Performs the peak selection based on complete spatial randomness test.

Description

CSRPeaksFilter returns the significance for the null hypothesis that the spatial distribution of the peak intensities follow a random pattern. A significant p-value (q-values can be returned after applying multiple testing correction) allows to reject the hypothesis that the spatial distribution of a peak signal is random. The tests are performed using the functions available in the statspat R package.

Usage

CSRPeaksFilter(
  msiData,
  method = "ClarkEvans",
  covariateImage = NULL,
  adjMethod = "bonferroni",
  returnQvalues = TRUE,
  plotCovariate = FALSE,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

Value

List of the p-values and adjusted p-values for the CSR test.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Baddeley, A., & Turner, R. (2005). Spatstat: an R package for analyzing spatial point patterns. Journal of statistical software, 12(6), 1-42.

Clark, P.J. and Evans, F.C. (1954) Distance to nearest neighbour as a measure of spatial relationships in populations. Ecology 35, 445–453.

Berman, M. (1986) Testing for spatial association between a point process and another stochastic process. Applied Statistics 35, 54–62.

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(16000), 400, 40)
X[X < 0] <- 0

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 900, by = (900 - 600) / 39)

## Read the image size
imSize <- c(20, 20)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Calculate the p-values using the Clark Evans test, then apply Benjamini-
## Hochberg correction.
csr <- CSRPeaksFilter(
  msiData = msiX, method = "ClarkEvans",
  calculateCovariate = FALSE, adjMethod = "BH"
)

## Print selected peaks
print(csr$q.value)

## Create a new filter selecting corrected p-values < 0.001
selIdx <- which(csr$q.value < 0.001)
csrFilter <- createPeaksFilter(selIdx)

Normalized mutual information (NMI).

Description

NMI returns the normalized mutual information between two ms.image objects. The normalized mutual information is calculated as the mutual information divided by square-root of the product of the entropies. This function makes use of the functions available in infotheo R package.

Usage

NMI(x, y, numBins = 256)

Arguments

Value

NMI value between 0 and 1.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Meyer, P. E. (2009). Infotheo: information-theoretic measures. R package. Version, 1(0).

Generates an RGB msImage representing the first 3 principal components. This image can be used to qualitatively evaluate the spatial heterogeneity of the sample.

Description

Generates an RGB msImage representing the first 3 principal components. This image can be used to qualitatively evaluate the spatial heterogeneity of the sample.

Usage

## S4 method for signature 'msi.dataset'
PCAImage(object, alignToSample = TRUE, seed = NULL)

Arguments

Value

RGB raster representing the first 3 principal components (see msImage).

Structural similarity index (SSIM).

Description

ssim returns the value of SSIM between two vectors representing the color intensities of two images.

Usage

SSIM(x, y, numBreaks = 256)

Arguments

Details

SSIM is an image quality measure, given a reference considered as noise-less image. It can be also used as a perceived similarity measure between images. The images are converted by default in 8bit.

Value

value of SSIM defined between 0 and 1.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.

Apply the results of a peaks filter.

Description

applyPeaksFilter select the peaks returned by a peak filter. Custom filters can be created passing a named array of selected peak indices to createPeaksFilter. Names correspond to the m/z values of the selected peaks and must coincide with those of the MS dataset.

Usage

## S4 method for signature 'msi.dataset'
applyPeaksFilter(object, peakFilter)

Arguments

Value

msi.dataset-class object with only selected peaks.

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(16000), 400, 40)
X[X < 0] <- 0

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 900, by = (900 - 600) / 39)

## Read the image size
imSize <- c(20, 20)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Calculate the p-values using the Clark Evans test, then apply Benjamini-
## Hochberg correction.
csr <- CSRPeaksFilter(
  msiData = msiX, method = "ClarkEvans",
  calculateCovariate = FALSE, adjMethod = "BH"
)

## Print selected peaks
print(csr$q.value)

## Create a new filter selecting corrected p-values < 0.001
selIdx <- which(csr$q.value < 0.001)
csrFilter <- createPeaksFilter(selIdx)

Return a binary mask generated applying k-means clustering on first 10 principal components of peaks intensities.

