In jasenfinch/binneRlyse: Spectral binning and accurate m/z extraction for FIE-HRMS metabolomics analyses
Introduction
The binneRlyse package provides a formalisation of the spectral binning approach by the binneR package. This is for routine processing of high resolution FIE-MS metabolomics fingerprinting experiments, the results of which can then be used for subsequent statistical analyses.
Spectral binning consists of the rounding of high resolution fingerprinting data by a specified amu bin width.
FIE-HRMS data consists of a 'plug flow', across which MS signal intensities can be averaged to provide a metabolome fingerprint.
Spectral binning is applied on a scan by scan basis where the data is rounded to the specified bin width, the signals are then sum aggregated and then their intensities are averaged across the specified scans.
Below is indicated the 'plug flow' region of an example FIE-MS chromatogram and the resulting spectrum when signal intensities are averaged across these scans.
chrom <- mzR::openMSfile(system.file(
'DataSets/FIE-HRMS/BdistachyonEcotypes/QC01.mzML',
package = 'metaboData'))
chrom <- mzR::header(chrom)
chrom <- chrom[seq(1,nrow(chrom),2),]
chrom$acquisitionNum <- 1:nrow(chrom)
spectrum <- binneR::readFiles(system.file(
'DataSets/FIE-HRMS/BdistachyonEcotypes/QC01.mzML',
package = 'metaboData'),scans = 6:12,sranges = list(c(70,1000)),dp = 2)$n
spectrum <- tidyr::gather(tibble::as_tibble(spectrum),'mz','Intensity')
spectrum <- dplyr::mutate(spectrum,mz = as.numeric(stringr::str_replace(mz,'[:alpha:]','')))
p <- list()
p$chromatogram <- ggplot2::ggplot(chrom,ggplot2::aes(x = acquisitionNum,y = totIonCurrent)) +
ggplot2::geom_line() +
ggplot2::geom_vline(xintercept = 6, linetype = "dashed",colour = 'red') +
ggplot2::geom_vline(xintercept = 12, linetype = "dashed",colour = 'red') +
ggplot2::theme_bw(base_size = 10) +
ggplot2::xlab('Scan Number') +
ggplot2::ylab('Total Ion Count') +
ggplot2::ggtitle('Chromatogram')
p$spectrum <- ggplot2::ggplot(spectrum,ggplot2::aes(x = mz, y = 0, xend = mz, yend = Intensity)) +
ggplot2::geom_segment() +
ggplot2::theme_bw() +
ggplot2::xlab('m/z') +
ggplot2::ylab(('Abundance')) +
ggplot2::ggtitle('Spectrum')
gridExtra::grid.arrange(gridExtra::arrangeGrob(p$chromatogram,p$spectrum))
Prior to the use of binneRlyse, vendor specific raw data files need to be converted to one of the open source file formats such as .mzXML or .mzML so that they can be parsed into R.
Data should also be centroided to reduce bin splitting artefacts that profile data can introduce during spectral binning.
The msconvert tool can be used for both data conversion and centroiding, allowing the use of vendor specific algorithms.
For a given set of experimental samples, binneRlyse will bin these to both 0.01 and 0.00001 amu.
The 0.00001 data will then be aggregated based on a specified class structure from which the modal accurate m/z is extracted for each 0.01 amu bin.
Some bin measures are also computed that allow the assessment of the quality of the 0.01 amu bins.
Subsequent analyses of these data can easily be applied using the metabolyseR package.
The metaboWorkflows package also provides customisable wrapper workflows for high resolution FIE-MS analyses.
The example data used here is from the metaboData package and consists of a comparison of four B. distachyon ecotypes.
This document will provide an overview of how to use the package as well as a discussion of the bin measures computed to assess bin quality.
Basic Usage
There are two main functions for processing data using binneRlyse:
binParameters() - allows the selection of processing parameters.binneRlyse() - input data file paths and sample information to process using the selected parameters.
Sample information
binneRlyse requires the provision of sample information (info) for the experimental run to be processed.
This should be in csv format and the recommended column headers include:
- fileOrder - the file order in alphabetical order as returned by
list.files()
- injOrder - the injection order of the samples during FIE-HRMS analysis
- fileName - the sample file name
- batch - the sample batch
- batchBlock - the randomised block of the sample
- name - the sample name
- class - the sample class
The row orders of the info file should match the order in which the files paths are submitted to the binneRlyse() processing function.
