,
Paolo Ribeca [ctb] | Title: | MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis |
|---|---|
| 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>). |
| Authors: | Javier Palarea-Albaladejo [cre, aut]
,
Paolo Ribeca [ctb] |
| Maintainer: | Javier Palarea-Albaladejo <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1.0-2 |
| Built: | 2025-01-27 04:55:50 UTC |
| Source: | https://github.com/Japal/MALDIrppa |
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).
| Package: | MALDIrppa |
| Type: | Package |
| Version: | 1.1.0-2 |
| Date: | 2024-01-23 |
| License: | GPL (>= 2) |
Javier Palarea-Albaladejo
Maintainer: Javier Palarea-Albaladejo <[email protected]>
AbstractMassObject class objects
This function adds metadata to the metaData slot of an AbstractMassObject-class class object.
addMetadata(x, metadata, pos)addMetadata(x, metadata, pos)
x |
List of |
metadata |
Vector containing the metadata to be included for each element of |
pos |
Position of the new metadata within the |
List of AbstractMassObject-class class objects including the new metadata in their metaData slot.
# 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# 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
MassPeaks objects
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.
alignPeaks(x, minFreq = 0.9, tolerance = 0.003, ...)alignPeaks(x, minFreq = 0.9, tolerance = 0.003, ...)
x |
A list of |
minFreq |
Minimum relative frequency of a peak over |
tolerance |
Maximal deviation in peak masses to be considered as identical (see |
... |
Other arguments from the original functions in |
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.
A list of MassPeaks class objects with aligned peaks along a common m/z range.
# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) # Peak alignment peaks <- alignPeaks(peaks, minFreq = 0.8)# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) # Peak alignment peaks <- alignPeaks(peaks, minFreq = 0.8)
MassPeaks objects
This function provides the number of peaks of each element of a list of MassPeaks objects.
countPeaks(x)countPeaks(x)
x |
A list of |
A vector consisting of the number of peaks for each peak profile in x.
# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) # Count peaks npeaks <- countPeaks(peaks)# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) # Count peaks npeaks <- countPeaks(peaks)
MassPeaks objects
This function deletes peaks of height (intensity) below a given value in MassPeaks objects.
deletePeaks(x, min = NULL)deletePeaks(x, min = NULL)
x |
A list of |
min |
Lower threshold used to discard a peak. |
This functions takes a list of MassPeaks objects and filters out peaks of height (intensity) falling below the given minimum value.
A filtered list of MassPeaks objects.
# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) # Delete peaks of intensity < 30 peaks <- deletePeaks(peaks, min = 30)# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$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)
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.
detectOutliers(x, by = NULL, binary = FALSE, ...)detectOutliers(x, by = NULL, binary = FALSE, ...)
x |
A list of |
by |
If given, a grouping variable ( |
binary |
Logical value. It indicates whether the procedure must be applied on either peak intensities ( |
... |
Optional arguments for the robust outlier detection method. |
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.
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.
# 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)# 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)
MassSpectrum objects
This function allows to import collections of mass spectra stored in individual text files into a list of MassSpectrum objects.
importSpectra(where = getwd())importSpectra(where = getwd())
where |
Path to the folder where the text files are stored (default: current working directory). |
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.
A list of MassSpectrum objects.
# 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)# 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)
This function displays the patterns of peak presence and absence in an intensity matrix as generated from intensityMatrix.
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, ...)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, ...)
x |
A |
abs.lab |
Unique label used to denote peak absence in |
barplot |
Logical value indicating whether a barplot of relative peak frequency across samples is displayed ( |
axis.lab |
Vector of axis labels in the |
bar.col |
Colour of the bars in the barplot. |
cell.col |
Vector of colours for the table cells (format |
grid |
Logical value indicating whether gridlines are added ( |
grid.col |
Colour of the gridlines ( |
grid.lty |
Style of the gridlines ( |
cex.axis |
Axis tick labels scaling factor relative to default. |
cex.lab |
Axis labels scaling factor relative to default. |
... |
Other arguments. |
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.
No return value, graphical output.
See intensityMatrix.
# 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$Isolate=="280"])# Load example data data(spectra) # list of MassSpectra class objects data(type) # metadata # Some pre-processing sc.results <- screenSpectra(spectra,meta=type) spectra <- sc.results$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"])
screenSpectra objects
This is a plot method for scSpectra objects that displays the results from applying screenSpectra to identify potential faulty, low-quality raw mass spectra.
