Package 'hdxmsqc' reference manual

Title:	An R package for quality Control for hydrogen deuterium exchange mass spectrometry experiments
Description:	The hdxmsqc package enables us to analyse and visualise the quality of HDX-MS experiments. Either as a final quality check before downstream analysis and publication or as part of a interative procedure to determine the quality of the data. The package builds on the QFeatures and Spectra packages to integrate with other mass-spectrometry data.
Authors:	Oliver M. Crook [aut, cre]
Maintainer:	Oliver M. Crook <[email protected]>
License:	file LICENSE
Version:	1.3.0
Built:	2024-11-29 08:14:36 UTC
Source:	https://github.com/bioc/hdxmsqc

This is data to be included in my package

Description

A small HDX-MS dataset for BRD4 in apo state and in complex with IBET151

Author(s)

My Name [email protected]

This is data to be included in my package

Description

A complete HDX-MS dataset for BRD4 in apo state and in complex with IBET151

Author(s)

My Name [email protected]

Charge states should have correlated incorperation but they need not be exactly the same

Description

Charge states should have correlated incorperation but they need not be exactly the same

Usage

chargeCorrelationHdx(object, experiment = NULL, timepoints = NULL)
chargeCorrelationHdx(object, experiment = NULL, timepoints = NULL)

Arguments

`object`	An object of class `QFeatures`
`experiment`	A character vector indicating the experimental conditions
`timepoints`	A numeric vector indicating the experimental timepoints

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- chargeCorrelationHdx(object = BRD4df_full_imputed,
experiment = experiment, 
timepoints = timepoints)
data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- chargeCorrelationHdx(object = BRD4df_full_imputed,
experiment = experiment, 
timepoints = timepoints)

Check whether deuterium uptakes are compatible with difference overlapping sequences.

Description

Check whether deuterium uptakes are compatible with difference overlapping sequences.

Usage

compatibleUptake(object, overlap = 5, experiment = NULL, timepoints = NULL)
compatibleUptake(object, overlap = 5, experiment = NULL, timepoints = NULL)

Arguments

`object`	An object of class `QFeatures`
`overlap`	How much overlap is required to check consistentcy. Default is sequences within 5 residues
`experiment`	A character vector indicating the experimental conditions
`timepoints`	A numeric vector indicating the experimental timepoints

Author(s)

Oliver Crook

Examples

data("BRD4df")
result <- compatibleUptake(BRD4df,  experiment = 1, timepoints = 1)
data("BRD4df")
result <- compatibleUptake(BRD4df,  experiment = 1, timepoints = 1)

Empirical versus theoretical mass errors

Description

Empirical versus theoretical mass errors

Usage

computeMassError(object, eCentroid = "Exp.Cent", tCentroid = "Theor.Cent")
computeMassError(object, eCentroid = "Exp.Cent", tCentroid = "Theor.Cent")

Arguments

`object`	An object of class `QFeatures`
`eCentroid`	character string indicating column identifier for experimental centroid
`tCentroid`	character string indicating column identifier for theoretical centroid

Value

The error difference between the empirical and theoretical centroid

Author(s)

Oliver Crook

Examples

data("BRD4df")
result <- computeMassError(BRD4df, "Exp.Cent", "Theor.Cent")
head(result)
data("BRD4df")
result <- computeMassError(BRD4df, "Exp.Cent", "Theor.Cent")
head(result)

Monotonicity based outlier detection.

Description

Monotonicity based outlier detection.

Usage

computeMonotoneStats(object, experiment = NULL, timepoints = NULL)
computeMonotoneStats(object, experiment = NULL, timepoints = NULL)

Arguments

`object`	An object of class `QFeatures`
`experiment`	A character vector indicating the experimental conditions
`timepoints`	A numeric vector indicating the experimental timepoints

Author(s)

Oliver Crook

Examples

data("BRD4df")
result <- computeMonotoneStats(BRD4df, experiment = 1, timepoint = 1)
data("BRD4df")
result <- computeMonotoneStats(BRD4df, experiment = 1, timepoint = 1)

Compute exchangeable amides.

