Class "MetaboSet"

Description

The MetaboSet class is a single generic class valid for all sorts of metabolomic studies regardless of the experimental platform, the statistical processing and the annotation stage. It is the core operation class of eRah.

Details

MetaboSet

Slots

Info

Slot Info stores the general information of the experiment and the experimental platform used in the analysis of the biological samples.

Data

Slot Data contains either the raw data or the path of the files. It also contains the list of the selected features (deconvolved compounds). In the subslot Parameters it is saved the information regarding the feature selector algorithm (type, parameters, version...) and the experimental platform used.

MetaData

Slot MetaData has two slots. In the Instrumental slot it is saved a data frame with some mandatory fields (filename, date, time, sampleID) and optional fields related to the experimental platform (Column ID, Column Type, Ioniser,...). Slot Phenotypic contains a data frame with the sample and experimental information (phenotypes, longitudinal data,...).

Results

In the Results slot it is saved the information related to the statistical and identification results. The slot Parameters contains all the values of the parameters used in the identification and statistical functions. Slot Identification has the results of the identification process as well as the identification or/and annotation steps. The results of the statistical functions are saved in the Statistics slot.

Author(s)

Xavier Domingo-Almenara, Arnald Alonso and Francesc Fernandez-Albert.

Class "RawDataParameters"

Description

The RawDataParameters class contains the slots for storing and accessing into a MS sample, and the essential parameters for performing its processing (deconvolution).

Slots

data

The data matrix of the sample to be processed

min.mz

The minimum adquired mz number

max.mz

The maximum adquired mz number

start.time

Starting time of adquisition

mz.resolution

Mz resolution

scans.per.second

Scans per second

avoid.processing.mz

Which mz do not have to be processed

min.peak.width

Minimum peak width (stored in scans)

min.peak.height

Minimum peak height

noise.threshold

The noise threshold

compression.coef

Compression coefficient (parameter for Orthogonal Signal Deconvolution)

Author(s)

Xavier Domingo-Almenara.

Alignment of compounds

Description

Alignment of GC-MS deconvolved compounds

Usage

alignComp(Experiment, alParameters, blocks.size=NULL)

## S4 method for signature 'MetaboSet'
alignComp(Experiment, alParameters, blocks.size = NULL)

Arguments

Details

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

For experiments containing more than 100 (Windows) or 1000 (Mac or Linux) samples (numbers depending on the computer resoures and sample type). In those cases alignment can be conducted by block segmentation. For an experiment of e.g. 1000 samples, the block.size can be set to 100, so the alignment will perform as multiple (ten) 100-samples experiments, to later align them into a single experiment.

This parameter is designed to solve the typical problem that appear when aligning under Windows operating system: "Error: cannot allocate vector of size XX Gb". Such a problem will not appear with Mac or Linux, but several hours of computation are expected when aligning a large number of samples. Using block segmentation provides a greatly improved run-time performance.

Value

The function returns an updated S4 'MetaboSet' class, where the GC-MS samples have been now aligned.

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

newExp setDecPar deconvolveComp

Alignment list

Description

The list of aligned metabolites and their relative quantification for each sample in a given experiment

Usage

alignList(object, by.area = TRUE)

## S4 method for signature 'MetaboSet'
alignList(object, by.area = TRUE)

Arguments

Details

Returns an alignment table containing the list of aligned metabolites and their relative quantification for each sample in a given experiment.

Value

alignList returns a data frame object:

See Also

idList dataList

Information of a Compound

Description

Displays basic information of a compound in the MS library.

Usage

compInfo(comp.id, id.database = mslib)

Arguments

Details

Returns details on a given compound such as the synonyms, CAS, KEGG, retention index, among others.

See Also

findComp

Examples

# finding proline
findComp("proline")

# we see that proline 2TMS has the DB.Id number 42, then:
compInfo(42)

computeRIerror

Description

This function uses RI of mslib database and RT of the identified compounds to discrimine proper compound identification.