Description

Return a binary mask generated applying k-means clustering on first 10 principal components of peaks intensities.

Usage

## S4 method for signature 'msi.dataset'
binKmeans(object, ref = "detected", invert = FALSE, npcs = 10)

Arguments

Value

ms.image-class object representing the binary mask image.

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(5, 4)
x <- matrix(rnorm(sz[1] * sz[2] * 20), sz[1] * sz[2], 20)
x[x < 0] <- 0
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Generate binary mask by applying k-means on the entire dataset
roiImg <- refImageBinaryKmeans(msiX, npcs = 3)

## Plot the mask
# plot(roiImg)

Return a binary mask generated applying k-means clustering on peaks intensities. A finer segmentation is obtained by using a larger number of clusters than 2. The off-sample clusters are merged looking at the most frequent labels in the image corners. The lookup areas are defined by the kernel size.

Description

Return a binary mask generated applying k-means clustering on peaks intensities. A finer segmentation is obtained by using a larger number of clusters than 2. The off-sample clusters are merged looking at the most frequent labels in the image corners. The lookup areas are defined by the kernel size.

Usage

## S4 method for signature 'msi.dataset'
binKmeans2(
  object,
  mzQuery = numeric(),
  useFullMZ = TRUE,
  mzTolerance = Inf,
  numClusters = 4,
  kernelSize = c(3, 3, 3, 3),
  numCores = 1,
  verbose = TRUE
)

Arguments

Value

ms.image-class object representing the binary mask image.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

Binarize MS image using Otsu's thresholding.

Description

Binarize MS image using Otsu's thresholding.

Usage

## S4 method for signature 'ms.image'
binOtsu(object)

Arguments

Value

ms.image-class object with binary intensities.

Examples

## Load package
library("SPUTNIK")

## Create ms.image-class object
msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)

## Generate binary image
binIm <- refImageBinaryOtsu(msIm)

Return a binary mask generated applying a supervised classifier on peaks intensities using manually selected regions corresponding to off-sample and sample-related areas.

Description

Return a binary mask generated applying a supervised classifier on peaks intensities using manually selected regions corresponding to off-sample and sample-related areas.

Usage

## S4 method for signature 'msi.dataset'
binSupervised(
  object,
  refImage,
  mzQuery = numeric(),
  mzTolerance = Inf,
  useFullMZ = TRUE,
  method = "svm",
  verbose = TRUE
)

Arguments

Value

ms.image-class object representing the binary mask image.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

Load the example MALDI-MSI data.

Description

Loads a single mouse urinary bladder MALDI mass spectrometry imaging dataset acquired in positive ionization mode using Thermo qExactive Orbitrap. The dataset is available at "https://raw.github.com/paoloinglese/SPUTNIKexamples/master/data/bladder_maldi_prepr_MALDIquant.RData" The dataset is loaded in the R environment under the variable name maldiData.

Usage

bladderMALDIRompp2010(verbose = TRUE)

Arguments

Value

desiData MS intensity matrix. Rows represent pixels, columns represent matched peaks.

References

Rompp, A., Guenther, S., Schober, Y., Schulz, O., Takats, Z., Kummer, W., & Spengler, B. (2010). Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging. Angewandte chemie international edition, 49(22), 3834-3838.

Apply morphological closing to binary image.

Description

Apply morphological closing to binary image.

Usage

## S4 method for signature 'ms.image'
closeImage(object, kern.size = 5)

Arguments

Value

ms.image-class object after closing.

Examples

## Load package
library("SPUTNIK")

## Create ms.image-class object
msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)

## Generate binary image
msImBin <- refImageBinaryOtsu(msIm)

## Apply the morphological closing
msImClosed <- closeImage(msImBin, kern.size = 3)

Filter based on the minimum number of connected pixels in the ROI.

