# run Knit on R studio or
# Load MetaboAnalystR
library(MetaboAnalystR)
## MetaboAnalystR 4.0.0 initialized Successfully !
## https://github.com/xia-lab/MetaboAnalystR
# Load OptiLCMS
library(OptiLCMS)
## Loading required package: Biobase
## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
## Loading required package: BiocParallel
## Loading required package: MSnbase
## Loading required package: mzR
## Loading required package: Rcpp
## Loading required package: S4Vectors
## Loading required package: stats4
##
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:utils':
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## findMatches
## The following objects are masked from 'package:base':
##
## expand.grid, I, unname
## Loading required package: ProtGenerics
##
## Attaching package: 'ProtGenerics'
## The following object is masked from 'package:stats':
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## smooth
##
## This is MSnbase version 2.28.0
## Visit https://lgatto.github.io/MSnbase/ to get started.
##
## Attaching package: 'MSnbase'
## The following object is masked from 'package:base':
##
## trimws
## OptiLCMS 1.1.0 initialized Successfully !
##
## Attaching package: 'OptiLCMS'
## The following objects are masked from 'package:MetaboAnalystR':
##
## CentroidCheck, InitDataObjects, InitializaPlan, PerformDataInspect,
## PerformDataTrimming, PerformParamsOptimization,
## PerformPeakProfiling, PerformROIExtraction, plotMSfeature,
## plotSingleTIC, PlotXIC, SetPeakParam
download.file("https://www.xialab.ca/api/download/metaboanalyst/malaria_r_example.zip",
destfile = "malaria_raw.zip",
method = "wget")
unzip("malaria_raw.zip", exdir = "upload")
# Load MetaboAnalystR and OptiLCMS
library(MetaboAnalystR)
library(OptiLCMS)
# Here, we extract ROIs from 3 QC samples.
DataFiles <- list.files("upload/QC/", full.names = TRUE)
mSet <- PerformROIExtraction(datapath = DataFiles, rt.idx = 0.9, rmConts = TRUE);
## Step 0/6: Scanning ROIs for parameters optimization...
## QC_001.mzML
## QC_003.mzML
## QC_005.mzML
## Data Loading...
## Raw file import begin...
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Import data: QC_001.mzML finished!
##
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Import data: QC_003.mzML finished!
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Import data: QC_005.mzML finished!
## Data Loaded !
## Empty Scan detecting...
## No Empty scan found !
## Identifying regions of interest (ROI)...
## Identifying regions of potential contaminants........Done!
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70 potential contaminamts will not be used for parameters optimization !
## Going to the next step...
## MS data Preparing...
## MS Data ready !
## Identifying ROIs in m/z dimensions...
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Identifying ROIs in m/z dimensions Done !
## Identifying ROIs in RT dimensions...
## Chromatogram Plotting Begin...
## Identification on ROIs Finished!
# 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... (2023-10-31 10:00:13.887121)
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##
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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...
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## Round 1 Finished !
## Model Parsing...
## Gaussian peak ratio (%): 39.3
## Model Parsing Done !
##
## Round:2
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## Round 2 Finished !
## Model Parsing...
## Gaussian peak ratio (%): 42.6
## Model Parsing Done !
##
## Round:3
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## 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 ! (2023-10-31 10:49:33.587386)
## Time Spent In Total:49.3mins
# "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.
## Step 2/6: Successfully imported raw MS data! (2023-10-31 10:49:51.927451)
## Going to the next step...
# "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))
## 11 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 ! (2023-10-31 10:50:32.786173)
##
## 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 ! (2023-10-31 10:51:04.625043)
##
## 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 ! (2023-10-31 10:51:28.303494)
## Peak Profiling finished successfully !
## Begin to plotting figures...
## Done !
# 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..
##
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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...
##
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Calculating graph cross linking in 109 Groups...
##
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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...
##
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Step 6/6: Successfully performed peak annotation! (2023-10-31 10:52:31.809792)
## Going to the final step...
# 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);
data_folder_Output <- "."
# Export annotation results, the annotation will be save as "annotated_peaklist.csv";
Export.Annotation(mSet, path = data_folder_Output);
# 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, path = data_folder_Output);
## Done!
##
## Everything has been finished Successfully ! (2023-10-31 10:52:32.459335)
# 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, path = data_folder_Output)