Type:	Package
Title:	MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis
Version:	1.1.0-2
Date:	2024-01-23
Maintainer:	Javier Palarea-Albaladejo <javier.palarea@udg.edu>
Depends:	R (≥ 3.2.5), MALDIquant, signal, robustbase, lattice, waveslim
ByteCompile:	yes
Description:	Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) <doi:10.1093/bioinformatics/btx628>).
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Repository:	CRAN
Suggests:	knitr, rmarkdown, testthat
VignetteBuilder:	knitr
URL:	https://github.com/Japal/MALDIrppa
BugReports:	https://github.com/Japal/MALDIrppa/issues
NeedsCompilation:	no
Packaged:	2024-01-23 16:12:30 UTC; Japal
Author:	Javier Palarea-Albaladejo [cre, aut], Paolo Ribeca [ctb]
Date/Publication:	2024-01-25 17:30:06 UTC

MALDI mass spectra robust pre-processing and analysis

Description

This package provides procedures for quality control and robust pre-processing and analysis of MALDI mass spectrometry data based on objects and methods from the MALDIquant package. Moreover, it includes some additional functionalities and data summary and management tools (see vignette).

Details

Author(s)

Javier Palarea-Albaladejo

Maintainer: Javier Palarea-Albaladejo <javier.palarea@udg.edu>

Add metadata to AbstractMassObject class objects

Description

This function adds metadata to the metaData slot of an AbstractMassObject-class class object.

Usage

addMetadata(x, metadata, pos)

Arguments

Value

List of AbstractMassObject-class class objects including the new metadata in their metaData slot.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects
data(type)    # metadata

# Add metadata

info <- paste("Spectrum No.",1:length(spectra)) # Artificial metadata vector

spectra2 <- addMetadata(spectra,info,1)

# Check info in metaData slot

spectra2[[1]]@metaData

Compact peak alignment process for MassPeaks objects

Description

This function provides a single command for selecting anchor peaks, peak alignment and binning of MassPeaks class objects (MALDIquant package). It also deals with alignment-related issues found in high-resolution mass spectrometry data.

Usage

alignPeaks(x, minFreq = 0.9, tolerance = 0.003, ...)

Arguments

Details

See warpMassPeaks and binPeaks in the MALDIquant package for details about the alignment and binning algorithms. Note that alignPeaks applies an additional binning round which helps to correct for misalignment issues found after using the default strict or relaxed bin creation rules in high-resolution mass spectrometry data.

Value

A list of MassPeaks class objects with aligned peaks along a common m/z range.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Some pre-processing

spectra <- screenSpectra(spectra)$fspectra
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)

# Peak alignment

peaks <- alignPeaks(peaks, minFreq = 0.8)

Count the number of peaks in MassPeaks objects

Description

This function provides the number of peaks of each element of a list of MassPeaks objects.

Usage

countPeaks(x)

Arguments

Value

A vector consisting of the number of peaks for each peak profile in x.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Some pre-processing

spectra <- screenSpectra(spectra)$fspectra
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)

# Count peaks

npeaks <- countPeaks(peaks)

Delete peaks from a MassPeaks objects

Description

This function deletes peaks of height (intensity) below a given value in MassPeaks objects.

Usage

deletePeaks(x, min = NULL)

Arguments

Details

This functions takes a list of MassPeaks objects and filters out peaks of height (intensity) falling below the given minimum value.

Value

A filtered list of MassPeaks objects.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Some pre-processing

spectra <- screenSpectra(spectra)$fspectra
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)

# Delete peaks of intensity < 30

peaks <- deletePeaks(peaks, min = 30)

Detection of outlying mass peak profiles

Description

This function identifies outlying cases in a collection of processed mass peak profiles. It can be applied either on peak intensities or binary data (peak presence/absence patterns). It allows to specify a grouping factor in order to execute the procedure at the desired level of aggregation.

Usage

detectOutliers(x, by = NULL, binary = FALSE, ...)

