Package 'IDSL.CSA'

Title: Composite Spectra Analysis (CSA) for High-Resolution Mass Spectrometry Analyses
Description: A fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the 'IDSL.IPA' workflow <doi:10.1021/acs.jproteome.2c00120>. The 'IDSL.CSA' package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The 'IDSL.CSA' package was introduced in <doi:10.1021/acs.analchem.3c00376>.
Authors: Sadjad Fakouri-Baygi [aut] , Dinesh Barupal [cre, aut]
Maintainer: Dinesh Barupal <[email protected]>
License: MIT + file LICENSE
Version: 1.2
Built: 2026-05-20 05:46:52 UTC
Source: https://github.com/idslme/idsl.csa

Help Index

Aligned Fragmentation Spectra Annotator

Description

This function detects frequent matched compounds across multiple samples on the aligned peak table matrix.

Usage

aligned_fragmentation_spectra_annotator(PARAM_AT, output_path)

Arguments

PARAM_AT

a parameter driven from the 'CSA_AlignedTable_xlsxAnalyzer' module.

output_path

output path

Value

This function stores '.Rdata' and '.csv' data from dataframe of aligned fragmentation spectra.

CSA Adduct Annotator

Description

This function updates IDSL.IPA peaklists with IDSL.CSA grouping

Usage

CSA_adductAnnotator(IPApeakList, CSA_peaklist, massError)

Arguments

IPApeakList

IDSL.IPA peaklist

CSA_peaklist

A dataframe peaklist of co-detected CSA analysis.

massError

Mass accuracy in Da

Value

IDSL.IPA peaklists with IDSL.CSA grouping

CSA Aligned Meta-Spectra Cataloger

Description

This function generates integrated and most abundant aligned spectra from the aligned spectra

Usage

CSA_alignedMetaSpectraCataloger(address_input_msp, peakXcol, peak_height,
CSA_aligned_property_table, groupedID, minTanimotoCoefficient = 0.5,
number_processing_threads = 1)

Arguments

address_input_msp

address of the .msp files generated via IDSL.IPA DIA grouping

peakXcol

aligned indexed peak table

peak_height

aligned peak height table

CSA_aligned_property_table

a matrix of three columns of "IPA detection frequency", "median_height", and "median_R13C" for the aligned peak table

groupedID

A 2-column dataframe of 'Co-detectedIDs' and 'TanimotoCoefficients' from the 'CSA_alignedPeaksTanimotoCoefficientCalculator' module

minTanimotoCoefficient

minimum Tanimoto coefficient

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A list of two objects for 'MSP_integrated_aligned_spectra' and 'MSP_most_abundant_aligned_spectra'

CSA Aligned Peaks Tanimoto Coefficient Calculator

Description

This function groups co-detected peaks on the aligned table.

Usage

CSA_alignedPeaksTanimotoCoefficientCalculator(address_input_msp, peakXcol,
minPercenetageDetection = 5, minNumberFragments = 2, minTanimotoCoefficient = 0.1,
RTtolerance = 0.05, number_processing_threads = 1)

Arguments

address_input_msp

address of the .msp files generated via IDSL.IPA CSA aggregation

peakXcol

aligned indexed peak table

minPercenetageDetection

minimum CSA frequency detection

minNumberFragments

minimum frequency

minTanimotoCoefficient

minimum Tanimoto coefficient

RTtolerance

retention time tolerance to detect common peaks

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A 2-column dataframe of 'Co-detectedIDs' and 'TanimotoCoefficients'

CSA Aligned Table xlsx Analyzer

Description

This function processes the spreadsheet of the 'AlignedTable' tab to ensure the parameter inputs are consistent with the requirements of the IDSL.CSA pipeline.

Usage

CSA_AlignedTable_xlsxAnalyzer(spreadsheet)

Arguments

spreadsheet

'AlignedTable' tab of the parameter spreadsheet

Value

This function returns the aligned table parameters to feed the 'aligned_fragmentation_spectra_annotator' function.

CSA peakList MSP generation

Description

This function detects fragmentation peaks for the Composite Spectra Analysis (CSA) using IDSL.IPA peaklists.