Usage

computeRIerror(
  Experiment,
  id.database = mslib,
  reference.list,
  ri.error.type = c("relative", "absolute"),
  plot.results = TRUE
)

Arguments

Details

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

showRTRICurve

Examples

## Not run: 
ex <- computeRIerror(
  ex, 
  mslib, 
  reference.list=list(AlignID = c(45,67,92,120)), 
  ri.error.type = "relative"
)

## End(Not run)

Create Instrumental Table

Description

Create table containing instrumental information such as sample IDs and file names.

Usage

createInstrumentalTable(files)

Arguments

Details

Creates instrumental information table based on experiment sample file paths. Columns containing further information can also be added to this.

See Also

newExp createPhenoTable

Examples

## Not run: 
library(gcspikelite)

files <- list.files(system.file('data',package = 'gcspikelite'),full.names = TRUE)
files <- files[sapply(files,grepl,pattern = 'CDF')]

instrumental <- createInstrumentalTable(files)

## End(Not run)

Create Phenotype Table

Description

Create table containing sample meta information such as as sample ID and class.

Usage

createPhenoTable(files, cls)

Arguments

Details

Creates phenotype information table based on experiment sample file paths and sample classes. Columns containing further information can also be added to this.

See Also

newExp createInstrumentalTable

Examples

## Not run: 
library(gcspikelite)
data(targets)

files <- list.files(system.file('data',package = 'gcspikelite'),full.names = TRUE)
files <- files[sapply(files,grepl,pattern = 'CDF')]

phenotype <- createPhenoTable(files,as.character(targets$Group[order(targets$FileName)]))

## End(Not run)

Creating Experiment Tables

Description

eRah requires an instrumental and (optionally) phenotype .csv file for starting/creating a new eRah project/experiment. This function automatically creates the Phenoytpe and Instrumental data .csv files.

Usage

createdt(path)

Arguments

Details

The experiment has to been organized as follows: all the samples related to each class have to be stored in the same folder (one folder = one class), and all the class-folders in one folder, which is the experiment folder.

Two things have to be considered at this step: .csv files are different when created by American and European computers, so errors may raise due to that fact. Also, the folder containing the samples, must contain only folders. If the folder contains files (for example, already created .csv files), eRah will prompt an error.

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

See Also

newExp

Examples

## Not run: 
# Store all the raw data files in one different folder per class,
# and all the class-folders in one folder, which is the experiment
# folder. Then execute

createdt(path)

# where path is the experiment folder path.
# The experiment can be now startd by:

ex <- newExp(instrumental="path/DEMO_inst.csv", 
phenotype="path/DEMO_pheno.csv", info="DEMO Experiment")

## End(Not run)

Data list

Description

The final eRah list of aligned and identified metabolites and their relative quantification for each sample in a given experiment

Usage

dataList(Experiment, id.database = mslib, by.area = TRUE)

## S4 method for signature 'MetaboSet'
dataList(Experiment, id.database = mslib, by.area = TRUE)

Arguments

Details

Returns an identification and alignment table containing the list of aligned and identifed metabolites (names) and their relative quantification for each sample in a given experiment.

Value

alignList returns an S3 object:

See Also

idList alignList

Deconvolution of compounds in samples

Description

Deconvolution of GC-MS data

Usage

deconvolveComp(
  Experiment,
  decParameters,
  samples.to.process = NULL,
  down.sample = FALSE,
  virtualScansPerSecond = NULL
)

## S4 method for signature 'MetaboSet'
deconvolveComp(
  Experiment,
  decParameters,
  samples.to.process = NULL,
  down.sample = FALSE,
  virtualScansPerSecond = NULL
)

Arguments

Details

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

eRah uses multivariate methods which run-time performance depend on the amount of data to be analyzed. When peaks are wider and the #' scans per second is also a small value, the number of points (scans) that define a peak might be too many, leading eRah to a poor run#'-time performance. To solve that, use down.sample=TRUE to allow eRah to define a peak with 10 seconds, and analyze the data more #' efficiently.

Value

The function returns an updated S4 'MetaboSet' class, where the GC-MS samples have been now deconvolved.