Description

countPixelsFilter selects peaks which signals are localized in regions consisting of a minimum number of connected pixels in the ROI.

Usage

countPixelsFilter(
  msiData,
  roiImage,
  minNumPixels = 9,
  smoothPeakImage = FALSE,
  smoothSigma = 2,
  closePeakImage = FALSE,
  closeKernSize = 5,
  aggressive = 0,
  verbose = TRUE
)

Arguments

Details

Count filter tries to determine and remove peaks which signal is scattered in a region unrelated with the expected ROI. A minimum number of connected pixels in the ROI is used to trigger the filter. This value should be carefully set equal to the geometrical size of the smallest expected informative sub-region. Each peak image is binarized using Otsu's thresholding and the connected components are extracted. The filter selects those peaks that show, within the ROI, at least one connected component of size larger or equal to minNumPixels. The level of aggressiveness, associated with increasing values of the parameter aggressive, determines whether the size of the connected components within the ROI should be compared with that of the connected components localized outside the ROI. If aggressive = 0, no comparison is performed. If aggressive = 1, the filter checks whether the max size of the connected components localized outside the ROI is smaller or equal to the maximum size of the connected components inside the ROI. If aggressive = 2, a stricter filter checks whether the maximum size of the connected components localized outside the ROI is smaller than minNumPixels. Different aggressiveness levels can produce completely different results, depending on the nature of the analyzed dataset.

Value

peak.filter object. See applyPeaksFilter-msi.dataset-method.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

See Also

applyPeaksFilter

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(16000), 400, 40)
X[X < 0] <- 0

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 900, by = (900 - 600) / 39)

## Read the image size
imSize <- c(20, 20)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Extract the ROI using k-means

refImg <- refImageContinuous(msiX, method = "sum")
roiImg <- refImageBinaryOtsu(refImg)

## Perform count pixels filtering
count.sel <- countPixelsFilter(
  msiData = msiX, roiImage = roiImg,
  minNumPixels = 4, aggressive = 1
)

## Apply the filter
msiX <- applyPeaksFilter(msiX, count.sel)

## Print new dimensions
print(dim(getIntensityMat(msiX)))

Generate a peak filter object.

Description

createPeaksFilter returns a peak.filter object.

Usage

createPeaksFilter(peaksIndices)

Arguments

Details

Function to create a custom peak that can be subsequently applied using the function applyPeaksFilter-msi.dataset-method. Argument of the function is the index vector of the selected peaks named with their m/z values. The m/z values are used to check whether the indices correspond to the common m/z values in the msi.dataset-class object.

Value

peak.filter object.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

See Also

applyPeaksFilter-msi.dataset-method

Examples

library("SPUTNIK")
mz <- seq(100, 195, 5)
mzIdx <- sample(100, 20)
names(mzIdx) <- mz
peaksFilter <- createPeaksFilter(mzIdx)

Return the peaks intensity matrix.

Description

Return the peaks intensity matrix.

Usage

## S4 method for signature 'msi.dataset'
getIntensityMat(object)

Arguments

Value

peaks intensity matrix. Rows represent pixels, and columns represent peaks.

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(5, 4)
x <- matrix(rnorm(sz[1] * sz[2] * 20), sz[1] * sz[2], 20)
x[x < 0] <- 0
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Get m/z vector
mz <- getMZ(msiX)

## Get intensity matrix
X <- getIntensityMat(msiX)

## Get image size
sz <- getShapeMSI(msiX)

Return the m/z vector.

Description

Return the m/z vector.

Usage

## S4 method for signature 'msi.dataset'
getMZ(object)

Arguments

Value

vector containing the m/z values.

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(5, 4)
x <- matrix(rnorm(sz[1] * sz[2] * 20), sz[1] * sz[2], 20)
x[x < 0] <- 0
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Get m/z vector
mz <- getMZ(msiX)

## Get intensity matrix
X <- getIntensityMat(msiX)

## Get image size
sz <- getShapeMSI(msiX)

Returns the geometrical shape of MSI dataset

Description

Returns the geometrical shape of MSI dataset

Usage

## S4 method for signature 'msi.dataset'
getShapeMSI(object)

Arguments

Value

number of rows ans number of columns of the MS image.