Description

Computes the number of exchangeable amides based on the sequnece

Usage

exchangeableAmides(sequence)
exchangeableAmides(sequence)

Arguments

sequence

The sequence of the peptide

Value

Returns a numeric indicating the number of exchangeable amides

Examples


exchangeableAmides(sequence = "HDAEHAHEAPRKL")
exchangeableAmides(sequence = "HDAEHAHEAPRKL")

fourier transform approach to computing isotopic distribution

Description

fourier transform approach to computing isotopic distribution

Usage

fourierIsotope(
  elements,
  incorp = 0,
  num_exch_sites = 0,
  charge = 1,
  isotopes = NULL
)
fourierIsotope(
  elements,
  incorp = 0,
  num_exch_sites = 0,
  charge = 1,
  isotopes = NULL
)

Arguments

`elements`	A list of elements
`incorp`	The deuterium incoperation
`num_exch_sites`	The number of exchangable amides. Default is 0.
`charge`	The charge state of the peptide
`isotopes`	The number of isotopes to compute. The default is NULL, in whiich a default heuristic is used to make a good guess that covers the expected peaks.

Value

A list of mass and intensity value corresponding to the isotope distribution

Author(s)

Oliver Crook

Examples

fourierIsotope(c(C = 0, H = 2, N = 0, O = 1, S = 0, P = 0))

fourierIsotope(c(C = 0, H = 2, N = 0, O = 1, S = 0, P = 0))

generate Spectra using a fourier transform

Description

generate Spectra using a fourier transform

Usage

generateSpectra(
  sequences,
  incorps,
  charges,
  customs = list(code = NULL, elements = NULL)
)
generateSpectra(
  sequences,
  incorps,
  charges,
  customs = list(code = NULL, elements = NULL)
)

Arguments

`sequences`	A vector of peptide sequences
`incorps`	A vector of deuterium incoperation
`charges`	A vector of charge states of the peptide
`customs`	Custom elements supplied as a list

Value

A Spectra object corresponding to the isotope distributions

Author(s)

Oliver Crook

Examples

generateSpectra(sequence = "HDAEHAHEAPRKL", incorps = c(0.5), charges = 2)

generateSpectra(sequence = "HDAEHAHEAPRKL", incorps = c(0.5), charges = 2)

A package to perfrom quality control for mass-spectrometry based hydrogen deuterium exchange experiment.

Description

'hdxmsqc' provides the functionality to assess the quality and perform quality control of HDX-MS experiments. Raw and processed data can be visualized and analyzed to identify potential issues with the data. The package is designed to work with data from any HDX-MS platform. Typically, users will have exported results from either HDExaminer or DynamX software. There is not need to filter the data in either of those software systems.

Author(s)

Oliver Crook

Ion Mobility time based outlier analysis

Description

Ion Mobility time based outlier analysis

Usage

imTimeOutlier(
  object,
  rightIMS = "rightIMS",
  leftIMS = "leftIMS",
  searchIMS = "Search.IMS"
)
imTimeOutlier(
  object,
  rightIMS = "rightIMS",
  leftIMS = "leftIMS",
  searchIMS = "Search.IMS"
)

Arguments

`object`	An object of class `QFeatures`
`rightIMS`	A string indicating the right boundary of the ion mobility separation time. Defaults is "rightIMS".
`leftIMS`	A string indicating the left boundary of the ion mobility separation time. Default is "leftIMS".
`searchIMS`	A string indicating the actual ion mobility search time. The default is "Search.IMS"

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
imTimeOutlier(object = BRD4df_full_imputed)

data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
imTimeOutlier(object = BRD4df_full_imputed)

Intensity based deviations

Description

Intensity based deviations

Usage

intensityOutliers(object, fcolIntensity = "Max.Inty")
intensityOutliers(object, fcolIntensity = "Max.Inty")