Arguments

Details

This function marks samples with mass peak profiles that largely deviates from other samples at the given aggregation level. It uses robust methods for the detection of multivariate outliers applied on metric multidimensional scaling (MDS) coordinates (Euclidean distance is used for peak intensities and binary distance for binary profiles; see dist). The number of MDS coordinates used is generally set to p = floor(n/2), where n is the number of samples in the target subset. This is an upper cap recommended for the computation of the robust MCD estimator by covMcd. However, that rule of thumb can still generate matrix singularity problems with covMcd in some cases. When this occurs detectOutliers further reduces p to use the maximum number of MDS coordinates giving rise to a non-singular covariance matrix (min(p) = 2 in any case). The adaptive multivariate outlier detection algorithm was adapted from the mvoutlier package.

Value

If by = NULL, a logical vector of length equal to the number of elements of x indicating outlying samples by TRUE. Otherwise, a 2-column data.frame is generated which includes such a logical vector along with the grouping variable given in by.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects
data(type) # metadata

# Some pre-processing

sc.results <- screenSpectra(spectra,meta=type)
spectra <- sc.results$fspectra # filtered mass spectra
type <- sc.results$fmeta    # filtered metadata  
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)
peaks <- alignPeaks(peaks, minFreq = 0.8)

# Find outlying samples at isolate level

out <- detectOutliers(peaks, by = type$isolate)

# From peak presence/absence patterns

out.binary <- detectOutliers(peaks, by = type$isolate, binary = TRUE)

  

Convert mass spectra from text files into MassSpectrum objects

Description

This function allows to import collections of mass spectra stored in individual text files into a list of MassSpectrum objects.

Usage

importSpectra(where = getwd())

Arguments

Details

This functions works with dat, csv or txt file types containing two columns: the first one referring to common m/z values and the second one to intensities (using single-space separator between both and no column names). It reads all the .dat, .csv or .txt files in the given folder (so unrelated files should better not be there) and creates a list of MassSpectrum objects. For importing data from more specialised file formats we refer the reader to the package MALDIquantForeign.

Value

A list of MassSpectrum objects.

Examples

# Create fake mass spectrometry data

s1 <- cbind(1:20, rlnorm(20))
s2 <- cbind(1:20, rlnorm(20))
s3 <- cbind(1:20, rlnorm(20))

# Save as csv files in temporary directory

path <- tempdir()

write.table(s1, file = file.path(path, "s1.csv"),
            row.names = FALSE, col.names = FALSE, sep=" ")
write.table(s2, file = file.path(path, "s2.csv"),
            row.names = FALSE, col.names = FALSE, sep=" ")
write.table(s3, file = file.path(path, "s3.csv"),
            row.names = FALSE, col.names = FALSE, sep=" ")

# Import files and arrange into a list of MassSpectrum objects

spectra <- importSpectra(where = path)

Display peak presence/absence patterns

Description

This function displays the patterns of peak presence and absence in an intensity matrix as generated from intensityMatrix.

Usage

peakPatterns(x, abs.lab = NA, barplot = TRUE,
             axis.lab = c("m/z", "Index"), bar.col = "red3",
             cell.col = c("white", "dodgerblue"), grid = FALSE,
             grid.col = "black", grid.lty = "dotted", cex.axis = 0.5,
             cex.lab = 0.5, ...)

Arguments

Details

The peak presence/absence patterns are displayed by rows from the first (top) to the last (bottom) sample in the data set x over the range of common m/z points. Positive peaks are by default represented by coloured cells whereas zero or absent peaks are left blank. A barplot on the top margins shows the relative frequency of a peak at each m/z point across samples.

Value

No return value, graphical output.

See Also

See intensityMatrix.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects
data(type) # metadata

# Some pre-processing

sc.results <- screenSpectra(spectra,meta=type)
spectra <- sc.results$fspectra # filtered mass spectra
type <- sc.results$fmeta    # filtered metadata  
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)
peaks <- alignPeaks(peaks, minFreq = 0.8)

# Display patterns across all data

peakPatterns(peaks)

# Check results within isolate 280

peakPatterns(peaks[type$Isolate=="280"])

Graphical summary of screenSpectra objects

Description

This is a plot method for scSpectra objects that displays the results from applying screenSpectra to identify potential faulty, low-quality raw mass spectra.

Usage

## S3 method for class 'scSpectra'
plot(x, type = c("index", "hist", "casewise"), breaks = 30,
        labels = FALSE, col = "green3", ...)