Usage

CSA_fragmentationPeakDetection(CSA_hrms_address, CSA_hrms_file,
tempAlignedTableSubsetsFolder = NULL, peaklist, selectedIPApeaks = NULL,
RTtolerance, massError, minSNRbaseline, smoothingWindowMS1, scanTolerance, nSpline,
topRatioPeakHeight, minIonRangeDifference, minNumCSApeaks, pearsonRHOthreshold,
outputCSAeic = NULL)

Arguments

CSA_hrms_address

path to the HRMS file

CSA_hrms_file

CSA HRMS file

tempAlignedTableSubsetsFolder

tempAlignedTableSubsetsFolder

peaklist

IDSL.IPA peaklist

selectedIPApeaks

A vector of selected IDSL.IPA peaks only when a number of IDSL.IPA peaks from one peaklist is processed. When 'NULL' is selected, the entire peaks in the peaklist are processed.

RTtolerance

retention time tolerance to detect common peaks

massError

Mass accuracy in Da

minSNRbaseline

A minimum baseline S/N threshold for IDSL.IPA pseudo-precursor m/z

smoothingWindowMS1

number of scans for peak smoothing.

scanTolerance

a scan tolerance to extend the chromatogram for better calculations.

nSpline

number of points for further smoothing using a cubic spline smoothing method to add more points to calculate Pearson correlation rho values

topRatioPeakHeight

The top percentage of the chromatographic peak to calculate Pearson correlation rho values

minIonRangeDifference

Minimum distance (Da) between lowest and highest m/z to prevent clustering isotopic envelopes

minNumCSApeaks

Minumum number of ions in a CSA cluster

pearsonRHOthreshold

Minimum threshold for Pearson correlation rho values

outputCSAeic

When 'NULL' CSA EICs are not plotted. 'outputCSAeic' represents an address to save CSA EICs figures.

Value

A dataframe peaklist of co-detected CSA analysis.

References

[1] Fakouri Baygi, S., Kumar, Y., Barupal, D.K. (2022). IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets. Journal of Proteome Research, 21(6), 1485-1494, doi:10.1021/acs.jproteome.2c00120

[2] Fakouri Baygi, S., Fernando, S., Hopke, P.K., Holsen, T.M., Crimmins, B.S. (2021). Nontargeted discovery of novel contaminants in the Great Lakes region: A comparison of fish fillets and fish consumers. Environmental Science & Technology, 55(6), 3765-3774, doi:10.1021/acs.est.0c08507

CSA PARAM SPEC

Description

default values for PARAM SPEC

Usage

data("CSA_PARAM_SPEC")

Format

A data frame on the following 2 variables.

Parameter ID

a character vector

User provided input

a numerical vector

Examples

data(CSA_PARAM_SPEC)

CSA reference xlsxAnalyzer

Description

CSA reference xlsxAnalyzer

Usage

CSA_reference_xlsxAnalyzer(ref_xlsx_file, input_path_hrms = NULL, PARAM = NULL,
PARAM_ID = "", checkpoint_parameter = TRUE)

Arguments

ref_xlsx_file

ref_xlsx_file

input_path_hrms

input_path_hrms

PARAM

PARAM

PARAM_ID

PARAM_ID

checkpoint_parameter

checkpoint_parameter

Value

ref_table

ref_table

PARAM

PARAM

checkpoint_parameter

checkpoint_parameter

CSA workflow

Description

This function executes the CSA workflow.

Usage

CSA_workflow(PARAM_CSA)

Arguments

PARAM_CSA

PARAM_CSA

Value

This module generates '.msp' files from DDA analysis.

Examples

s_path <- system.file("extdata", package = "IDSL.CSA")
SSh1 <- paste0(s_path,"/CSA_parameters.xlsx")
## To see the results, use a known folder instead of the `tempdir()` command
temp_wd <- tempdir()
temp_wd_zip <- paste0(temp_wd, "/idsl_csa_test_files.zip")
spreadsheet <- readxl::read_xlsx(SSh1, sheet = "CSA")
PARAM_CSA <- cbind(spreadsheet[, 2], spreadsheet[, 4])
download.file(paste0("https://github.com/idslme/IDSL.CSA/blob/main/",
                     "CSA_educational_files/idsl_csa_test_files.zip?raw=true"),
              destfile = temp_wd_zip, mode = "wb")
unzip(temp_wd_zip, exdir = temp_wd)
PARAM_CSA[2, 2] <- "NO"
PARAM_CSA[3, 2] <- "NO"
PARAM_CSA[5, 2] <- temp_wd
PARAM_CSA[8, 2] <- temp_wd
PARAM_CSA[9, 2] <- "NA"
PARAM_CSA[11, 2] <- temp_wd
## To ensure `PARAM_CSA` is consistent with the `CSA_workflow`
PARAM_CSA <- CSA_xlsxAnalyzer(PARAM_CSA)
##
CSA_workflow(PARAM_CSA)

CSA xlsx Analyzer

Description

This function processes the spreadsheet of the CSA parameters to ensure the parameter inputs are consistent with the requirements of the IDSL.CSA pipeline.