## S3 method for class 'scSpectra' plot(x, type = c("index", "hist", "casewise"), breaks = 30, labels = FALSE, col = "green3", ...)## S3 method for class 'scSpectra' plot(x, type = c("index", "hist", "casewise"), breaks = 30, labels = FALSE, col = "green3", ...)
x |
A |
type |
Type of graphical display. |
breaks |
Number of break points for plotting a histogram when |
labels |
Vector of labels for the mass spectra (default = |
col |
Colour for the histogram bars when |
... |
Other arguments. |
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.
No return value, graphical output.
See screenSpectra and summary.scSpectra.
# 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$SpectID) plot(sc.results, type = "hist")# Load example data data(spectra) # list of MassSpectra objects data(type) # metadata sc.results <- screenSpectra(spectra) plot(sc.results) plot(sc.results, labels = TRUE) plot(sc.results, labels = type$SpectID) plot(sc.results, type = "hist")
MassPeaks objects
This is an auxiliary function to create a list of MassPeaks objects from raw data.
rawToPeaks(mz, I)rawToPeaks(mz, I)
mz |
Vector of m/z values. |
I |
Matrix of peak intensity values. |
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.
A list of MassPeaks objects.
MassSpectrum objects
This is an auxiliary function to create a list of MassSpectrum objects from raw data.
rawToSpectra(mz, I)rawToSpectra(mz, I)
mz |
Vector of m/z values. |
I |
Matrix of intensity values. |
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.
A list of MassSpectrum objects.
See importSpectra.
MassSpectrum objects
This function allows to obtain a lighter version of a list of MassSpectrum objects by decreasing their m/z resolution.
redResolution(x, by = 1)redResolution(x, by = 1)
x |
A list of |
by |
Number of times reduction ( |
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.
A list of MassSpectrum objects.
# Load example data data(spectra) # list of MassSpectra class objects # Reduce resolution by a half spectra.LowRes <- redResolution(spectra, by = 2)# Load example data data(spectra) # list of MassSpectra class objects # Reduce resolution by a half spectra.LowRes <- redResolution(spectra, by = 2)
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.
screenSpectra(x, meta = NULL, threshold = 1.5, estimator = c("Q", "MAD"), method = c("adj.boxplot", "boxplot", "ESD", "Hampel", "RC"), nd = 1, lambda = 0.5, ...)screenSpectra(x, meta = NULL, threshold = 1.5, estimator = c("Q", "MAD"), method = c("adj.boxplot", "boxplot", "ESD", "Hampel", "RC"), nd = 1, lambda = 0.5, ...)
x |
A list of |
meta |
(optional) Matrix or vector containing metadata associated to |
threshold |
Multiplicative factor used in computing the upper and lower fences to determine passes and failures. It is related to the actual method used to compute the fences (see |
estimator |
Robust scale estimator used:
|
method |
Method used to compute upper and lower fences for the identification of atypical mass spectra.
|
nd |
Order for the derivative function of the mass spectra (default = 1). |
lambda |
Weight given to each component of the atypicality score (values in [0, 1], default = 0.5, see details below). |
... |
Other arguments. |
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.
An object of class scSpectra with elements:
fspectra |
List of mass spectra ( |
fmeta |
Associated filtered metadata ( |
est.table |
Results table showing the mass spectra ID, A score and label (pass/failure). |
... |
Other details (see method |
See methods summary.scSpectra and plot.scSpectra for scSpectra objects.
# 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# 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
MassPeaks objects
This function extracts the thresholds used to determine peaks from mass spectra based on signal-to-noise ratio (SNR) (threshold equal to SNR*noise).
snrPeaks(x)snrPeaks(x)
x |
A list of |
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).
A list of vectors of SNR-based thresholds, one for each sample.
# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) # Extract thresholds for each mass peak profile SNRs <- snrPeaks(peaks)# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$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)
List of mass spectra (MassSpectrum class).
data(spectra)data(spectra)
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).
data(spectra) str(spectra[[1]]) plot(spectra[[1]])data(spectra) str(spectra[[1]]) plot(spectra[[1]])
screenSpectra objects
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.
## S3 method for class 'scSpectra' summary(object, ncases = 10, ...)## S3 method for class 'scSpectra' summary(object, ncases = 10, ...)
object |
A |
ncases |
Number of cases shown in the results table. |
... |
Other arguments. |
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).
No return value, text printed on console.
See screenSpectra and plot.scSpectra.
# Load example data data(spectra) # list of MassSpectra objects sc.results <- screenSpectra(spectra) summary(sc.results)# Load example data data(spectra) # list of MassSpectra objects sc.results <- screenSpectra(spectra) summary(sc.results)
This function generates a numerical summary of a collection of MassPeaks objects.
summaryPeaks(x, digits = 4)summaryPeaks(x, digits = 4)
x |
A list of |
digits |
Integer indicating the number of decimal places to be used. |
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).
