Package 'MassSpecWavelet'

Title: Peak Detection for Mass Spectrometry data using wavelet-based algorithms
Description: Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See <doi:10.1093/bioinformatics/btl355} for further details.
Authors: Pan Du [aut], Warren Kibbe [aut], Simon Lin [aut], Sergio Oller Moreno [aut, cre]
Maintainer: Sergio Oller Moreno <[email protected]>
License: LGPL (>= 2)
Version: 1.73.1
Built: 2024-12-18 08:32:00 UTC
Source: https://github.com/bioc/MassSpecWavelet

Help Index


Peak detection of mass spectrum by Wavelet transform based methods

Description

MassSpecWavelet R package is aimed to detect peaks on Mass Spectrometry (MS) data using Continuous Wavelet Transform (CWT).

Author(s)

Pan Du, Simon Lin

References

Du, P., Kibbe, W.A. and Lin, S.M. (2006) Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, 22, 2059-2065.

See Also

Useful links:

Examples

data(exampleMS)
SNR.Th <- 3
peakInfo <- peakDetectionCWT(exampleMS, SNR.Th = SNR.Th)
majorPeakInfo <- peakInfo$majorPeakInfo
peakIndex <- majorPeakInfo$peakIndex
plotPeak(exampleMS, peakIndex, main = paste("Identified peaks with SNR >", SNR.Th))

Continuous Wavelet Transform (CWT)

Description

CWT(Continuous Wavelet Transform) with Mexican Hat wavelet (by default) to match the peaks in Mass Spectrometry spectrum

Usage

cwt(ms, scales = 1, wavelet = "mexh")

Arguments

ms

Mass Spectrometry spectrum (a vector of MS intensities)

scales

a vector represents the scales at which to perform CWT. See the Details section. Additionally, a prepared_wavelets object is also accepted (see prepareWavelets()).

wavelet

The wavelet base, Mexican Hat by default. User can provide wavelet Psi(x) as a form of two row matrix. The first row is the x value, and the second row is Psi(x) corresponding to x.

Details

The default mother wavelet is a Mexican Hat evaluated in the [8,8][-8,8] range using 1024 points (a step of roughly 1/64):

ψ(x)=23π0.25(1x2)expx2/2\psi(x) = \frac{2}{\sqrt{3}} \pi^{-0.25} ( 1 - x^2 ) \exp{-x^2/2}

The σ\sigma of the mother Mexican Hat is of one x unit.

The scaled wavelet is a downsampled version of the mother wavelet. The scale determines how many samples per xx unit are taken. For instance, with the default Mexican Hat wavelet, a scales = 2 will evaluate the [8,8][-8, 8] range sampling twice per xx unit, this is with a step of 0.5. This generates a 33 points long scaled wavelet. Choosing this type of scaling is convenient because the scaled wavelet becomes a wavelet of σ=scales\sigma = `scales` points. Using the default wavelet, a scales smaller than 1 will show sampling issues, while a scales larger than 64 will resample points from the original mother wavelet. If you need to use scales larger than 64, consider providing your own mother wavelet. See the examples.

According to doi:10.1063/1.3505103, if your spectrum has a gaussian peak shape of variance m2m^2 points then the scales range should cover [1,1.9m][1, 1.9 m]. If your spectrum has a Lorentzian peak shape of half-width-half-maximum LL points then the scales range should cover [1,2.9L][1, 2.9 L]. They also suggest using a log1.18\log_{1.18} spacing. Take these values as suggestions for your analysis.

Value

The return is the 2-D CWT coefficient matrix, with column names as the scale. Each column is the CWT coefficients at that scale. If the scales are too big for the given signal, the returned matrix may include less columns than the given scales.