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

newExp setAlPar

Examples

## Not run: 
# Deconvolve data from a created experiment by \code{\link{newExp}}.
# ex <- newExp(instrumental="path")

# The following will set eRah for analyzing the chromatograms
# from minutes 5 to 15, and withouth taking into account the masses
# 35:69,73:75,147:149, with a minimum peak width of 0.7 seconds.

ex.dec.par <- setDecPar(min.peak.width=0.7, min.peak.height=5000, 
                       noise.threshold=500, avoid.processing.mz=c(35:69,73:75,147:149), 
                       analysis.time=c(5,15))

# An now deconvolve the compounds in the samples:
# ex <- deconvolveComp(ex, decParameters=ex.dec.par)

## End(Not run)

Class "eRah_DB"

Description

The eRah_DB class contains the slots for storing and accessing a MS library.

Slots

name

The name of the stored library

version

The version of the stored library (and which is the database identifier, should be unique and used to check if is the database used in other experiments)

info

Character vector containing complementary information about the library.

database

A list of S3 objects, which each object contains the information on a different compound.

Author(s)

Xavier Domingo-Almenara.

expClasses-method

Description

The classes of a given experiment.

Usage

expClasses(object)

## S4 method for signature 'MetaboSet'
expClasses(object)

Arguments

Details

Returns the classes details of the experiment.

See Also

metaData phenoData

Export spectra to CEF

Description

Export spectra to CEF format for comparison with the NIST library through MassHunter interface.

Usage

export2CEF(Experiment, export.id = NULL, 
id.database = mslib, store.path = getwd())

Arguments

Export spectra to MSP

Description

Export spectra to MSP format for comparison with the NIST library.

Usage

export2MSP(
  Experiment,
  export.id = NULL,
  id.database = mslib,
  store.path = getwd(),
  alg.version = 1
)

Arguments

Find a compound

Description

Finds compounds in the MS library by Name, CAS or chemical formula.

Usage

findComp(name = NULL, id.database = mslib, CAS = NULL, chem.form = NULL)

Arguments

Value

findComp returns an S3 object:

See Also

compInfo

Examples

# finding proline

findComp("proline")

# be careful, exact matches are not supported, 
# as well as different names like these cases:

findComp("L-proline (2TMS)")


findComp("proline 2")

Identification list

Description

The list of identified metabolites in a given experiment

Usage

idList(object, id.database = mslib)

## S4 method for signature 'MetaboSet'
idList(object, id.database = mslib)

Arguments

Details

Returns an identification table containing the names, match scores, and other variables for a given experiment.

Value

idList returns an S3 object:

See Also

alignList dataList

Identification of compounds

Description

Identification of compounds. Each empirical spectrum is compared against a ms library.

Usage

identifyComp(Experiment, id.database = mslib,mz.range = NULL, n.putative = 3)

## S4 method for signature 'MetaboSet'
identifyComp(Experiment, id.database = mslib, mz.range = NULL, n.putative = 3)

Arguments

Value

The function returns an updated S4 'MetaboSet' class, where the GC-MS samples have been now aligned.

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

[2] MassBank: A public repository for sharing mass spectral data for life sciences, H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda, K. Suwa. Y. Ojima, K. Tanaka, S. Tanaka, K. Aoshima, Y. Oda, Y. Kakazu, M. Kusano, T. Tohge, F. Matsuda, Y. Sawada, M. Yokota Hirai, H. Nakanishi, K. Ikeda, N. Akimoto, T. Maoka, H. Takahashi, T. Ara, N. Sakurai, H. Suzuki, D. Shibata, S. Neumann, T. Iida, K. Tanaka, K. Funatsu, F. Matsuura, T. Soga, R. Taguchi, K. Saito and T. Nishioka, J. Mass Spectrom., 45 (2010) 703-714.

See Also

newExp alignComp setAlPar setDecPar

Import MSP files from GMD to R

Description

Import the Golm Metabolome Database.