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(5, 4)
x <- matrix(rnorm(sz[1] * sz[2] * 20), sz[1] * sz[2], 20)
x[x < 0] <- 0
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Get m/z vector
mz <- getMZ(msiX)

## Get intensity matrix
X <- getIntensityMat(msiX)

## Get image size
sz <- getShapeMSI(msiX)

Gini index.

Description

gini.index returns the Gini index of the ion intensity vector as a measure of its sparseness. The intensity vector is first quantized in N levels (default = 256). A value close to 1 represents a high level of sparseness, a value close to 0 represents a low level of sparseness.

Usage

gini.index(x, levels = 256)

Arguments

Value

A value between 0 and 1. High levels of signal sparsity are associated with values close to 1, whereas low levels of signal sparsity are associated with values close to 0.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723-4741.

See Also

scatter.ratio spatial.chaos

Examples

## Load package
library("SPUTNIK")

## Image
im <- matrix(rnorm(100), 10, 10)
im[im < 0] <- 0

## Spatial chaos
sc <- spatial.chaos(im, levels = 30, morph = TRUE)
stopifnot(sc <= 1)

## Gini index
gi <- gini.index(im, levels = 16)
stopifnot(gi >= -1 && gi <= 1)

## Scatter ratio
sr <- scatter.ratio(im)
stopifnot(sr <= 1)

Reference similarity based peak selection.

Description

globalPeaksFilter returns a list of peaks selected by their similarity with a reference image.

Usage

globalPeaksFilter(
  msiData,
  referenceImage,
  method = "pearson",
  threshold = NULL,
  cores = 1,
  verbose = TRUE
)

Arguments

Details

A filter based on the similarity between the peak signals and a reference signal. The reference signal, passed as an ms.image-class object. Both continuous and binary references can be passed. The filter then calculates the similarity between the peaks signal and the reference image and select those with a similarity larger than threshold. Multiple measures are available, correlation, structural similarity index measure (SSIM), and normalized mutual information (NMI). Since correlation can assume values in [-1, 1], also NMI are scaled in [-1, 1].

Value

peak.filter object. See applyPeaksFilter.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.

Meyer, P. E. (2009). Infotheo: information-theoretic measures. R package. Version, 1(0).

See Also

countPixelsFilter applyPeaksFilter-msi.dataset-method

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(16000), 400, 40)
X[X < 0] <- 0

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 900, by = (900 - 600) / 39)

## Read the image size
imSize <- c(20, 20)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Generate the reference image and the ROI mask
refImg <- refImageContinuous(msiX, method = "sum")

## Perform global peaks filter
glob.peaks <- globalPeaksFilter(
  msiData = msiX, referenceImage = refImg,
  method = "pearson", threshold = 0
)

## Apply the filter
msiX <- applyPeaksFilter(msiX, glob.peaks)

## Print the new dimensions
print(dim(getIntensityMat(msiX)))

Invert the colors of an MS image.

Description

Invert the colors of an MS image.

Usage

## S4 method for signature 'ms.image'
invertImage(object)

Arguments

Value

ms.image-class object after inverting colors.

Examples

## Load package
library("SPUTNIK")

## Create ms.image-class object
msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)

## Invert the colors
msImInverted <- invertImage(msIm)

ms.image-class definition.

Description

ms.image-class definition.

Slots

values

numeric 2-D matrix representing the pixel intensity values.

name

string. Image name used for plotting.

scaled

logical. Whether the pixels intensities have been scaled in [0, 1] or not.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

Constructor for ms.image-class objects.

Description

Constructor for ms.image-class objects.

