and saved as a .csv or .txt
file. The exported information includes compound names, chemical
formula, InChIKeys, and matching scores. If the reference library is
lipid library, the exported information also includes lipid
classifications (super class, main class and sub-class). The database
searching results can be exported using
PerformResultsExport function and formatted as a data
frame table using FormatMSnAnnotation function.
mSet <- PerformResultsExport (mSet,
type = 0L,
topN = 10L,
ncores = 3L)
======= Converting started. See progress below =======
============ Exporting finished successfully! ============
# 2nd argument, TopN, is the argument used to specifiy the number of compounds to export
# If the dataset is a lipidomics dataset, please set 3rd argument as "TRUE"
# to extract the lipid classification information
# All identified compounds have been summarized as a data.frame (dtx) here
dtx <- FormatMSnAnnotation(mSet, 5L, F)
All identified compounds have been summarized as a data.frame. The
results are shown in Table 1 below. Here are the explication of
different columns:
1). mzmin, minimum m/z value for this feature;
2). mzmax, maximum m/z value for this feature;
3). rtmin, minimum retention time value for this feature;
4). rtmax, maximum retention time value for this feature;
5). Compound_N, Compound name of the Nth identification. The
identification results are sorted based on the scores (decreasing);
6). InChiKey_N, InChiKey of the Nth identification;
7). Formula_N, Formula of the Nth identification;
8). Score_N, Similarity score of the Nth identification. 100 is the
perfect match. 0 is the negative match;
9). Database_N, the MS/MS reference library source of the Nth
identification;
10). SuperClass_N, Superclass of the Nth identification (only for
lipidomics database).
11). MainClass_N, Mainclass of the Nth identification (only for
lipidomics database).
12). SubClass_N, Subclass of the Nth identification (only for
lipidomics database).
4. Example of SWATH-DIA MS/MS data processing
Compared to DDA MS/MS spectra, Data independent acquisition (DIA)
MS/MS provides more coverage on the metabolome. SWATH-DIA is an emerging
technique of DIA data acquisition by splitting the acquisition range as
sequential windows, and further reduce the complexities of the data
processing. Different from the DDA MS/MS data, there is no clear
association between precursors and MS/MS spectra. Therefore, the
deconvolution step of SWATH-DIA is more important for chemical
identification.
The SWATH-DIA MS/MS data processing module of MetaboAnalystR has been
developed based on the DecoMetDIA.
The entire deconvolution workflow was written using Rcpp/C++ framework
and further optimized to enable high-throughput processing. In addition,
MetaboAnalystR supports multi-threaded data processing to further speed
up the analysis through parallel computing.
In this case study, we are implementing another COVID-19 metabolomcis
SWATH-DIA data.
4.0 Download example SWATH-DIA spectra
Similar to the example dataset of DDA, in this example, we are
including another small sub-dataset from another COVID-19 metabolomics
study (Mahmoud S. et
al). This small dataset includes 6 samples (3 COVID-19 samples and 3
healthy controls) for quick learning and demo purpose.
# There are 6 raw spectral files (.mzML) and 1 text file, which include the design of SWATH windows
download.file("https://www.xialab.ca/api/download/metaboanalyst/ms2_dia_example.zip",
destfile = "ms2_dia_example.zip",
method = "curl")
unzip("ms2_dia_example.zip", exdir = "ms2_dia")
## Load OptiLCMS
library(OptiLCMS)
## Construct meta data
meta_dt <- data.frame(samples = c("210210-SWATH-NEG-Covid-Cov-16.mzML",
"210210-SWATH-NEG-Covid-Cov-17.mzML",
"210210-SWATH-NEG-Covid-Cov-18.mzML",
"210210-SWATH-NEG-Covid-Ct-1.mzML",
"210210-SWATH-NEG-Covid-Ct-2.mzML",
"210210-SWATH-NEG-Covid-Ct-3.mzML"),
groups = c(rep("COVID",3), rep("Control",3)))
## Import raw MS1 data
mSet1 <- ImportRawMSData(path = "ms2_dia/ms2_dia_example/", metadata = meta_dt)
## No initialized mSet found, will initialize one automatically !
##
## Step 1/6: Start to import the spectrum!
## This step will take a short time...
## Raw file import begin...
## Step 1/6: Successfully imported raw MS data! (2023-10-02 14:41:40)
## Going to the next step...
## Process MS1 peaks by using customized parameters
mSet1 <- PerformPeakProfiling(mSet1,
Params = SetPeakParam(ppm = 25,
bw = 3,
mzdiff = 0.001,
max_peakwidth = 35,
min_peakwidth = 5,
noise = 200,
minFraction = 0.2),
ncore = 6,
plotSettings = SetPlotParam(Plot = F))