A data.frame containing summary information of a collection of MassPeaks objects.
# 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$SpectID # spectra IDs are lost after removeBaseline() # Summary of peak profile features (results for positions 10 to 20) summaryPeaks(peaks[10:20])# Load example data data(spectra) # list of MassSpectra class objects data(type) # metadata # Some pre-processing sc.results <- screenSpectra(spectra, meta = type) spectra <- sc.results$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])
This function generates a numerical summary of a collection of MassSpectrum objects.
summarySpectra(x, digits = 4)summarySpectra(x, digits = 4)
x |
A list of |
digits |
Integer indicating the number of decimal places to be used. |
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).
A data.frame containing summary information of a collection of MassSpectrum objects.
# 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$SpectID # spectra IDs are lost with removeBaseline() # Summary of spectra features (results for positions 10 to 20) summarySpectra(spectra[10:20])# Load example data data(spectra) # list of MassSpectra class objects data(type) # metadata # Summary of spectra features (results for 20 first mass spectra) summarySpectra(spectra[1:20]) # Some pre-processing sc.results <- screenSpectra(spectra, meta = type) spectra <- sc.results$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])
MassSpectrum objects
This function applies user-defined transformations on the intensities of MassSpectrum objects.
transfIntensity(x, fun = NULL, ...)transfIntensity(x, fun = NULL, ...)
x |
A list of |
fun |
Name of an user-defined transformation function or any other pre-defined one in R. |
... |
Other arguments. |
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.
A list of MassSpectrum objects with signal intensities transformed according to fun.
# 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)# 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)
Metadata associated to the spectra data set containing information about isolate, biological and technical replicate numbers and mass spectra IDs.
data(type)data(type)
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 ...
data(type) str(type)data(type) str(type)
MassSpectrum objects
This function performs undecimated wavelet transform (UDWT) on mass spectra in MassSpectrum format. Alternatively, smoothing methods included in the MALDIquant package can be called.
wavSmoothing(x, method = c("Wavelet", "SavitzkyGolay", "MovingAverage"), n.levels = 4, ...)wavSmoothing(x, method = c("Wavelet", "SavitzkyGolay", "MovingAverage"), n.levels = 4, ...)
x |
A list of |
method |
Smoothing method used. |
n.levels |
Depth of the decomposition for wavelet-based smoothing. |
... |
Other arguments. |
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).
A list of MassSpectrum objects with denoised signal intensities.
# 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)# 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)
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.
writeIntensity(x, filename = "intMatrix", format = c("R", "csv", "NEXUS", "FASTA"), binary = FALSE, labels = NULL, weights=NULL, ...)writeIntensity(x, filename = "intMatrix", format = c("R", "csv", "NEXUS", "FASTA"), binary = FALSE, labels = NULL, weights=NULL, ...)
x |
Intensity matrix as obtained from |
filename |
A character string specifying a name for the destination file (filename extension not required). |
format |
One of R (default |
binary |
Logical value. If |
labels |
Optional vector of ID labels for the samples. |
weights |
Optional numeric vector of peak weights (NEXUS format). |
... |
Additional arguments. |
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.
No return value, file in selected format created on destination folder.
# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$fspectra spectra <- transformIntensity(spectra, method = "sqrt") spectra <- wavSmoothing(spectra) spectra <- removeBaseline(spectra) peaks <- detectPeaks(spectra) peaks <- alignPeaks(peaks, minFreq = 0.8) # Intensity matrix int <- intensityMatrix(peaks) # Save as R file (saved to a temporary location as an example) writeIntensity(int, file = file.path(tempdir(), "int")) # Save as binary NEXUS file (saved to a temporary location as an example) writeIntensity(int, file = file.path(tempdir(),"int.binary"), format = "NEXUS", interleaved = FALSE)# Load example data data(spectra) # list of MassSpectra class objects # Some pre-processing spectra <- screenSpectra(spectra)$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)
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.
writeMetadata(x, filename = "Metadata", format = c("R", "csv"), ...)writeMetadata(x, filename = "Metadata", format = c("R", "csv"), ...)
x |
Metadata in any sensible data format, preferably |
filename |
A character string specifying a name for the destination file (filename extension not required). |
format |
One of R (default |
... |
Other arguments. |
It uses either save or write.table to store the metadata. Check these functions for adequate data formats.
No return value, file in selected format created on destination folder.
# 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")# 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")