Parameters
Prior to spectral binning the processing parameters first need to be selected.
The default parameters can be initialised a BinParameters object using the binParameters() function as shown below.
library(binneRlyse)
binParameters()
These parameters specify the following:
scans - the scan indexes to use for binningmodes - the scan order and names of the ionisation modessranges - a list of vectors containing minimum and maximum ranges for the scan events presentcls - the column of the info that contains class information if relevant nCores - the number of cores to use for parallelisationclusterType - the cluster type to use for parallelisation
Parameters can be altered upon initialisation of the BinParameters by specifying the parameter and it's value upon calling the binParameters function as shown below.
binParameters(scans = 6:14)
Alternatively for and already initialised BinParameters object, the slot containing the parameter of interest can be changed by directly accessing the slot as shown below.
parameters <- binParameters()
parameters@scans <- 6:14
parameters
Processing
Processing is simple and requires only the use of the binneRlyse() function.
The input of this function is a vector of the paths of the data files to process, a tibble containing the sample info and BinParameters object.
Below shows the files and info inputs for the example data set.
library(readr)
files <- list.files(
system.file(
'DataSets/FIE-HRMS/BdistachyonEcotypes',
package = 'metaboData'),
full.names = TRUE)
info <- readr::read_csv(files[grepl('runinfo',files)])
files <- files[!grepl('runinfo',files)]
head(files)
info
It is crucial that the positions of the sample information in the info file match the sample positions within the files vector.
Below shows an example of how this can be checked by matching the file names present in the info with those in the vector.
fileNames <- list.files(
system.file(
'DataSets/FIE-HRMS/BdistachyonEcotypes',
package = 'metaboData'))
fileNames <- fileNames[-grep('runinfo',fileNames)]
FALSE %in% (info$fileName == fileNames)
Spectral binning using the default parameters can then be performed with the following.
analysis <- binneRlyse(files,info,binParameters())
analysis <- binneRlyse(files,info,binParameters(nCores = 2))
analysis
Data Extraction
There are a number of functions that can be used to return processing data from a Binalysis object:
info() for sample informationbinnedData() for the spectrally binned matricesaccurateData() for the accurate mass information for each of the 0.01 amu bins
Bin Measures
binneRlyse provides a number of measures that allow the assessment of the quality of a given 0.01 amu bin in terms of the accurate m/z peaks present within its boundaries. These include both purity and centrality.
dat <- binneR::readFiles(system.file(
'DataSets/FIE-HRMS/BdistachyonEcotypes/QC01.mzML',
package = 'metaboData'),scans = 6:12,sranges = list(c(70,1000)),dp = 5)$n
dat <- tidyr::gather(tibble::as_tibble(dat),'mz','Intensity')
dat <- dplyr::mutate(dat,mz = as.numeric(stringr::str_replace(mz,'[:alpha:]','')))
dat <- dplyr::mutate(dat,bin = round(mz,2))
measures <- dplyr::group_by(dat,bin)
measures <- dplyr::summarise(measures,purity = binneRlyse:::binPurity(mz,Intensity),centrality = binneRlyse:::binCentrality(mz,Intensity),Intensity = mean(Intensity))
Purity
Bin purity gives a measure of the spread of accurate m/z peaks found within a given bin and can be a signal for the presences of multiple real spectral peaks within a bin.
Purity for a given bin is calculated using the equation below.
$$p = 1 - \frac{\sigma}{w} $$
Where p is purity, $\sigma$ is the standard deviation of the accurate m/z present within the bin and w is the width of the bin in amu.
A purity closer to 1 indicates that the accurate m/z present within a bin are found over a narrow region and therefore likely only to be as the result of 1 real mass spectral peak.
A reduction in purity could indicate the presence of multiple peaks present within a bin.
Below shows example density plots of two negative ionisation mode 0.01 amu bins showing high (133.01) and low (98.96) purity respectively.