Usage

CSA_xlsxAnalyzer(spreadsheet)

Arguments

spreadsheet

CSA tab of the parameter spreadsheet

Value

This function returns the CSA parameters to feed the 'CSA_workflow' function.

DDA Fragmentation Peaks Detection

Description

This function detects fragmentation peaks for the Data-Dependent Acquisition (DDA) analysis.

Usage

DDA_fragmentationPeakDetection(DDA_hrms_address, DDA_hrms_file, peaklist,
selectedIPApeaks, massErrorPrecursor, DDAprocessingMode = 'MostIntenseDDAspectra',
outputDDAspectra = NULL, number_processing_threads = 1)

Arguments

DDA_hrms_address

path to the HRMS file

DDA_hrms_file

DDA HRMS file

peaklist

IDSL.IPA peaklist

selectedIPApeaks

A vector of selected IDSL.IPA peaks only when a number of IDSL.IPA peaks from one peaklist is processed.

massErrorPrecursor

Mass accuracy (Da) to find precursor m/z in IDSL.IPA peaklists

DDAprocessingMode

c('MostIntenseDDAspectra', c('DDAspectraIntegration', massErrorIntegration), c('IonFiltering', massErrorIonFiltering, minPercentageDetectedScans, rsdCutoff, pearsonRHOthreshold)). Required variables for each DDA processing mode should be provided in this vector.

outputDDAspectra

When 'NULL' DDA spectra are not plotted. 'outputDDAspectra' represents an address to save DDA spectra figures.

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A dataframe peaklist of co-detected DDA analysis.

DDA Raw Spectra Deconvolution

Description

This function stacks all DDA scans.

Usage

DDA_rawSpectraDeconvolution(DDA_hrms_address, DDA_hrms_file, rawDDAspectraVar = NULL,
number_processing_threads = 1)

Arguments

DDA_hrms_address

path to the HRMS file

DDA_hrms_file

DDA HRMS file

rawDDAspectraVar

c(NULL, list(precursorMZvec, precursorRTvec, massError, RTtolerance)). When NULL, all scans with precursor values are used for DDA peaklist generation. When the list is provided, it filters the scans with respect to predefined 'precursorMZvec' and 'precursorRTvec' values.

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A dataframe stacked DDA scans.

DDA Workflow

Description

This function runs the Data-Dependent Acquisition (DDA) analysis.

Usage

DDA_workflow(PARAM_DDA)

Arguments

PARAM_DDA

DDA parameters

Value

This module generates '.msp' files from DDA analysis.

Examples

s_path <- system.file("extdata", package = "IDSL.CSA")
SSh1 <- paste0(s_path,"/CSA_parameters.xlsx")
## To see the results, use a known folder instead of the `tempdir()` command
temp_wd <- tempdir()
temp_wd_zip <- paste0(temp_wd, "/idsl_dda_test_files.zip")
spreadsheet <- readxl::read_xlsx(SSh1, sheet = "DDA")
PARAM_DDA <- cbind(spreadsheet[, 2], spreadsheet[, 4])
download.file(paste0("https://github.com/idslme/IDSL.CSA/blob/main/",
                     "CSA_educational_files/idsl_dda_test_files.zip?raw=true"),
              destfile = temp_wd_zip, mode = "wb")
unzip(temp_wd_zip, exdir = temp_wd)
PARAM_DDA[2, 2] <- "no"
PARAM_DDA[4, 2] <- temp_wd
PARAM_DDA[7, 2] <- temp_wd
PARAM_DDA[8, 2] <- "NA"
PARAM_DDA[11, 2] <- temp_wd
## To ensure `PARAM_DDA` is consistent with the `DDA_workflow`
PARAM_DDA <- DDA_xlsxAnalyzer(PARAM_DDA)
##
DDA_workflow(PARAM_DDA)

xlsx Analyzer for DDA analysis

Description

This function processes the spreadsheet of the DDA spreadsheet tab to ensure the parameter inputs are in agreement with requirements of the Data-Dependent Acquisition (DDA) analysis.