Arguments

Details

For type = "index" (default) the upper and lower fences used to declare a mass spectrum as potentially low-quality are shown along with their A scores (see screenSpectra for details). Point labels can be added using the labels argument (either a position index when labels = TRUE or a given vector of labels; see examples below). For type = "hist" a histogram of the distribution of the A scores is produced along with the tolerance fences. Finally, type = "casewise" displays interactively the flagged spectra for visual inspection.

Value

No return value, graphical output.

See Also

See screenSpectra and summary.scSpectra.

Examples

# Load example data

data(spectra) # list of MassSpectra objects
data(type)    # metadata

sc.results <- screenSpectra(spectra)
plot(sc.results)
plot(sc.results, labels = TRUE)
plot(sc.results, labels = type$SpectID)
plot(sc.results, type = "hist")

Create list of MassPeaks objects

Description

This is an auxiliary function to create a list of MassPeaks objects from raw data.

Usage

rawToPeaks(mz, I)

Arguments

Details

This functions creates a list of MassPeaks objects from a vector of common m/z values and a matrix of column vectors of peak intensities for a collections of mass peak profiles. The column names are used to label the elements of the list.

Value

A list of MassPeaks objects.

Create list of MassSpectrum objects

Description

This is an auxiliary function to create a list of MassSpectrum objects from raw data.

Usage

rawToSpectra(mz, I)

Arguments

Details

This functions creates a list of MassSpectrum objects from a vector of common m/z values and a matrix of column vectors of intensities for a collections of mass spectra. The column names are used to label the elements of the list.

Value

A list of MassSpectrum objects.

See Also

See importSpectra.

Reduce resolution of MassSpectrum objects

Description

This function allows to obtain a lighter version of a list of MassSpectrum objects by decreasing their m/z resolution.

Usage

redResolution(x, by = 1)

Arguments

Details

This function reduces the resolution of mass spectra by eliminating a regular sequence of m/z sampling points in steps given by the argument by. For example, specifiying by = 2 means to reduce the length and memory usage of the signal by a half approximately.

Value

A list of MassSpectrum objects.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Reduce resolution by a half

spectra.LowRes <- redResolution(spectra, by = 2)

Identification of potentially low-quality raw mass spectra

Description

This function implements a quality control check to help in the identification of possibly faulty, low-quality raw mass spectra. It computes an atypicality score and labels suspicious profiles for further inspection and filtering.

Usage

screenSpectra(x, meta = NULL, threshold = 1.5, estimator = c("Q", "MAD"),
                 method = c("adj.boxplot", "boxplot", "ESD", "Hampel", "RC"),
                 nd = 1, lambda = 0.5, ...)

Arguments

Details

The procedure computes an atypicality score (A score) based on a weighted function of two components: (1) a robust scale estimator (Q or MAD) of the n-order derivative (computed using Savitzky-Golay smoothing filter) of scaled mass spectra and (2) the median intensity of the signals. Given a method to determine tolerance fences, a mass spectrum is labelled as potentially faulty, low-quality according to the magnitude of its A score. The adj.boxplot method based on the Q scale estimator and equal weights to both components (lambda = 0.5) are the default options. The greater lambda the higher the weight given to the scale estimator in the A score. The function produces summaries and a list of mass spectra and (if given) associated metadata in which the identified cases were filtered out.

Value

An object of class scSpectra with elements:

See Also

See methods summary.scSpectra and plot.scSpectra for scSpectra objects.

Examples

# Load example data

data(spectra) # list of MassSpectra objects
data(type)    # metadata

# Results using different settings

sc.results <- screenSpectra(spectra)
sc.results <- screenSpectra(spectra, type)
sc.results <- screenSpectra(spectra, type, method = "RC")
sc.results <- screenSpectra(spectra, type, threshold = 3, estimator = "MAD", method = "Hampel")

# Numerical and graphical summary

summary(sc.results)
plot(sc.results)

# Save filtered data for further pre-processing

filtered.spectra <- sc.results$fspectra
filtered.type <- sc.results$fmeta

Extract signal-to-noise ratio thresholds from MassPeaks objects

Description

This function extracts the thresholds used to determine peaks from mass spectra based on signal-to-noise ratio (SNR) (threshold equal to SNR*noise).

Usage

snrPeaks(x)

Arguments

Details

Given a collection of MassPeaks objects as obtained from detectPeaks, this function provides the thresholds used in each case to determine peaks from the original mass spectra. The thresholds are calculated as the product of a SNR value set by the user and the estimated noise of the signal (see detectPeaks).