Usage

importGMD(filename, DB.name, DB.version, DB.info, 
type = c("VAR5.ALK","VAR5.FAME","MDN35.ALK", "MDN35.FAME"))

Arguments

Details

For more details, please see the eRah manual

Import MSP files to R

Description

Import MS libraries in MSP format to eRah DB format.

Usage

importMSP(filename, DB.name, DB.version, DB.info)

Arguments

Details

The MSP input file should look like:

—–

Name: Metabolite_name

Formula: H2O

MW: 666

ExactMass: 666.266106

CAS#: 11-22-3

DB#: 1

Comments: Metabolite_name reference standard

Num Peaks: XX

53 1; 54 2; 55 5; 56 2; 57 2;

58 14; 59 18; 60 1000; 61 2; 67 1;

Name: Metabolite_name_2

Formula: H2O2

MW: 999

ExactMass: 999.266106

CAS#: 22-33-4

DB#: 2

Comments: Metabolite_name_"" reference standard

Num Peaks: XX

66 10; 67 1000; 155 560; 156 800; 157 2;

158 14; 159 1; 160 100; 161 2; 167 1;

——-

OR

—–

Name: Metabolite_name

Formula: H2O

MW: 666

ExactMass: 666.266106

CASNO: 11-22-3

DB#: 1

Comment: Metabolite_name reference standard

Num peaks: XX

53 1

54 2

55 5

Name: Metabolite_name_2

Formula: H2O2

MW: 999

ExactMass: 999.266106

CASNO: 22-33-4

DB#: 2

Comment: Metabolite_name_"" reference standard

Num Peaks: XX

66 10

67 1000

155 560

——-

Or combinations of both.

For more details, please see the eRah manual.

metaData-method

Description

Displays the Experiment metadata

Usage

metaData(object)

## S4 method for signature 'MetaboSet'
metaData(object)

Arguments

See Also

phenoData

MassBank Spectral Library

Description

The default mass spectral library of eRah, which is the MassBank repository.

Usage

data(mslib)

Format

An object of class eRah_DB of length 1.

Details

This is the eRah default MS library, and automatically loaded with the eRah package. It contains almost 500 MS spectra. For details, see reference below.

Author(s)

The TOF-MS spectra were contributted by Kazusa DNA Research Institute, the Engineering Department of Osaka University and Plant Science Center of RIKEN.

MassBank (http://www.massbank.jp/)

References

[1] MassBank: A public repository for sharing mass spectral data for life sciences, H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda, K. Suwa. Y. Ojima, K. Tanaka, S. Tanaka, K. Aoshima, Y. Oda, Y. Kakazu, M. Kusano, T. Tohge, F. Matsuda, Y. Sawada, M. Yokota Hirai, H. Nakanishi, K. Ikeda, N. Akimoto, T. Maoka, H. Takahashi, T. Ara, N. Sakurai, H. Suzuki, D. Shibata, S. Neumann, T. Iida, K. Tanaka, K. Funatsu, F. Matsuura, T. Soga, R. Taguchi, K. Saito and T. Nishioka, J. Mass Spectrom., 45, 703-714 (2010)

See Also

compInfo

New Experiment

Description

Sets a new experiment for eRah

Usage

newExp(instrumental, phenotype = NULL, info = character())

Arguments

Details

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

Value

newExp returns an S4 object of the class 'MetaboSet'.

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

createInstrumentalTable createPhenoTable setDecPar setAlPar

Examples

## Not run: 
library(gcspikelite)
data(targets)

files <- list.files(system.file('data',package = 'gcspikelite'),full.names = TRUE)
files <- files[sapply(files,grepl,pattern = 'CDF')]

instrumental <- createInstrumentalTable(files)
phenotype <- createPhenoTable(files,as.character(targets$Group[order(targets$FileName)]))

ex <- newExp(instrumental = instrumental, 
phenotype = phenotype, info = "DEMO Experiment")

## End(Not run)

phenoData-method

Description

Displays the Experiment phenotypic data (if included).