Pure <- dplyr::filter(measures,bin == 133.01)
Pure <- dplyr::mutate(Pure, purity = paste('Purity = ',round(purity,3), sep = ''))
pure <- dplyr::filter(dat,bin == Pure$bin)
pure <- tibble::tibble(mz = unlist(apply(pure,1,function(x){rep(x[1],x[2])})))
Impure <- dplyr::filter(measures,bin == 98.96)
Impure <- dplyr::mutate(Impure, purity = paste('Purity = ',round(purity,3), sep = ''))
impure <- dplyr::filter(dat,bin == Impure$bin)
impure <- tibble::tibble(mz = unlist(apply(impure,1,function(x){rep(x[1],x[2])})))
p <- list()
p$pure <- ggplot2::ggplot(pure,ggplot2::aes(x = mz)) +
ggplot2::geom_density() +
ggplot2::theme_bw() +
ggplot2::xlim(Pure$bin - 0.005,Pure$bin + 0.005) +
ggplot2::ggtitle(paste(Pure$bin,'\t',Pure$purity)) +
ggplot2::xlab('m/z') +
ggplot2::ylab('Density')
p$impure <- ggplot2::ggplot(impure,ggplot2::aes(x = mz)) +
ggplot2::geom_density() +
ggplot2::theme_bw() +
ggplot2::xlim(Impure$bin - 0.005,Impure$bin + 0.005) +
ggplot2::ggtitle(paste(Impure$bin,'\t',Impure$purity)) +
ggplot2::xlab('m/z') +
ggplot2::ylab('Density')
gridExtra::grid.arrange(gridExtra::arrangeGrob(p$pure,p$impure))
Bin 133.01, that has a purity very close to 1, has only one peak present.
Bin 98.96, that has a reduced purity, clearly has two peaks present.
Centrality
Bin centrality gives a measure of how close the mean of the accurate m/z are to the center of a given bin and can give indication of whether a peak could have been split between the boundary of tow adjacent bins.
Centrality is calculated for a given bin using the equation below.
$$ c = 1 - \frac{\sqrt{(\mu - k)^2}}{\frac{1}{2}w}$$
Where c is centrality, $\mu$ is the mean accurate m/z present in the bin, k is the center of the bin and w is the bin width in amu.
A centrality close to 1 indicates that the accurate m/z present within the boundaries of the bin are located close to the center of the bin.
Low centrality would indicate that the accurate m/z present within the bin are found close to the bin boundary and could therefore indicate bin splitting, were an mass spectral peak is split between two adjacent bins.
Below shows example density plots of two negative ionisation mode 0.01 amu bins showing high (88.04) and low (128.03) centrality respectively.
Pure <- dplyr::filter(measures,bin == 88.04)
Pure <- dplyr::mutate(Pure, centrality = paste('Centrality = ',round(centrality,3), sep = ''))
pure <- dplyr::filter(dat,bin == Pure$bin)
pure <- tibble::tibble(mz = unlist(apply(pure,1,function(x){rep(x[1],x[2])})))
Impure <- dplyr::filter(measures,bin == 128.03)
Impure <- dplyr::mutate(Impure, centrality = paste('Centrality = ',round(centrality,3), sep = ''))
impure <- dplyr::filter(dat,bin == Impure$bin)
impure <- tibble::tibble(mz = unlist(apply(impure,1,function(x){rep(x[1],x[2])})))
p <- list()
p$pure <- ggplot2::ggplot(pure,ggplot2::aes(x = mz)) +
ggplot2::geom_density() +
ggplot2::theme_bw() +
ggplot2::xlim(Pure$bin - 0.005,Pure$bin + 0.005) +
ggplot2::ggtitle(paste(Pure$bin,'\t',Pure$centrality)) +
ggplot2::xlab('m/z') +
ggplot2::ylab('Density')
p$impure <- ggplot2::ggplot(impure,ggplot2::aes(x = mz)) +
ggplot2::geom_density() +
ggplot2::theme_bw() +
ggplot2::xlim(Impure$bin - 0.005,Impure$bin + 0.005) +
ggplot2::ggtitle(paste(Impure$bin,'\t',Impure$centrality)) +
ggplot2::xlab('m/z') +
ggplot2::ylab('Density')
gridExtra::grid.arrange(gridExtra::arrangeGrob(p$pure,p$impure))
Bin 88.04 has a high centrality with single peak that is located very close to the center of the bin.