Arguments

`object`	An object of class `QFeatures`
`fcolIntensity`	character to intensity intensity columns. Default is "Max.Inty" and uses regular expressions to find relevant columns

Value

The Cook's distance to characterise outleirs

Author(s)

Oliver Crook

Examples

data("BRD4df_full")

intensityOutliers(BRD4df_full)
data("BRD4df_full")

intensityOutliers(BRD4df_full)

Missing at random versus missing not at random

Description

Missing at random versus missing not at random

Usage

isMissingAtRandom(object, threshold = NULL, filter = TRUE)
isMissingAtRandom(object, threshold = NULL, filter = TRUE)

Arguments

object

An object of class QFeatures

threshold

A threshold indicated how many missing values indicate whether missingness is not at random. Default is NULL, which means leads to a threshold which is half the number of columns.

filter

A logial indicating whether to filter out data that is deemed missing not at random

data("BRD4df_full")

isMissingAtRandom(BRD4df_full)

Value

Adds a missing not at random indicator column

Author(s)

Oliver Crook

fourier transform approach to computing isotopic distribution

Description

fourier transform approach to computing isotopic distribution

Usage

isotopicDistributionHDXfourier(
  sequence,
  incorp = 0,
  charge = 1,
  custom = list(code = NULL, elements = NULL)
)
isotopicDistributionHDXfourier(
  sequence,
  incorp = 0,
  charge = 1,
  custom = list(code = NULL, elements = NULL)
)

Arguments

`sequence`	A peptide
`incorp`	The deuterium incoperation
`charge`	The charge state of the peptide
`custom`	custom amino acids can be provided here provide a list of the elements.

Value

A list of mass and intensity value corresponding to the isotope distribution

Author(s)

Oliver Crook

Examples

isotopicDistributionHDXfourier(sequence = "HDAEHAHEAPRKL")
isotopicDistributionHDXfourier(sequence = "HDAEHAHEAPRKL")

Ion Mobility time based outlier analysis

Description

Ion Mobility time based outlier analysis

Usage

plotImTimeOutlier(
  object,
  rightIMS = "rightIMS",
  leftIMS = "leftIMS",
  searchIMS = "Search.IMS"
)
plotImTimeOutlier(
  object,
  rightIMS = "rightIMS",
  leftIMS = "leftIMS",
  searchIMS = "Search.IMS"
)

Arguments

`object`	An object of class `QFeatures`
`rightIMS`	A string indicating the right boundary of the ion mobility separation time. Defaults is "rightIMS".
`leftIMS`	A string indicating the left boundary of the ion mobility separation time. Default is "leftIMS".
`searchIMS`	A string indicating the actual ion mobility search time. The default is "Search.IMS"

Author(s)

Oliver Crook

Examples

library(RColorBrewer)
data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
plotImTimeOutlier(object = BRD4df_full_imputed)
library(RColorBrewer)
data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
plotImTimeOutlier(object = BRD4df_full_imputed)

Intensity based deviation plot

Description

Intensity based deviation plot

Usage

plotIntensityOutliers(object, fcolIntensity = "Max.Inty")
plotIntensityOutliers(object, fcolIntensity = "Max.Inty")

Arguments

`object`	An object of class `QFeatures`
`fcolIntensity`	character to intensity intensity columns. Default is "Max.Inty" and uses regular expressions to find relevant columns

Value

A ggplot2 object showing intensity based outliers

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
library(RColorBrewer)

plotIntensityOutliers(BRD4df_full)
data("BRD4df_full")
library(RColorBrewer)

plotIntensityOutliers(BRD4df_full)

Mass error plot

Description

Mass error plot

Usage

plotMassError(object, eCentroid = "Exp.Cent", tCentroid = "Theor.Cent")
plotMassError(object, eCentroid = "Exp.Cent", tCentroid = "Theor.Cent")