Value

A list of vectors of SNR-based thresholds, one for each sample.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Some pre-processing

spectra <- screenSpectra(spectra)$fspectra
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)

# Extract thresholds for each mass peak profile

SNRs <- snrPeaks(peaks)

Example mass spectra profiles

Description

List of mass spectra (MassSpectrum class).

Usage

data(spectra)

Details

Low-resolution version of a MALDI-TOF mass spectrometry data set in the range [2500, 13000] m/z provided for illustration purposes. It consists of 4 technical replicates of 5 biological replicates from 19 bacterial isolates (see type for associated metadata).

Examples

data(spectra)

str(spectra[[1]])

plot(spectra[[1]])

Numerical summary of screenSpectra objects

Description

This is a summary method for scSpectra objects that generates a numerical summary of the settings and results from applying screenSpectra to identify potential faulty, low-quality raw mass spectra.

Usage

## S3 method for class 'scSpectra'
summary(object, ncases = 10, ...)

Arguments

Details

A table is generated that includes details of the numerical estimations along with mass spectra ID, A score and the label for each mass spectra, either potentially low-quality (failure) or good-quality (success).

Value

No return value, text printed on console.

See Also

See screenSpectra and plot.scSpectra.

Examples

# Load example data

data(spectra) # list of MassSpectra objects

sc.results <- screenSpectra(spectra)
summary(sc.results)

Summary of mass peak profiles

Description

This function generates a numerical summary of a collection of MassPeaks objects.

Usage

summaryPeaks(x, digits = 4)

Arguments

Details

For each MassPeaks on the list this function provides summary statistics of m/z points, peak intensities and SNR thresholds (number, minimum, mean, standard deviation, median, mean absolute deviation, maximum).

Value

A data.frame containing summary information of a collection of MassPeaks objects.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects
data(type)    # metadata

# Some pre-processing

sc.results <- screenSpectra(spectra, meta = type)

spectra <- sc.results$fspectra
type <- sc.results$fmeta

spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)

names(peaks) <- type$SpectID # spectra IDs are lost after removeBaseline()

# Summary of peak profile features (results for positions 10 to 20)

summaryPeaks(peaks[10:20])

Summary of mass spectra

Description

This function generates a numerical summary of a collection of MassSpectrum objects.

Usage

summarySpectra(x, digits = 4)

Arguments

Details

For each MassSpectrum on the list this function provides summary statistics of m/z points and signal intensities (number, minimum, mean, standard deviation, median, mean absolute deviation, maximum).

Value

A data.frame containing summary information of a collection of MassSpectrum objects.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects
data(type)    # metadata

# Summary of spectra features (results for 20 first mass spectra)

summarySpectra(spectra[1:20])

# Some pre-processing

sc.results <- screenSpectra(spectra, meta = type)

spectra <- sc.results$fspectra
type <- sc.results$fmeta

spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)

names(spectra) <- type$SpectID # spectra IDs are lost with removeBaseline()

# Summary of spectra features (results for positions 10 to 20)

summarySpectra(spectra[10:20])

Transform intensity of MassSpectrum objects

Description

This function applies user-defined transformations on the intensities of MassSpectrum objects.

Usage

transfIntensity(x, fun = NULL, ...)

Arguments

Details

This function allows the user to define any sensible function to be applied on signal intensities. For logarithm and square root transformations it is equivalent to transformIntensity in the MALDIquant package.

Value

A list of MassSpectrum objects with signal intensities transformed according to fun.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Scale intensities into [0, 1] by dividing by their maximum value

scale.max <- function(x){x/max(x)} # define scaling function

scaled.spectra <- transfIntensity(spectra, fun = scale.max)
    
# Compute natural logarithm of intensity values (using the pre-defined sqrt R function)

log.spectra <- transfIntensity(spectra, sqrt)

Example mass spectra metadata

Description

Metadata associated to the spectra data set containing information about isolate, biological and technical replicate numbers and mass spectra IDs.

Usage

data(type)

Format

The format is:

Isolate: Factor w/ 14 levels "280","43","45",..: 2 2 2 2 2 2 2 2 2 2 ...

BioRep : int 1 1 1 1 2 2 2 2 3 3 ...

TechRep: int 1 2 3 4 1 2 3 4 1 2 ...