## 6 CPU Threads will be used for peak profiling !
##
## Step 3/6: Started peak picking! This step will take some time...
## Step 3/6: Peak picking finished ! (2023-10-02 14:41:53)
##
## Step 4/6: Started peak alignment! This step is running...
## Total of 7535 slices detected for processing... Done !
## Going to the next step...
## No blank sample included based on grouping info. Skipped!
## PeakGroup -- loess is used for retention time correction.....
## Performing retention time correction using 1306 peak groups.
## Applying retention time adjustment to the identified chromatographic peaks ... Done !
## Total of 7535 slices detected for processing... Done !
## Going to the next step...
## Step 4/6: Peak alignment finished ! (2023-10-02 14:42:00)
##
## Step 5/6: Started peak filling! This step may take some time...
## Starting peak filling!
## Defining peak areas for filling-in....OK
## Start integrating peak areas from original files...
## Step 5/6: Peak filing finished ! (2023-10-02 14:42:25)
## Peak Profiling finished successfully !
## Begin to plotting figures...
## Annotation
annParams <- SetAnnotationParam(polarity = 'negative',
mz_abs_add = 0.035);
mSet1 <- PerformPeakAnnotation(mSet = mSet1,
annotaParam = annParams,
ncore =1)
##
## Step 6/6: Starting Peak Annotation...
## Start grouping after retention time.
## Created 66 pseudospectra.
## Generating peak matrix...
## Run isotope peak annotation..
## Found isotopes:98
## Start grouping after correlation...
## Generating EIC's....
## Detecting 210210-SWATH-NEG-Covid-Cov-16.mzML ... 334 peaks found!
## Detecting 210210-SWATH-NEG-Covid-Cov-17.mzML ... 1366 peaks found!
## Detecting 210210-SWATH-NEG-Covid-Cov-18.mzML ... 646 peaks found!
## Detecting 210210-SWATH-NEG-Covid-Ct-1.mzML ... 637 peaks found!
## Detecting 210210-SWATH-NEG-Covid-Ct-2.mzML ... 142 peaks found!
## Detecting 210210-SWATH-NEG-Covid-Ct-3.mzML ... 56 peaks found!
## Warning: Found NA peaks in selected sample.
## Calculating peak correlations in 66 Groups...
## Calculating graph cross linking in 66 Groups...
## New number of ps-groups: 2241
## mSet has now 2241 groups, instead of 66 !
## Generating peak matrix for peak annotation!
## Polarity is set in annotaParam: negative
## Ruleset could not read from object! Recalculating...
## Calculating possible adducts in 2241 Groups...
## Step 6/6: Successfully performed peak annotation! (2023-10-02 14:42:47)
## Going to the final step...
## Format MS1 feature table
mSet1 <- FormatPeakList(mSet = mSet1,
annParams,
filtIso =FALSE,
filtAdducts = FALSE,
missPercent = 1)
Figure 6. 2D PCA score plot of all features
(colored based on group information)
mSet <- PerformMSnImport(mSet = mSet1,
filesPath = c(list.files("ms2_dia/ms2_dia_example/", pattern = ".mzML", full.names = T, recursive = T)),
acquisitionMode = "DIA",
SWATH_file = "ms2_dia/ms2_dia_example/DIA_SWATH_MS_experiment_file_neg.txt")
## Importing raw MS data .. done.
mSet <- PerformDIADeconvolution(mSet,
min_width = 5,
span = 0.3,
ppm2 = 30,
sn = 12,
filtering = 0,
ncores = 6L)
mSet <- PerformSpectrumConsenus (mSet,
ppm2 = 30,
concensus_fraction = 0.25,
database_path = "",
use_rt = FALSE,
user_dbCorrection = FALSE)
mSet <- PerformDBSearchingBatch (mSet,
ppm1 = 15,
ppm2 = 30,
rt_tol = 5,
database_path = "ms2_db/MS2ID_Bio_v09102023.sqlite",
use_rt = FALSE,
enableNL = FALSE,
ncores = 4L)
## ==== Database searching against MS2ID_Bio_v09102023.sqlite started ====
##
## ============ Searching finished successfully! ============
mSet <- PerformResultsExport (mSet,
type = 0L,
topN = 10L,
ncores = 4L)
## ======= Converting started. See progress below =======
##
## ============ Exporting finished successfully! ============
dtx2 <- FormatMSnAnnotation(mSet, 5L, F)