Whereas bin 128.03 as low centrality with a single peak that is located very close to the upper boundary of the bin and has likely been split between this bin and bin 128.04.
jasenfinch/binneRlyse documentation built on May 29, 2019, 4:51 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Introduction
The binneRlyse package provides a formalisation of the spectral binning approach by the binneR package. This is for routine processing of high resolution FIE-MS metabolomics fingerprinting experiments, the results of which can then be used for subsequent statistical analyses.
Spectral binning consists of the rounding of high resolution fingerprinting data by a specified amu bin width. FIE-HRMS data consists of a 'plug flow', across which MS signal intensities can be averaged to provide a metabolome fingerprint. Spectral binning is applied on a scan by scan basis where the data is rounded to the specified bin width, the signals are then sum aggregated and then their intensities are averaged across the specified scans. Below is indicated the 'plug flow' region of an example FIE-MS chromatogram and the resulting spectrum when signal intensities are averaged across these scans.
chrom <- mzR::openMSfile(system.file( 'DataSets/FIE-HRMS/BdistachyonEcotypes/QC01.mzML', package = 'metaboData')) chrom <- mzR::header(chrom) chrom <- chrom[seq(1,nrow(chrom),2),] chrom$acquisitionNum <- 1:nrow(chrom) spectrum <- binneR::readFiles(system.file( 'DataSets/FIE-HRMS/BdistachyonEcotypes/QC01.mzML', package = 'metaboData'),scans = 6:12,sranges = list(c(70,1000)),dp = 2)$n spectrum <- tidyr::gather(tibble::as_tibble(spectrum),'mz','Intensity') spectrum <- dplyr::mutate(spectrum,mz = as.numeric(stringr::str_replace(mz,'[:alpha:]',''))) p <- list() p$chromatogram <- ggplot2::ggplot(chrom,ggplot2::aes(x = acquisitionNum,y = totIonCurrent)) + ggplot2::geom_line() + ggplot2::geom_vline(xintercept = 6, linetype = "dashed",colour = 'red') + ggplot2::geom_vline(xintercept = 12, linetype = "dashed",colour = 'red') + ggplot2::theme_bw(base_size = 10) + ggplot2::xlab('Scan Number') + ggplot2::ylab('Total Ion Count') + ggplot2::ggtitle('Chromatogram') p$spectrum <- ggplot2::ggplot(spectrum,ggplot2::aes(x = mz, y = 0, xend = mz, yend = Intensity)) + ggplot2::geom_segment() + ggplot2::theme_bw() + ggplot2::xlab('m/z') + ggplot2::ylab(('Abundance')) + ggplot2::ggtitle('Spectrum') gridExtra::grid.arrange(gridExtra::arrangeGrob(p$chromatogram,p$spectrum))
Prior to the use of binneRlyse, vendor specific raw data files need to be converted to one of the open source file formats such as .mzXML or .mzML so that they can be parsed into R. Data should also be centroided to reduce bin splitting artefacts that profile data can introduce during spectral binning. The msconvert tool can be used for both data conversion and centroiding, allowing the use of vendor specific algorithms.
For a given set of experimental samples, binneRlyse will bin these to both 0.01 and 0.00001 amu. The 0.00001 data will then be aggregated based on a specified class structure from which the modal accurate m/z is extracted for each 0.01 amu bin. Some bin measures are also computed that allow the assessment of the quality of the 0.01 amu bins.
Subsequent analyses of these data can easily be applied using the metabolyseR package. The metaboWorkflows package also provides customisable wrapper workflows for high resolution FIE-MS analyses.
The example data used here is from the metaboData package and consists of a comparison of four B. distachyon ecotypes.
This document will provide an overview of how to use the package as well as a discussion of the bin measures computed to assess bin quality.
Basic Usage
There are two main functions for processing data using binneRlyse:
binParameters()- allows the selection of processing parameters.binneRlyse()- input data file paths and sample information to process using the selected parameters.
Sample information
binneRlyse requires the provision of sample information (info) for the experimental run to be processed. This should be in csv format and the recommended column headers include:
- fileOrder - the file order in alphabetical order as returned by
list.files() - injOrder - the injection order of the samples during FIE-HRMS analysis
- fileName - the sample file name
- batch - the sample batch
- batchBlock - the randomised block of the sample
- name - the sample name
- class - the sample class
The row orders of the info file should match the order in which the files paths are submitted to the binneRlyse() processing function.