Usage

DDA_xlsxAnalyzer(spreadsheet)

Arguments

spreadsheet

DDA spreadsheet tab

Value

DDA parameters to feed the 'DDA_workflow' function.

DDA to msp

Description

DDA to msp

Usage

DDA2msp(input_path_hrms, file_name_hrms = NULL, number_processing_threads = 1)

Arguments

input_path_hrms

path to the HRMS file

file_name_hrms

file_name_hrms

number_processing_threads

Number of processing threads for multi-threaded processing

Value

This module generates '.msp' files from DDA analysis.

Examples

## To see the results, use a known folder instead of the `tempdir()` command
temp_wd <- tempdir()
temp_wd_zip <- paste0(temp_wd, "/idsl_rawdda_test_files.zip")
download.file(paste0("https://github.com/idslme/IDSL.CSA/blob/main/",
                     "CSA_educational_files/idsl_rawdda_test_files.zip?raw=true"),
              destfile = temp_wd_zip, mode = "wb")
unzip(temp_wd_zip, exdir = temp_wd)
DDA2msp(input_path_hrms = temp_wd, file_name_hrms = NULL, number_processing_threads = 1)

CSA DIA MS1 Fragmentation Peaks Detection

Description

This function detects fragmentation peaks for the Data-Independent Acquisition (DIA) analysis at ms level 1.

Usage

DIA_MS1_fragmentationPeakDetection(DIA_hrms_address, DIA_hrms_file, peaklist,
selectedIPApeaks, massError, smoothingWindowMS1, scanTolerance, nSpline,
topRatioPeakHeight, intensityThresholdFragment, pearsonRHOthreshold, outputDIAeic = NULL,
number_processing_threads = 1)

Arguments

DIA_hrms_address

path to the HRMS file

DIA_hrms_file

DIA HRMS file

peaklist

IDSL.IPA peaklist

selectedIPApeaks

A vector of selected IDSL.IPA peaks only when a number of IDSL.IPA peaks from one peaklist is processed.

massError

Mass accuracy in Da

smoothingWindowMS1

number of scans for peak smoothing.

scanTolerance

a scan tolerance to extend the chromatogram for better calculations.

nSpline

number of points for further smoothing using a cubic spline smoothing method to add more points to calculate Pearson correlation rho values

topRatioPeakHeight

The top percentage of the chromatographic peak to calculate Pearson correlation rho values

intensityThresholdFragment

a value to represent intensity threshold for the fragment at the apex chromatogram scan

pearsonRHOthreshold

Minimum threshold for Pearson correlation rho values

outputDIAeic

When 'NULL' DIA EICs are not plotted. 'outputDIAeic' represents an address to save DIA EICs figures.

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A dataframe peaklist of co-detected DIA analysis.

References

Fakouri Baygi, S., Fernando, S., Hopke, P.K., Holsen, T.M., Crimmins, B.S. (2021). Nontargeted discovery of novel contaminants in the Great Lakes region: A comparison of fish fillets and fish consumers. Environmental Science & Technology, 55(6), 3765-3774, doi:10.1021/acs.est.0c08507

CSA DIA MS2 Fragmentation Peaks Detection

Description

This function detects fragmentation peaks for the DIA analysis at MS level 2.

Usage

DIA_MS2_fragmentationPeakDetection(DIA_hrms_address, DIA_hrms_file, peaklist,
selectedIPApeaks, massError, smoothingWindowMS1, smoothingWindowMS2,
scanTolerance, nSpline, topRatioPeakHeight, intensityThresholdFragment,
pearsonRHOthreshold,  outputDIAeic = NULL, number_processing_threads = 1)

Arguments

DIA_hrms_address

path to the HRMS file

DIA_hrms_file

DIA HRMS file

peaklist

IDSL.IPA peaklist

selectedIPApeaks

A vector of selected IDSL.IPA peaks only when a number of IDSL.IPA peaks from one peaklist is processed.

massError

Mass accuracy in Da

smoothingWindowMS1

Number of scans for peak smoothing in MS1 channel

smoothingWindowMS2

Number of scans for peak smoothing in MS2 channel

scanTolerance

a scan tolerance to extend the chromatogram for better calculations.

nSpline

number of points for further smoothing using a cubic spline smoothing method to add more points to calculate Pearson correlation rho values

topRatioPeakHeight

The top percentage of the chromatographic peak to calculate Pearson correlation rho values

intensityThresholdFragment

a value to represent intensity threshold for the fragment at the apex chromatogram scan in MS2 channel

pearsonRHOthreshold

Minimum threshold for Pearson correlation rho values

outputDIAeic

When 'NULL' DIA EICs are not plotted. 'outputDIAeic' represents an address to save DIA EICs figures.