Author(s)

Pan Du, Simon Lin

Examples

data(exampleMS)
scales <- seq(1, 64, 3)
wCoefs <- cwt(exampleMS[5000:11000], scales = scales, wavelet = "mexh")

## Plot the 2-D CWT coefficients as image (It may take a while!)
xTickInterval <- 1000
image(5000:11000, scales, wCoefs,
    col = terrain.colors(256), axes = FALSE,
    xlab = "m/z index", ylab = "CWT coefficient scale", main = "CWT coefficients"
)
axis(1, at = seq(5000, 11000, by = xTickInterval))
axis(2, at = c(1, seq(10, 64, by = 10)))
box()

## Provide a larger wavelet:
scales <- c(seq(1, 64, 3), seq(72, 128, 8))
psi_xval <- seq(-8, 8, length.out = 4096)
psi <- (2 / sqrt(3) * pi^(-0.25)) * (1 - psi_xval^2) * exp(-psi_xval^2 / 2)
wCoefs <- cwt(exampleMS[5000:11000], scales = scales, wavelet = rbind(psi_xval, psi))
xTickInterval <- 1000
image(5000:11000, scales, wCoefs,
    col = terrain.colors(256), axes = FALSE,
    xlab = "m/z index", ylab = "CWT coefficient scale", main = "CWT coefficients"
)
axis(1, at = seq(5000, 11000, by = xTickInterval))
axis(2, at = c(1, seq(10, 128, by = 10)))
box()

## Custom log1.18 spaced scales:
get_scales <- function(scale_min, scale_max, num_scales) {
    (seq(0, 1, length.out = num_scales)^1.18) * (scale_max - scale_min) + scale_min
}
scales <- get_scales(scale_min = 1, scale_max = 64, num_scales = 32)
print(scales)
# For detecting a gaussian peak of 10 points:
gaussian_peak_sigma <- 10 # points
scales <- get_scales(scale_min = 1, scale_max = 1.9 * gaussian_peak_sigma, num_scales = 32)
print(scales)
# For detecting a lorentzian peak of 10 points:
lorentzian_peak_gamma <- 10 # points
scales <- get_scales(scale_min = 1, scale_max = 2.9 * lorentzian_peak_gamma, num_scales = 32)
print(scales)

An example mass spectrum

Description

An example mass spectrum from CAMDA 2006. All-in-1 Protein Standard II (Ciphergen Cat. \# C100-0007) were measured on Ciphergen NP20 chips. There are 7 polypeptides in the sample with m/z values of 7034, 12230, 16951, 29023, 46671, 66433, 147300.

Format

A numeric vector represents the mass spectrum with equal sample intervals.

Source

CAMDA, CAMDA 2006 Competition Data Set. 2006, http://camda.duke.edu.


Find local maxima and return the size of the window where they are maximum.

Description

Compared to the rest of the package, this is a rather experimental function. If you plan to use it or are interested in it, please open an issue at https://github.com/zeehio/MassSpecWavelet/issues to show your interest.

Usage

findLocalMaxWinSize(x, capWinSize = NA)

Arguments

x

A numeric vector.

capWinSize

the maximum window size to report. NA means unlimited.

Value

An integer vector y of the same length as x. y[i] will be the size of the largest window on x containing x[i] where:

  • x[i] is a local maximum or a center of a plateau

  • x[i] is not at a window border Optionally, if capWinSize is a positive integer, the maximum window size is capped to that value, to increase performance. Use this in case you just want to check if there exists a window of that size. @export @examples x <- c(1, 2, 3, 2, 1) findLocalMaxWinSize(x)


Identify the local maximum of each column in 2-D CWT coefficients matrix

Description

Identify the local maximum of each column in 2-D CWT coefficients matrix by using a slide window. The size of slide window linearly changes from the coarse scale (bigger window size) to detail scale. The scale of CWT increases with the column index.

Usage

getLocalMaximumCWT(
  wCoefs,
  minWinSize = 5,
  amp.Th = 0,
  isAmpThreshRelative = FALSE,
  exclude0scaleAmpThresh = FALSE
)

Arguments

wCoefs

2-D CWT coefficients, each column corresponding to CWT coefficient at one scale. The column name is the scale.

minWinSize

The minimum slide window size used.

amp.Th

The minimum peak amplitude.

isAmpThreshRelative

Whether amp.Th is given relative to max(wCoefs).

exclude0scaleAmpThresh

When computing the relative amp.Th, if this is set to TRUE, the amp.Th will exclude the zero-th scale from the max(wCoefs). The zero-th scale corresponds to the original signal, that may have a much larger baseline than the wavelet coefficients and can distort the threshold calculation. The default is FALSE to preserve backwards compatibility.

Value

return a matrix with same dimension as CWT coefficient matrix, wCoefs. The local maxima are marked as 1, others are 0.