Usage

phenoData(object)

## S4 method for signature 'MetaboSet'
phenoData(object)

Arguments

See Also

metaData

Plotting chromatographic profile with and without alignment

Description

Plots the chromatophic profiles of the compounds found by eRah. Similarly to plotProfile, but with two sub-windows, showing the chromatophic profiles before and after alignment.

Usage

plotAlign(Experiment,AlignId, per.class = T, xlim = NULL)

## S4 method for signature 'MetaboSet'
plotAlign(Experiment, AlignId, per.class = T, xlim = NULL)

Arguments

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

See Also

plotSpectra plotProfile

Plotting sample chromatogram

Description

Plot the sample chromatogram

Usage

plotChr(
  Experiment,
  N.sample = 1,
  type = c("BIC", "TIC", "EIC"),
  xlim = NULL,
  mz = NULL
)

## S4 method for signature 'MetaboSet'
plotChr(
  Experiment,
  N.sample = 1,
  type = c("BIC", "TIC", "EIC"),
  xlim = NULL,
  mz = NULL
)

Arguments

See Also

sampleInfo

Examples

## Not run: 
plotChr(Experiment, 1, "BIC")

# Plots from minute 5 to 7.
plotChr(Experiment, 1, "TIC", xlim=c(5,7))  

# Plots from minute 5 to 7, and only the masses from 50 to 70.
plotChr(Experiment, 1, "EIC", mz=50:70 xlim=c(5,7))  

# Plots the EIC from minute 7 to 7.5, and only the masses 50, 54 and 70.
plotChr(Experiment, 1, "EIC", xlim=c(7,7.5), mz=c(50,54,70))

## End(Not run)

Plotting chromatographic profile

Description

Plots the chromatophic profiles of the compounds found by eRah.

Usage

plotProfile(Experiment,AlignId, per.class = T, xlim = NULL, cols=NULL)

## S4 method for signature 'MetaboSet'
plotProfile(Experiment, AlignId, per.class = T, xlim = NULL, cols = NULL)

Arguments

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

See Also

plotSpectra plotAlign

Plotting Spectra

Description

Plots the empirical spectra found by eRah, and allows comparing it with the reference spectra.

Usage

plotSpectra(Experiment, AlignId, n.putative = 1,
compare = T, id.database = mslib, comp.db = NULL, 
return.spectra = F, draw.color = "purple", xlim = NULL)

## S4 method for signature 'MetaboSet'
plotSpectra(
  Experiment,
  AlignId,
  n.putative = 1,
  compare = T,
  id.database = mslib,
  comp.db = NULL,
  return.spectra = F,
  draw.color = "purple",
  xlim = NULL
)

Arguments

Details

When identification is applied (see identifyComp), the number of hits to be returned (n.putative) has to be selected. Therefore, here you can compare the empirical spectrum (found by eRah) with each n.putative hit returned (1, 2, ...) by (see identifyComp).

Value

plotSpectra returns an vector when return.spectra=TRUE.

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] eRah: an R package for spectral deconvolution, alignment, and metabolite identification in GC/MS-based untargeted metabolomics. Xavier Domingo-Almenara, Alexandre Perera, Maria Vinaixa, Sara Samino, Xavier Correig, Jesus Brezmes, Oscar Yanes. (2016) Article in Press.

[2] MassBank: A public repository for sharing mass spectral data for life sciences, H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda, K. Suwa. Y. Ojima, K. Tanaka, S. Tanaka, K. Aoshima, Y. Oda, Y. Kakazu, M. Kusano, T. Tohge, F. Matsuda, Y. Sawada, M. Yokota Hirai, H. Nakanishi, K. Ikeda, N. Akimoto, T. Maoka, H. Takahashi, T. Ara, N. Sakurai, H. Suzuki, D. Shibata, S. Neumann, T. Iida, K. Tanaka, K. Funatsu, F. Matsuura, T. Soga, R. Taguchi, K. Saito and T. Nishioka, J. Mass Spectrom., 45, 703-714 (2010)

See Also

plotProfile plotAlign

Missing compound recovery

Description

Missing compounds recovery: fits a general model (all the compounds above a certain minimum number of samples) to all the samples.