Arguments

`object`	An object of class `QFeatures`
`eCentroid`	character string indicating column identifier for experimental centroid
`tCentroid`	character string indicating column identifier for theoretical centroid

Value

a ggplot2 object which can be used to visualise the

Author(s)

Oliver Crook

Examples

library(RColorBrewer)
data("BRD4df")
result <- plotMassError(BRD4df, "Exp.Cent", "Theor.Cent")
library(RColorBrewer)
data("BRD4df")
result <- plotMassError(BRD4df, "Exp.Cent", "Theor.Cent")

missing value plot

Description

missing value plot

Usage

plotMissing(object, ...)
plotMissing(object, ...)

Arguments

`object`	An object of class `QFeatures`
`...`	Additional arguemnts to pheatmap

Value

a pheatmap showing missing values

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
library(pheatmap)
library(RColorBrewer)

plotMissing(BRD4df_full)


data("BRD4df_full")
library(pheatmap)
library(RColorBrewer)

plotMissing(BRD4df_full)

Monotonicity based outlier detection, plot.

Description

Monotonicity based outlier detection, plot.

Usage

plotMonotoneStat(object, experiment = NULL, timepoints = NULL)
plotMonotoneStat(object, experiment = NULL, timepoints = NULL)

Arguments

`object`	An object of class `QFeatures`
`experiment`	A character vector indicating the experimental conditions
`timepoints`	A numeric vector indicating the experimental timepoints

Author(s)

Oliver Crook

Examples

library("RColorBrewer")
data("BRD4df_full")
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- computeMonotoneStats(object = BRD4df_full,
experiment = experiment, 
timepoints = timepoints)
library("RColorBrewer")
data("BRD4df_full")
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- computeMonotoneStats(object = BRD4df_full,
experiment = experiment, 
timepoints = timepoints)

Retention time based analysis

Description

Retention time based analysis

Usage

plotrTimeOutliers(
  object,
  leftRT = "leftRT",
  rightRT = "rightRT",
  searchRT = "Search.RT"
)
plotrTimeOutliers(
  object,
  leftRT = "leftRT",
  rightRT = "rightRT",
  searchRT = "Search.RT"
)

Arguments

`object`	An object of class `QFeatures`
`leftRT`	A character indicated pattern associated with left boundary of retention time search. Default is "leftRT".
`rightRT`	A character indicated pattern associated with right boundary of retneton time search. Default is "rightRT".
`searchRT`	The actual search retention time pattern. Default is "Search.RT"

Value

a ggplot2 object showing distribution of retention time windows.

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
library(RColorBrewer)

plotrTimeOutliers(BRD4df_full)
data("BRD4df_full")
library(RColorBrewer)

plotrTimeOutliers(BRD4df_full)

Function to curate and HDExaminer file so that in contains all the information in a sensible format. This object can then be straightforwardly passed to a object of class `QFeatures`

Description

Function to curate and HDExaminer file so that in contains all the information in a sensible format. This object can then be straightforwardly passed to a object of class QFeatures

Usage

processHDE(HDExaminerFile, proteinStates = NULL)
processHDE(HDExaminerFile, proteinStates = NULL)

Arguments

`HDExaminerFile`	an object of class data.frame containing an HDExaminer data
`proteinStates`	a character vector indicating the protein states

Value

A wide format data frame with HDExaminer data

Author(s)

Oliver Crook

Examples

sample_data <- data.frame(read.csv(system.file("extdata", "ELN55049_AllResultsTables_Uncurated.csv", package = "hdxmsqc", mustWork = TRUE), nrows = 10))

processHDE(sample_data) 

sample_data <- data.frame(read.csv(system.file("extdata", "ELN55049_AllResultsTables_Uncurated.csv", package = "hdxmsqc", mustWork = TRUE), nrows = 10))

processHDE(sample_data)

Quality Control table function. Generate a table that collates quality control metrics