Parameters
Prior to spectral binning the processing parameters first need to be selected.
The default parameters can be initialised a BinParameters object using the binParameters() function as shown below.
library(binneRlyse) binParameters()
These parameters specify the following:
scans- the scan indexes to use for binningmodes- the scan order and names of the ionisation modessranges- a list of vectors containing minimum and maximum ranges for the scan events presentcls- the column of the info that contains class information if relevantnCores- the number of cores to use for parallelisationclusterType- the cluster type to use for parallelisation
Parameters can be altered upon initialisation of the BinParameters by specifying the parameter and it's value upon calling the binParameters function as shown below.
binParameters(scans = 6:14)
Alternatively for and already initialised BinParameters object, the slot containing the parameter of interest can be changed by directly accessing the slot as shown below.
parameters <- binParameters() parameters@scans <- 6:14 parameters
Processing
Processing is simple and requires only the use of the binneRlyse() function.
The input of this function is a vector of the paths of the data files to process, a tibble containing the sample info and BinParameters object.
Below shows the files and info inputs for the example data set.
library(readr) files <- list.files( system.file( 'DataSets/FIE-HRMS/BdistachyonEcotypes', package = 'metaboData'), full.names = TRUE) info <- readr::read_csv(files[grepl('runinfo',files)]) files <- files[!grepl('runinfo',files)] head(files) info
It is crucial that the positions of the sample information in the info file match the sample positions within the files vector. Below shows an example of how this can be checked by matching the file names present in the info with those in the vector.
fileNames <- list.files( system.file( 'DataSets/FIE-HRMS/BdistachyonEcotypes', package = 'metaboData')) fileNames <- fileNames[-grep('runinfo',fileNames)] FALSE %in% (info$fileName == fileNames)
Spectral binning using the default parameters can then be performed with the following.
analysis <- binneRlyse(files,info,binParameters())
analysis <- binneRlyse(files,info,binParameters(nCores = 2))
analysis
Data Extraction
There are a number of functions that can be used to return processing data from a Binalysis object:
info()for sample informationbinnedData()for the spectrally binned matricesaccurateData()for the accurate mass information for each of the 0.01 amu bins
Bin Measures
binneRlyse provides a number of measures that allow the assessment of the quality of a given 0.01 amu bin in terms of the accurate m/z peaks present within its boundaries. These include both purity and centrality.
dat <- binneR::readFiles(system.file( 'DataSets/FIE-HRMS/BdistachyonEcotypes/QC01.mzML', package = 'metaboData'),scans = 6:12,sranges = list(c(70,1000)),dp = 5)$n dat <- tidyr::gather(tibble::as_tibble(dat),'mz','Intensity') dat <- dplyr::mutate(dat,mz = as.numeric(stringr::str_replace(mz,'[:alpha:]',''))) dat <- dplyr::mutate(dat,bin = round(mz,2)) measures <- dplyr::group_by(dat,bin) measures <- dplyr::summarise(measures,purity = binneRlyse:::binPurity(mz,Intensity),centrality = binneRlyse:::binCentrality(mz,Intensity),Intensity = mean(Intensity))
Purity
Bin purity gives a measure of the spread of accurate m/z peaks found within a given bin and can be a signal for the presences of multiple real spectral peaks within a bin. Purity for a given bin is calculated using the equation below.
$$p = 1 - \frac{\sigma}{w} $$
Where p is purity, $\sigma$ is the standard deviation of the accurate m/z present within the bin and w is the width of the bin in amu. A purity closer to 1 indicates that the accurate m/z present within a bin are found over a narrow region and therefore likely only to be as the result of 1 real mass spectral peak. A reduction in purity could indicate the presence of multiple peaks present within a bin.
Below shows example density plots of two negative ionisation mode 0.01 amu bins showing high (133.01) and low (98.96) purity respectively.