Author(s)

Pan Du

See Also

localMaximum()

Examples

data(exampleMS)
scales <- seq(1, 64, 3)
wCoefs <- cwt(exampleMS[5000:11000], scales = scales, wavelet = "mexh")

localMax <- getLocalMaximumCWT(wCoefs)
plotLocalMax(localMax)

Identify ridges based on the local maximum matrix

Description

Identify ridges by connecting the local maximum of 2-D CWT coefficients from the coarse scale to detail scale. The local maximum matrix is returned from getLocalMaximumCWT()

Usage

getRidge(
  localMax,
  iInit = ncol(localMax),
  step = -1,
  iFinal = 1,
  minWinSize = 5,
  gapTh = 3,
  skip = NULL,
  scaleToWinSize = "doubleodd"
)

Arguments

localMax

The local maximum matrix is returned from getLocalMaximumCWT() with 1 represents maximum, others are 0.

iInit

The start column to search ridge. By default, it starts from the coarsest scale level.

step

Search step. -1 by default, which means searching from coarse scale to detail scale column by column.

iFinal

The final column index of search ridge.

minWinSize

The minimum slide window size used.

gapTh

The gap allowed during searching for ridge. 3 by default.

skip

The column to be skipped during search.

scaleToWinSize

How scales should be mapped to window sizes. Traditionally, MassSpecWavelet used the "doubleodd" mapping (winSize <- 2*scale+1). xcms switched this mapping to "halve" (winSize <- floor(scale/2)). Besides "doubleodd" and "halve" this parameter can also be a custom function of the scale.

Value

Return a list of ridge. As some ridges may end at the scale larger than 1, in order to keep the uniqueness of the ridge names, we combined the smallest scale of the ridge and m/z index of the peak at that scale together to name the ridges. For example the ridge name "1\_653" means the peak ridge ends at the CWT scale 1 with m/z index 653 at scale 1.

Author(s)

Pan Du, Simon Lin

References

Du, P., Kibbe, W.A. and Lin, S.M. (2006) Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, 22, 2059-2065.

See Also

getLocalMaximumCWT(), identifyMajorPeaks()

Examples

data(exampleMS)
scales <- seq(1, 64, 3)
wCoefs <- cwt(exampleMS[5000:11000], scales = scales, wavelet = "mexh")

localMax <- getLocalMaximumCWT(wCoefs)
ridgeList <- getRidge(localMax)
plotRidgeList(ridgeList)

Estimate the length of the ridge

Description

Estimate the length of the ridge line, which is composed of local maxima at adjacent CWT scales. The ridge line is cut off at the end point, whose amplitude divided by the maximum ridge amplitude is larger than the cutoff amplitude ratio threshold (0.5 by default).

Usage

getRidgeLength(ridgeList, Th = 0.5)

Arguments

ridgeList

a list of identified ridges

Th

the cutoff amplitude ratio (the amplitude divided by the maximum amplitude of the ridge) threshold of the ridge line end.

Value

a vector of estimated ridge length

Author(s)

Pan Du

Examples

stopifnot(getRidgeLength(list(c(5,4,3,2,1), c(5,3,1))) == c(3,2))

Get the CWT coefficient values corresponding to the peak ridge

Description

Get the CWT coefficient values corresponding to the peak ridge

Usage

getRidgeValue(ridgeList, wCoefs, skip = 0)

Arguments

ridgeList

a list of ridge lines

wCoefs

2-D CWT coefficients

skip

the CWT scale level to be skipped, by default the 0 scale level (raw spectrum) is skipped.

Value

A list of ridge values corresponding to the input ridgeList.

Author(s)

Pan Du


Identify peaks based on the ridges in 2-D CWT coefficient matrix

Description

Indentify the peaks based on the ridge list (returned by getRidge()) in 2-D CWT coefficient matrix and estimated Signal to Noise Ratio (SNR)

Usage

identifyMajorPeaks(
  ms,
  ridgeList,
  wCoefs,
  scales = as.numeric(colnames(wCoefs)),
  SNR.Th = 3,
  peakScaleRange = 5,
  ridgeLength = 32,
  nearbyPeak = FALSE,
  nearbyWinSize = ifelse(nearbyPeak, 150, 100),
  winSize.noise = 500,
  SNR.method = "quantile",
  minNoiseLevel = 0.001,
  excludeBoundariesSize = nearbyWinSize/2
)

Arguments

ms

the mass spectrometry spectrum

ridgeList

returned by getRidge()

wCoefs

2-D CWT coefficients

scales

scales of CWT, by default it is the colnames of wCoefs

SNR.Th

threshold of SNR

peakScaleRange

the CWT scale range of the peak.

ridgeLength

the maximum ridge scale of the major peaks.

nearbyPeak

determine whether to include the small peaks close to large major peaks

nearbyWinSize

the window size to determine the nearby peaks. Only effective when nearbyPeak is true.

winSize.noise

the local window size to estimate the noise level.