Description

Quality Control table function. Generate a table that collates quality control metrics

Usage

qualityControl(
  object,
  massError = NULL,
  intensityOutlier = NULL,
  retentionOutlier = NULL,
  monotonicityStat = NULL,
  mobilityOutlier = NULL,
  chargeCorrelation = NULL,
  replicateCorrelation = NULL,
  replicateOutlier = NULL,
  sequenceCheck = NULL,
  spectraCheck = NULL,
  experiment = NULL,
  timepoints = NULL,
  undeuterated = FALSE
)
qualityControl(
  object,
  massError = NULL,
  intensityOutlier = NULL,
  retentionOutlier = NULL,
  monotonicityStat = NULL,
  mobilityOutlier = NULL,
  chargeCorrelation = NULL,
  replicateCorrelation = NULL,
  replicateOutlier = NULL,
  sequenceCheck = NULL,
  spectraCheck = NULL,
  experiment = NULL,
  timepoints = NULL,
  undeuterated = FALSE
)

Arguments

`object`	An object of class Qfeatures, with the data used for the analysis
`massError`	The output of the `computeMassError` function
`intensityOutlier`	The output of the `intensityOutliers` function
`retentionOutlier`	The output of the `rTimeOutliers` function
`monotonicityStat`	The output of the `computeMonotoneStats` function
`mobilityOutlier`	The output of the `imTimeOutliers` function
`chargeCorrelation`	The output of the `chargeCorrelationsHdx` function
`replicateCorrelation`	The output of the `replicateCorrelation` function
`replicateOutlier`	The output of the `replicateOutlier` function
`sequenceCheck`	The output of the `compatibleUptake` function
`spectraCheck`	The output of the `spectraSimiarity` function
`experiment`	The experimental conditions.
`timepoints`	The timepoints used in the analysis, must include repeat for replicates
`undeuterated`	A logical indicating whether only the undeuterated data should be exported

Value

An object of class DataFrame containing a summary of the quality control results.

Author(s)

Oliver Crook

Correlation based checks

Description

Correlation based checks

Usage

replicateCorrelation(object, experiment, timepoints)
replicateCorrelation(object, experiment, timepoints)

Arguments

`object`	An object of class QFeatures.
`experiment`	A character vector indicating the experimental conditions
`timepoints`	A numeric vector indicating the experimental timepoints

Value

Returns A list of the same length as the number of experiments indicating outlier from correlation analysis. Outliers are flagged if their deuterium uptake is highly variable.

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- replicateCorrelation(object = BRD4df_full,
experiment = experiment, 
timepoints = timepoints)

data("BRD4df_full")
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- replicateCorrelation(object = BRD4df_full,
experiment = experiment, 
timepoints = timepoints)

Correlation based checks

Description

Correlation based checks

Usage

replicateOutlier(object, experiment, timepoints)
replicateOutlier(object, experiment, timepoints)

Arguments

`object`	An object of class QFeatures.
`experiment`	A character vector indicating the experimental conditions
`timepoints`	A numeric vector indicating the experimental timepoints

Value

Returns A list of the same length as the number of experiments indicating outlier from correlation analysis. Outliers are flagged if their deuterium uptake is highly variable.

Author(s)

Oliver Crook

Examples

data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- replicateOutlier(object = BRD4df_full_imputed,
experiment = experiment, 
timepoints = timepoints)
data("BRD4df_full")
BRD4df_filtered <- isMissingAtRandom(object = BRD4df_full)
BRD4df_full_imputed <- impute(BRD4df_filtered, method = "zero", i = 1)
experiment <- c("wt", "iBET")
timepoints <- rep(c(0, 15, 60, 600, 3600, 14000), each = 3)
monoStat <- replicateOutlier(object = BRD4df_full_imputed,
experiment = experiment, 
timepoints = timepoints)

