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 PARAM SPEC
Description
default values for PARAM SPEC
Usage
data("CSA_PARAM_SPEC")Format
A data frame on the following 2 variables.
Parameter IDa character vector
User provided inputa numerical vector
Examples
data(CSA_PARAM_SPEC)
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 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 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 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)
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.
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.
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.
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.
Adducta character vector
massAdducta 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.
Adducta character vector
massAdducta numerical vector
Examples
data(positiveAdducts)