SNR.method

method to estimate noise level. Currently, only 95 percentage quantile is supported.

minNoiseLevel

the minimum noise level used in calculating SNR, i.e., if the estimated noise level is less than "minNoiseLevel", it will use "minNoiseLevel" instead. If the noise level is less than 0.5, it will be treated as the ratio to the maximum amplitude of the spectrum.

excludeBoundariesSize

number of points at each boundary of the ms signal that will be excluded in search for peaks to avoid boundary effects.

Details

The determination of the peaks is based on three rules: Rule 1: The maximum ridge scale of the peak should larger than a certain threshold Rule 1.1: Based on the scale of the peak (corresponding to the maximum value of the peak ridge) should be within certain range Rule 2: Based on the peak SNR Rule 3: The peak should not appear at the boundaries of the signal.

Value

Return a list with following elements:

peakIndex

the m/z indexes of the identified peaks

peakCenterIndex

the m/z indexes of peak centers, which correspond to the maximum on the ridge. peakCenterIndex includes all the peaks, not just the identified major peaks.

peakCenterValue

the CWT coefficients (the maximum on the ridge) corresponding to peakCenterIndex

peakSNR

the SNR of the peak, which is the ratio of peakCenterValue and noise level

peakScale

the estimated scale of the peak, which corresponds to the peakCenerIndex

potentialPeakIndex

the m/z indexes of all potential peaks, which satisfy all requirements of a peak without considering its SNR. Useful, if you want to change to a lower SNR threshold later.

allPeakIndex

the m/z indexes of all the peaks, whose order is the same as peakCenterIndex, peakCenterValue, peakSNR and peakScale.

All of these return elements have peak names, which are the same as the corresponding peak ridges. see getRidge() for details.

Author(s)

Pan Du, Simon Lin

References

Du, P., Kibbe, W.A. and Lin, S.M. (2006) Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, 22, 2059-2065.

See Also

peakDetectionCWT(), tuneInPeakInfo()

Examples

data(exampleMS)
scales <- seq(1, 64, 3)
wCoefs <- cwt(exampleMS, scales = scales, wavelet = "mexh")

localMax <- getLocalMaximumCWT(wCoefs)
ridgeList <- getRidge(localMax)

SNR.Th <- 3
majorPeakInfo <- identifyMajorPeaks(exampleMS, ridgeList, wCoefs, SNR.Th = SNR.Th)
## Plot the identified peaks
peakIndex <- majorPeakInfo$peakIndex
plotPeak(exampleMS, peakIndex, main = paste("Identified peaks with SNR >", SNR.Th))

Identify local maximum within a slide window.

Description

The simplest local maximum detection using a sliding window searches for maxima in a window of a given size, and slides that window across the signal, shifting it one position at a time.

Usage

localMaximum(x, winSize = 5)

Arguments

x

a vector represents a signal profile

winSize

the slide window size, 5 by default.

Details

The default implementation found here shifts the window by half of its size instead of by one position at a time. This makes the implementation faster, at the expense of not being able to detect peaks that are too close to each other, if they appear in some positions with respect to the windows.

Additionally, this implementation removes all instances of peaks found at a distance less than the window size

Experimentally, we are exploring other algorithms for local maxima detection. These algorithms can be chosen setting the "MassSpecWavelet.localMaximum.algorithm" option. See the "Finding local maxima" vignette for further details.

Value

Return a vector with the same length of the input x. The position of local maximum is set as 1L, 0L else where.