Pure <- dplyr::filter(measures,bin == 133.01) Pure <- dplyr::mutate(Pure, purity = paste('Purity = ',round(purity,3), sep = '')) pure <- dplyr::filter(dat,bin == Pure$bin) pure <- tibble::tibble(mz = unlist(apply(pure,1,function(x){rep(x[1],x[2])}))) Impure <- dplyr::filter(measures,bin == 98.96) Impure <- dplyr::mutate(Impure, purity = paste('Purity = ',round(purity,3), sep = '')) impure <- dplyr::filter(dat,bin == Impure$bin) impure <- tibble::tibble(mz = unlist(apply(impure,1,function(x){rep(x[1],x[2])}))) p <- list() p$pure <- ggplot2::ggplot(pure,ggplot2::aes(x = mz)) + ggplot2::geom_density() + ggplot2::theme_bw() + ggplot2::xlim(Pure$bin - 0.005,Pure$bin + 0.005) + ggplot2::ggtitle(paste(Pure$bin,'\t',Pure$purity)) + ggplot2::xlab('m/z') + ggplot2::ylab('Density') p$impure <- ggplot2::ggplot(impure,ggplot2::aes(x = mz)) + ggplot2::geom_density() + ggplot2::theme_bw() + ggplot2::xlim(Impure$bin - 0.005,Impure$bin + 0.005) + ggplot2::ggtitle(paste(Impure$bin,'\t',Impure$purity)) + ggplot2::xlab('m/z') + ggplot2::ylab('Density') gridExtra::grid.arrange(gridExtra::arrangeGrob(p$pure,p$impure))
Bin 133.01, that has a purity very close to 1, has only one peak present. Bin 98.96, that has a reduced purity, clearly has two peaks present.
Centrality
Bin centrality gives a measure of how close the mean of the accurate m/z are to the center of a given bin and can give indication of whether a peak could have been split between the boundary of tow adjacent bins. Centrality is calculated for a given bin using the equation below.
$$ c = 1 - \frac{\sqrt{(\mu - k)^2}}{\frac{1}{2}w}$$
Where c is centrality, $\mu$ is the mean accurate m/z present in the bin, k is the center of the bin and w is the bin width in amu. A centrality close to 1 indicates that the accurate m/z present within the boundaries of the bin are located close to the center of the bin. Low centrality would indicate that the accurate m/z present within the bin are found close to the bin boundary and could therefore indicate bin splitting, were an mass spectral peak is split between two adjacent bins.
Below shows example density plots of two negative ionisation mode 0.01 amu bins showing high (88.04) and low (128.03) centrality respectively.
Pure <- dplyr::filter(measures,bin == 88.04) Pure <- dplyr::mutate(Pure, centrality = paste('Centrality = ',round(centrality,3), sep = '')) pure <- dplyr::filter(dat,bin == Pure$bin) pure <- tibble::tibble(mz = unlist(apply(pure,1,function(x){rep(x[1],x[2])}))) Impure <- dplyr::filter(measures,bin == 128.03) Impure <- dplyr::mutate(Impure, centrality = paste('Centrality = ',round(centrality,3), sep = '')) impure <- dplyr::filter(dat,bin == Impure$bin) impure <- tibble::tibble(mz = unlist(apply(impure,1,function(x){rep(x[1],x[2])}))) p <- list() p$pure <- ggplot2::ggplot(pure,ggplot2::aes(x = mz)) + ggplot2::geom_density() + ggplot2::theme_bw() + ggplot2::xlim(Pure$bin - 0.005,Pure$bin + 0.005) + ggplot2::ggtitle(paste(Pure$bin,'\t',Pure$centrality)) + ggplot2::xlab('m/z') + ggplot2::ylab('Density') p$impure <- ggplot2::ggplot(impure,ggplot2::aes(x = mz)) + ggplot2::geom_density() + ggplot2::theme_bw() + ggplot2::xlim(Impure$bin - 0.005,Impure$bin + 0.005) + ggplot2::ggtitle(paste(Impure$bin,'\t',Impure$centrality)) + ggplot2::xlab('m/z') + ggplot2::ylab('Density') gridExtra::grid.arrange(gridExtra::arrangeGrob(p$pure,p$impure))
Bin 88.04 has a high centrality with single peak that is located very close to the center of the bin. Whereas bin 128.03 as low centrality with a single peak that is located very close to the upper boundary of the bin and has likely been split between this bin and bin 128.04.
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