SpectID: Factor w/ 315 levels "160408C13","160408C14",..: 1 2 3 4 5 6 7 8 9 10 ...

Examples

data(type)
str(type)

Discrete wavelet transformation for MassSpectrum objects

Description

This function performs undecimated wavelet transform (UDWT) on mass spectra in MassSpectrum format. Alternatively, smoothing methods included in the MALDIquant package can be called.

Usage

wavSmoothing(x, method = c("Wavelet", "SavitzkyGolay", "MovingAverage"),
             n.levels = 4, ...)

Arguments

Details

Note that from version 1.1.0 of MALDIrppa wavelet smoothing is conducted by maximal overlap discrete wavelet transformation and universal thresholding of coefficients based on methods available on the waveslim package. The optimal level of smoothing is determined by model-driven estimates of the thresholds. The parameter n.levels (values > 0 and <= log(length(x),2)) can be used to tweak the levels to obtain a smoother or rougher result.

Alternatively, smoothing methods SavitzkyGolay and MovingAverage from the MALDIquant package can be called directly from this function.

If the previous implementation of the wavelet method is required please download and install manually source files of version 1.0.5-1 from the archive of old sources of the package (https://CRAN.R-project.org/package=MALDIrppa).

Value

A list of MassSpectrum objects with denoised signal intensities.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# sqrt transformation and signal smoothing using UDWT

spectra <- transfIntensity(spectra, fun = "sqrt")
spectra <- wavSmoothing(spectra)

Write intensity matrix in different formats

Description

This function writes an intensity matrix as generated by intensityMatrix into a file in the R, csv, NEXUS or FASTA formats. For NEXUS format it allows to specify weights for peaks.

Usage

writeIntensity(x, filename = "intMatrix", format = c("R", "csv", "NEXUS", "FASTA"),
               binary = FALSE, labels = NULL, weights=NULL, ...)

Arguments

Details

This is a wrapper function to simplify the writing of an intensity matrix in different formats while adding some extra features. It includes the common NEXUS and FASTA formats as an extension of functions in the ape package to handle peak intensity data. It also allows for taxa/sample pre-computed peak weights to be included in the NEXUS file. It checks whether the names meet NEXUS name conventions and gives them adequate format if not. A binary intensity matrix is always internally generated (binary = TRUE) when either the NEXUS or FASTA format is chosen. If any, NA values in x are assumed to denote zero intensity/peak absence and are then converted into zeros.

Value

No return value, file in selected format created on destination folder.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects

# Some pre-processing

spectra <- screenSpectra(spectra)$fspectra
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)
peaks <- alignPeaks(peaks, minFreq = 0.8)

# Intensity matrix

int <- intensityMatrix(peaks)

# Save as R file (saved to a temporary location as an example)

writeIntensity(int, file = file.path(tempdir(), "int"))

# Save as binary NEXUS file (saved to a temporary location as an example)

writeIntensity(int, file = file.path(tempdir(),"int.binary"),
               format = "NEXUS", interleaved = FALSE)

Write metadata in different formats

Description

This function is simply a wrapper to write the metadata associated with a collection of mass spectra into a file in either the R or csv format.

Usage

writeMetadata(x, filename = "Metadata", format = c("R", "csv"), ...)

Arguments

Details

It uses either save or write.table to store the metadata. Check these functions for adequate data formats.

Value

No return value, file in selected format created on destination folder.

Examples

# Load example data

data(spectra) # list of MassSpectra class objects
data(type)    # metadata

# Some pre-processing

sc.spectra <- screenSpectra(spectra, meta = type)

spectra <- sc.spectra$fspectra # filtered spectra
type <- sc.spectra$fmeta # filtered metadata

spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)
peaks <- alignPeaks(peaks, minFreq = 0.8)

# Intensity matrix

int <- intensityMatrix(peaks)

# Save resulting data in R format (to a temporary location as an example)

writeIntensity(int, filename = file.path(tempdir(),"MyintMatrix"))
writeMetadata(type, filename = file.path(tempdir(),"MyMetadata"))

# Save resulting data in csv format (to a temporary location as an example)

writeIntensity(int, filename = file.path(tempdir(),"MyintMatrix"),
               format = "csv")
writeMetadata(type, filename = file.path(tempdir(),"MyMetadata"),
               format = "csv")