Author(s)

Pan Du and Sergio Oller

See Also

getLocalMaximumCWT()

Examples

x <- rnorm(200)
lmax <- localMaximum(x, 5)
maxInd <- which(lmax > 0)
plot(x, type = "l")
points(maxInd, x[maxInd], col = "red")

The mexican hat function

Description

ψ(x)=23π0.25(1x2)expx2/2\psi(x) = \frac{2}{\sqrt{3}} \pi^{-0.25} ( 1 - x^2 ) \exp{-x^2/2}

Usage

mexh(x)

Arguments

x

where to evaluate the mexican hat

Value

A vector of the same length as x with the corresponding values

Examples

x <- seq(-8, 8, length.out = 256)
mexh(x)

Match m/z index to m/z value with a certain error range

Description

Match m/z index to m/z value with a certain error range

Usage

mzInd2vRange(mzInd, error = 0.003)

Arguments

mzInd

a vector of m/z index

error

error range

Value

return a vector of sorted m/z values

Author(s)

Pan Du

See Also

mzV2indRange()


Match m/z value to m/z index with a certain error range

Description

Match m/z value to m/z index with a certain error range

Usage

mzV2indRange(mzV, error = 0.003)

Arguments

mzV

a vector of m/z value

error

error range

Value

return a vector of sorted m/z indexes

Author(s)

Pan Du

See Also

mzInd2vRange()


The main function of peak detection by CWT based pattern matching

Description

This function is a wrapper of cwt(), getLocalMaximumCWT(), getRidge(), identifyMajorPeaks()

Usage

peakDetectionCWT(
  ms,
  scales = c(1, seq(2, 30, 2), seq(32, 64, 4)),
  SNR.Th = 3,
  nearbyPeak = TRUE,
  peakScaleRange = 5,
  amp.Th = 0.01,
  minNoiseLevel = amp.Th/SNR.Th,
  ridgeLength = 24,
  peakThr = NULL,
  tuneIn = FALSE,
  ...,
  exclude0scaleAmpThresh = FALSE,
  getRidgeParams = list(gapTh = 3, skip = 2)
)

Arguments

ms

the mass spectrometry spectrum

scales

Scales of CWT. See cwt() for details. Additionally, a prepared_wavelets object is also accepted (see prepareWavelets()).

SNR.Th

SNR (Signal to Noise Ratio) threshold

nearbyPeak

Determine whether to include the nearby small peaks of major peaks. TRUE by default

peakScaleRange

the scale range of the peak. larger than 5 by default.

amp.Th

the minimum required relative amplitude of the peak (ratio to the maximum of CWT coefficients)

minNoiseLevel

the minimum noise level used in computing the SNR

ridgeLength

the minimum highest scale of the peak in 2-D CWT coefficient matrix

peakThr

Minimal absolute intensity (above the baseline) of peaks to be picked. If this value is provided, then the smoothing function signal::sgolayfilt() will be called to estimate the local intensity.(added based on the suggestion and code of Steffen Neumann)

tuneIn

determine whether to tune in the parameter estimation of the detected peaks. If TRUE, peak detection is run again on a segment of the spectrum with more detailed scales. This tuning happens with the default wavelet and settings so it may not be that useful to you if you are using custom wavelets or thresholds.

...

other parameters used by identifyMajorPeaks(). Additionally, fl (filter length, with a default value of 1001) and forder (filter order, with a default value of 2) are set and passed to signal::sgolayfilt() when peakThr is given.

exclude0scaleAmpThresh

When computing the relative amp.Th, if this is set to TRUE, the amp.Th will exclude the zero-th scale from the max(wCoefs). The zero-th scale corresponds to the original signal, that may have a much larger baseline than the wavelet coefficients and can distort the threshold calculation. The default is FALSE to preserve backwards compatibility.

getRidgeParams

A list with parameters for getRidge().

Value

majorPeakInfo

return of identifyMajorPeaks()

ridgeList

return of getRidge()

localMax

return of getLocalMaximumCWT()

wCoefs

2-D CWT coefficient matrix, see cwt() for details.

Author(s)

Pan Du, Simon Lin

References

Du, P., Kibbe, W.A. and Lin, S.M. (2006) Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, 22, 2059-2065.