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A dataframe peaklist of co-detected DIA analysis.

References

Fakouri Baygi, S., Fernando, S., Hopke, P.K., Holsen, T.M., Crimmins, B.S. (2021). Nontargeted discovery of novel contaminants in the Great Lakes region: A comparison of fish fillets and fish consumers. Environmental Science & Technology, 55(6), 3765-3774, doi:10.1021/acs.est.0c08507

DIA Workflow

Description

This function runs the Data-Independent Acquisition (DIA) analysis.

Usage

DIA_workflow(PARAM_DIA)

Arguments

PARAM_DIA

DIA parameters

Value

This module generates '.msp' files from DDA analysis.

DIA xlsx Analyzer for DIA analysis

Description

This function processes the spreadsheet of the DIA spreadsheet tab to ensure the parameter inputs are in agreement with requirements of the Data-Independent Acquisition (DIA) analysis.

Usage

DIA_xlsxAnalyzer(spreadsheet)

Arguments

spreadsheet

DIA spreadsheet tab

Value

DIA parameters to feed the 'DIA_workflow' function.

IDSL.CSA MSP Generator

Description

This function creates standard .msp files that can also be used for Pepsearch.

Usage

IDSL.CSA_MSPgenerator(CSA_peaklist, msLevel, spectral_search_mode = "dda", 
spectral_search_mode_option = NA, number_processing_threads = 1)

Arguments

CSA_peaklist

A dataframe peaklist of co-detected peaks

spectral_search_mode

Type of analysis. spectral_search_mode = c("dda", "dia", "csa")

msLevel

MS level = c(1, 2)

spectral_search_mode_option

Secondary type of analysis. spectral_search_mode_option = c(NA, "rawddaspectra", "alignedtable")

number_processing_threads

Number of processing threads for multi-threaded processing

Value

A string of standard .msp file

IDSL.CSA Reference MSP Generator

Description

This function creates reference standard .msp files.

Usage

IDSL.CSA_referenceMSPgenerator(REF_peaklist, refTable, selectedIPApeaks_IDref, msLevel,
spectral_search_mode = "dda", spectral_search_mode_option = NA)

Arguments

REF_peaklist

A dataframe peaklist of co-detected peaks

refTable

reference CSA table

selectedIPApeaks_IDref

selectedIPApeaks_IDref

msLevel

MS level = c(1, 2)

spectral_search_mode

Type of analysis. spectral_search_mode = c("dda", "dia", "csa")

spectral_search_mode_option

Secondary type of analysis. spectral_search_mode_option = c(NA, "rawddaspectra", "alignedtable")

Value

A string of standard .msp file

IDSL.CSA workflow

Description

This function executes the CSA workflow.

Usage

IDSL.CSA_workflow(spreadsheet)

Arguments

spreadsheet

CSA spreadsheet

Value

This function organizes the IDSL.CSA file processing for better performance using the template spreadsheet.

IDSL.CSA workflow xlsx Analyzer

Description

This function processes the spreadsheet of the CSA parameters to ensure the parameter inputs are consistent with the requirements of the IDSL.CSA pipeline.

Usage

IDSL.CSA_xlsxAnalyzer(spreadsheet)

Arguments

spreadsheet

'Start' tab of the parameter spreadsheet

Value

This function returns the CSA parameters to feed the 'IDSL.CSA_workflow' function.

negative adducts

Description

This data consists of adducts and mass differences for common ionization pathways in negative modes.

Usage

data("negativeAdducts")

Format

A data frame on the following 2 variables.

Adduct

a character vector

massAdduct

a numerical vector

Examples

data(negativeAdducts)

positive adducts

Description

This data consists of adducts and mass differences for common ionization pathways in positive modes.

Usage

data("positiveAdducts")

Format

A data frame on the following 2 variables.

Adduct

a character vector

massAdduct

a numerical vector

Examples

data(positiveAdducts)