Usage

recMissComp(Experiment, min.samples, free.model = F)

## S4 method for signature 'MetaboSet'
recMissComp(Experiment, min.samples, free.model = F)

Arguments

Details

WARNING: If compounds were previously identified, they have to be identified again after applying the "recMissComp" function. This means that "identifyComp" function has to be executed always after "recMissComp" for identification of compounds, even if "identifyComp" has been previously applied before.

The free.model parameter is recomended to be always FALSE (except for carbon tracking applications). This is because the spectra of the samples where the compound is missing is usually affected by noise, and this could decrease the matching score for a certain compound.

Value

The function returns an updated S4 'MetaboSet' class, where the GC-MS samples have been now aligned.

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Domingo-Almenara X, et al. Compound deconvolution in GC-MS-based metabolomics by blind source separation. Journal of Chromatography A (2015). Vol. 1409: 226-233. DOI: 10.1016/j.chroma.2015.07.044

See Also

newExp alignComp setAlPar setDecPar

Information of the samples

Description

Returns basic information on the samples.

Usage

sampleInfo(Experiment, N.sample = 1)

## S4 method for signature 'MetaboSet'
sampleInfo(Experiment, N.sample = 1)

Arguments

Details

Returns details on a given sample of the experiment, such as name, start time, end time, minium and maximum adquired m/z and scans per second.

See Also

plotChr

Set Alignment Parameters

Description

Setting alignment parameters for eRah.

Usage

setAlPar(min.spectra.cor, max.time.dist,mz.range = c(70:600))

Arguments

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

newExp setDecPar alignComp

Examples

## Not run: 
# The following will set eRah for aligning compounds which are
# at least 90 (per cent) similar, and which peaks are at a 
# maximum distance of 2 seconds. All the masses are considered when
# computing the spectral similarity.

ex.al.par <- setAlPar(min.spectra.cor=0.90, max.time.dist=2,
mz.range=1:600)

## End(Not run)

Set Software Parameters

Description

Sets Software Parameters for eRah.

Usage

setDecPar(
  min.peak.width,
  min.peak.height = 2500,
  noise.threshold = 500,
  avoid.processing.mz = c(73:75, 147:149),
  compression.coef = 2,
  analysis.time = 0
)

Arguments

Details

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

newExp deconvolveComp alignComp setAlPar

Examples

## Not run: 
# The following will set eRah for analyzing the chromatograms
#from minutes 5 to 15, and withouth taking into account the masses
#35:69,73:75,147:149, widht a minimum peak widht of 0.7 seconds.
ex.dec.par <- setDecPar(min.peak.width = 0.7, 
                        min.peak.height = 5000, 
                        noise.threshold = 500, 
                        avoid.processing.mz = c(35:69,73:75,147:149), 
                        analysis.time = c(5,15))

## End(Not run)

Show MetaboSet object

Description

Show MetaboSet object

Usage

## S4 method for signature 'MetaboSet'
show(object)

Arguments

Details

show-MetaboSet

Show RT-RI curve

Description

This function uses RI of mslib database and RT of the identified compounds to discrimine proper compound identification.

Usage

showRTRICurve(
  Experiment,
  reference.list,
  nAnchors = 4,
  ri.thrs = "1R",
  id.database = mslib
)

Arguments

Details

See eRah vignette for more details. To open the vignette, execute the following code in R: vignette("eRahManual", package="erah")

Author(s)

Xavier Domingo-Almenara. xavier.domingo@urv.cat

References

[1] Xavier Domingo-Almenara, et al., eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC-MS-Based Metabolomics. Analytical Chemistry (2016). DOI: 10.1021/acs.analchem.6b02927

See Also

computeRIerror

Examples

## Not run: 
# The following set erah to determine which indetified compounds are in RI treshold
RTRICurve <- showRTRICurve(ex, list, nAnchors=4, ri.thrs='1R')

## End(Not run)