See Also

cwt(), getLocalMaximumCWT(), getRidge(), identifyMajorPeaks()

Examples

data(exampleMS)

# Detect peaks with prepared wavelets:
prep_wav <- prepareWavelets(length(exampleMS))
SNR.Th <- 3
peakInfo <- peakDetectionCWT(exampleMS, prep_wav, SNR.Th = SNR.Th, exclude0scaleAmpThresh=TRUE)
peakIndex <- peakInfo$majorPeakInfo$peakIndex
plotPeak(exampleMS, peakIndex, main = paste("Identified peaks with SNR >", SNR.Th))

SNR.Th <- 3
peakInfo <- peakDetectionCWT(exampleMS, SNR.Th = SNR.Th)
majorPeakInfo <- peakInfo$majorPeakInfo
peakIndex <- majorPeakInfo$peakIndex
plotPeak(exampleMS, peakIndex, main = paste("Identified peaks with SNR >", SNR.Th))

## In some cases, users may want to add peak filtering based on the absolute peak amplitude
peakInfo <- peakDetectionCWT(exampleMS, SNR.Th = SNR.Th, peakThr = 500)
majorPeakInfo <- peakInfo$majorPeakInfo
peakIndex <- majorPeakInfo$peakIndex
plotPeak(exampleMS, peakIndex, main = paste("Identified peaks with SNR >", SNR.Th))

Plot the local maximum matrix

Description

Plot the local maximum matrix of 2-D CWT coefficients returned by getLocalMaximumCWT()

Usage

plotLocalMax(
  localMax,
  wCoefs = NULL,
  range = c(1, nrow(localMax)),
  colorMap = "RYB",
  main = NULL,
  cex = 3,
  pch = ".",
  ...
)

Arguments

localMax

local maximum matrix of 2-D CWT coefficients returned by getLocalMaximumCWT()

wCoefs

2-D CWT coefficients

range

plot range of m/z index

colorMap

the colormap used in plotting the points

main

parameter of plot()

cex

parameter of plot()

pch

parameter of plot()

...

other parameters of points()

Value

No value is returned; this function is called for its side effects (plot).

Author(s)

Pan Du

See Also

getLocalMaximumCWT()

Examples

data(exampleMS)
scales <- seq(1, 64, 3)
wCoefs <- cwt(exampleMS[5000:11000], scales = scales, wavelet = "mexh")

localMax <- getLocalMaximumCWT(wCoefs)
plotLocalMax(localMax)

Plot the identified peaks over the spectrum

Description

Plot the identified peaks over the spectrum. The identified peaks are returned by peakDetectionCWT() or identifyMajorPeaks()

Usage

plotPeak(
  ms,
  peakIndex = NULL,
  mz = 1:length(ms),
  range = c(min(mz), max(mz)),
  method = c("p", "l"),
  main = NULL,
  log = "",
  ...
)

Arguments

ms

the MS spectrum

peakIndex

m/z indexes of the identified peaks

mz

m/z value correspond to m/z index

range

the plot range of m/z value

method

plot method of the identified peaks. method 'p' plot circles on the peaks; method 'l' add vertical lines over the peaks.

main

parameter of plot()

log

parameter of plot()

...

other parameters of points()

Value

No value is returned; this function is called for its side effects (plot).

Author(s)

Pan Du

See Also

peakDetectionCWT(), identifyMajorPeaks()

Examples

data(exampleMS)
SNR.Th <- 3
peakInfo <- peakDetectionCWT(exampleMS, SNR.Th = SNR.Th)
majorPeakInfo <- peakInfo$majorPeakInfo
peakIndex <- majorPeakInfo$peakIndex
plotPeak(exampleMS, peakIndex, main = paste("Identified peaks with SNR >", SNR.Th))

Plot the ridge list

Description

Plot the ridge list returned by getRidge()

Usage

plotRidgeList(
  ridgeList,
  wCoefs = NULL,
  range = NULL,
  colorMap = "RYB",
  main = NULL,
  pch = ".",
  cex = 3,
  ...
)

Arguments

ridgeList

returned by getRidge()

wCoefs

2-D CWT coefficients

range

plot range of m/z index

colorMap

colorMap to plot the points of local maximum

main

parameter of plot()

pch

parameter of plot()

cex

parameter of plot()

...

other parameters of points()

Value

No value is returned; this function is called for its side effects (plot).