Retention time based analysis

Description

Retention time based analysis

Usage

rTimeOutliers(
  object,
  leftRT = "leftRT",
  rightRT = "rightRT",
  searchRT = "Search.RT"
)
rTimeOutliers(
  object,
  leftRT = "leftRT",
  rightRT = "rightRT",
  searchRT = "Search.RT"
)

Arguments

`object`	An object of class `QFeatures`
`leftRT`	A character indicated pattern associated with left boundary of retention time search. Default is "leftRT".
`rightRT`	A character indicated pattern associated with right boundary of retneton time search. Default is "rightRT".
`searchRT`	The actual search retention time pattern. Default is "Search.RT"

Value

A list indicating the retention time based outliers.

Author(s)

Oliver Crook

Examples

data("BRD4df_full")

rTimeOutliers(BRD4df_full)
data("BRD4df_full")

rTimeOutliers(BRD4df_full)

Spectral checking using data from HDsite

Description

Spectral checking using data from HDsite

Usage

spectraSimilarity(
  peaks,
  object,
  experiment = NULL,
  mzCol = 14,
  startRT = "Start.RT",
  endRT = "End.RT",
  charge = "z",
  incorpD = "X.D.left",
  maxD = "maxD",
  numSpectra = NULL,
  ppm = 300,
  BPPARAM = bpparam()
)
spectraSimilarity(
  peaks,
  object,
  experiment = NULL,
  mzCol = 14,
  startRT = "Start.RT",
  endRT = "End.RT",
  charge = "z",
  incorpD = "X.D.left",
  maxD = "maxD",
  numSpectra = NULL,
  ppm = 300,
  BPPARAM = bpparam()
)

Arguments

`peaks`	a data.frame containing data exported from hdsite
`object`	a data.frame obtained from HDexaminer data
`experiment`	A character vector indicating the experimental conditions
`mzCol`	The column in the peak information indicating the base mz value
`startRT`	The column indicatng the start of the retention time. Default is "Start.RT"
`endRT`	The column indicating the end of the retention time. Default is "End.RT
`charge`	The column indicating the charge information. Default is "z".
`incorpD`	The deuterium uptake value column. Default is "X.D.left".
`maxD`	The maximum allowed deuterium incorporation column. Default is "maxD".
`numSpectra`	The number of spectra to analyse. Default is NULL in which all Spectra are analysed.
`ppm`	The ppm error
`BPPARAM`	Bioconductor parallel options.

Value

Two list of spectra observed and matching theoretical Spectra

Author(s)

Oliver Crook

Package 'hdxmsqc'

Help Index

This is data to be included in my package

Description

Author(s)

This is data to be included in my package

Description

Author(s)

Charge states should have correlated incorperation but they need not be exactly the same

Description

Usage

Arguments

Author(s)

Examples

Check whether deuterium uptakes are compatible with difference overlapping sequences.

Description

Usage

Arguments

Author(s)

Examples

Empirical versus theoretical mass errors

Description

Usage

Arguments

Value

Author(s)

Examples

Monotonicity based outlier detection.

Description

Usage

Arguments

Author(s)

Examples

Compute exchangeable amides.

Description

Usage

Arguments

Value

Examples

fourier transform approach to computing isotopic distribution

Description

Usage

Arguments

Value

Author(s)

Examples

generate Spectra using a fourier transform

Description

Usage

Arguments

Value

Author(s)

Examples

A package to perfrom quality control for mass-spectrometry based hydrogen deuterium exchange experiment.

Description

Author(s)

Ion Mobility time based outlier analysis

Description

Usage

Arguments

Author(s)

Examples

Intensity based deviations

Description

Usage

Arguments

Value

Author(s)

Examples

Missing at random versus missing not at random

Description

Usage

Arguments

Value

Author(s)

fourier transform approach to computing isotopic distribution

Description

Usage

Arguments

Value

Function to curate and HDExaminer file so that in contains all the information in a sensible format. This object can then be straightforwardly passed to a object of class `QFeatures`