Author(s)

Pan Du

See Also

getRidge()

Examples

data(exampleMS)
scales <- seq(1, 64, 3)
wCoefs <- cwt(exampleMS[5000:11000], scales = scales, wavelet = "mexh")

localMax <- getLocalMaximumCWT(wCoefs)
ridgeList <- getRidge(localMax)
plotRidgeList(ridgeList)

Prepare daughter wavelets for faster CWT

Description

Prepare daughter wavelets for faster CWT

Usage

prepareWavelets(
  mslength,
  scales = c(1, seq(2, 30, 2), seq(32, 64, 4)),
  wavelet = "mexh",
  wavelet_xlimit = 8,
  wavelet_length = 1024L,
  extendLengthScales = TRUE
)

Arguments

mslength

Length of the signal to transform

scales

a vector represents the scales at which to perform CWT. See the Details section. Additionally, a prepared_wavelets object is also accepted (see prepareWavelets()).

wavelet

The wavelet base, Mexican Hat by default. User can provide wavelet Psi(x) as a form of two row matrix. The first row is the x value, and the second row is Psi(x) corresponding to x.

wavelet_xlimit

The mother wavelet will be evaluated between these limits. Ignored if wavelet is a matrix.

wavelet_length

The number of points of the mother wavelet. Ignored if wavelet is a matrix

extendLengthScales

A logical value. If the signal is too short, we may need to pad it to convolve it with larger daughter wavelets. Set this to TRUE to let scales be used to determine the padding length. It's set to FALSE by default to preserve backwards compatibility.

Value

A prepared_wavelets object.

See Also

cwt

Examples

x <- runif(2000)
scales <- c(1, 2, 4, 8)
prep_wavelets <- prepareWavelets(length(x), scales = scales)
wCoefs <- cwt(x, prep_wavelets)

smooth (denoise) the spectrum by DWT (Discrete Wavelet Transform)

Description

Smooth (denoise) the spectrum by DWT (Discrete Wavelet Transform)

Usage

smoothDWT(
  ms,
  nLevel = 6,
  wf = "la8",
  localNoiseTh = seq(1, 0, by = -0.2),
  localWinSize = 500,
  globalNoiseTh = 0.75,
  smoothMethod = c("soft", "hard"),
  method = c("dwt", "modwt")
)

Arguments

ms

a vector representing the mass spectrum

nLevel

the level of DWT decomposition

wf

the name of wavelet for DWT

localNoiseTh

local noise level threshold

localWinSize

local window size for estimate local noise threshold

globalNoiseTh

global noise level threshold

smoothMethod

the method used for denoising. 'hard' means keeping the dwt coefficients higher than the threshold unchanged; "soft" means the dwt coefficients higher than the threshold were subtracted by the threshold.

method

'dwt' or 'modwt' used for decomposition

Value

return the smoothed mass spectrum with the 'detail' component of DWT as an attribute 'detail'.

Author(s)

Pan Du


Tune in the peak information: peak position and peak scale

Description

Based on the identified peak position, more precise estimation of the peak information, i.e., peak position and peak scale, can be got by this function. The basic idea is to cut the segment of spectrum near the identified peaks, and then do similar procedures as peakDetectionCWT(), but with more detailed scales around the estimated peak scale.

Usage

tuneInPeakInfo(
  ms,
  majorPeakInfo = NULL,
  peakIndex = NULL,
  peakScale = NULL,
  maxScale = 128,
  ...
)

Arguments

ms

the mass spectrometry spectrum

majorPeakInfo

return of identifyMajorPeaks()

peakIndex

the m/z index of the identified peaks

peakScale

the scales of the identified peaks

maxScale

the maximum scale allowed for the peak

...

other parameters of used by getLocalMaximumCWT(), getRidge(), identifyMajorPeaks()

Details

The majorPeakInfo or peakIndex and peakScale must be provided.

Value

peakCenterIndex

the updated peak center m/z index

peakScale

the updated peak scale

peakValue

the corresponding peak value

Author(s)

Pan Du

References

Du, P., Kibbe, W.A. and Lin, S.M. (2006) Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, 22, 2059-2065.

See Also

peakDetectionCWT()

Examples

data(exampleMS)
SNR.Th <- 3
peakInfo <- peakDetectionCWT(exampleMS, SNR.Th = SNR.Th)
majorPeakInfo <- peakInfo$majorPeakInfo
betterPeakInfo <- tuneInPeakInfo(exampleMS, majorPeakInfo)
plot(500:length(exampleMS), exampleMS[500:length(exampleMS)], type = "l", log = "x")
abline(v = betterPeakInfo$peakCenterIndex, col = "red")