Usage

msImage(values, name = character(), scale = TRUE)

Arguments

Value

ms.image-class object.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

Examples

## Load package
library("SPUTNIK")

## MS image
imShape <- c(40, 50)
matIm <- matrix(rnorm(200), imShape[1], imShape[2])
im <- msImage(values = matIm, name = "random", scale = TRUE)

msi.dataset-class S4 class definition containing the information about the mass spectrometry imaging dataset.

Description

msi.dataset-class S4 class definition containing the information about the mass spectrometry imaging dataset.

Slots

matrix

the peaks intensity matrix. Rows represent pixels, and columns represent peaks.

mz

vector of matched m/z values.

nrow

geometrical shape (number of rows) of image.

ncol

geometrical shape (number of columns) of image.

norm

normalization method.

normoffset

numeric offset used for the normalization.

vartr

variance stabilizing transformation.

vartroffset

numeric offset used for the variance stabilizing transformation.

numdetected

msImage of number of detected peaks.

totalioncount

msImage of total-ion-count per pixel.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

Constructor for msi.dataset-class objects.

Description

msiDataset returns a msi.dataset-class object. It contains information about the matched peaks intensities, the geometrical dimensions of the mass spectral image, and the common m/z values.

Usage

msiDataset(values, mz, rsize, csize, verbose = TRUE)

Arguments

Details

Function used to construct the main object msi.dataset-class. This object contains all the information about peaks intensities (intensity matrix), the geometrical shape of the image (rows, columns), and the vector of the common m/z values, generated during the peak matching process.

Value

msi.dataset-class object.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(5, 4)
numIons <- 20
x <- matrix(rnorm(prod(sz) * numIons), prod(sz), numIons)
mz <- sort(sample(100, numIons))
msiX <- msiDataset(x, mz, sz[1], sz[2])

Normalize the peaks intensities.

Description

Normalize the peaks intensities.

Usage

## S4 method for signature 'msi.dataset'
normIntensity(object, method = "median", peaksInd = NULL, offsetZero = 0)

Arguments

Details

The valid values for method are:

• "median": median of spectrum intensities is scaled to one.

• "PQN":

  1. apply "TIC" normalization

  2. calculate the median reference spectrum (after removing the zeros)

  3. calculate the quotients of peaks intensities and reference

  4. calculate the median of quotients for each peak (after removing the zeros)

  5. divide all the peak intensities by the median of quotients

• "TIC": total ion current normalization assign the sum of the peaks intensities to one.

• "TMM": trimmed mean of M-values (TMM with zero pairing). Called TMMwzp in edgeR.

• "upperQuartile": spectra are scaled by their 3rd quartile.

Value

object msi.dataset-class object, with normalized peaks intensities.

When using TIC scaling, if zeros are present in the matrix, a positive offset must be added to all the peak intensities through the parameter offsetZero. This is necessary for applying the CLR transformation. TIC scaling transforms the spectra into compositional data; in this case the CLR transformation must be applied through the varTransform function.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

F. Dieterle, A. Ross, G. Schlotterbeck, and Hans Senn. 2006. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry 78(13): 4281-4290.

Robinson MD, Oshlack A (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11, R25.

See Also

msi.dataset-class

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(40, 40)
x <- matrix(rnorm(sz[1] * sz[2] * 20) * 1000, sz[1] * sz[2], 20)
x[x < 0] <- 0 # MS data is positive
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Normalize and log-transform
msiX <- normIntensity(msiX, "median")
msiX <- varTransform(msiX, "log")

## Create the msi.dataset-class object
sz <- c(40, 40)
x <- matrix(rnorm(sz[1] * sz[2] * 20) * 1000, sz[1] * sz[2], 20)
x[x < 0] <- 0 # MS data is positive
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Normalize using PQN
msiX <- normIntensity(msiX, "PQN")

Generates an msImage representing the number of detected peaks per pixel. This image can be used to qualitatively evaluate the spatial heterogeneity of the sample.

Description

Generates an msImage representing the number of detected peaks per pixel. This image can be used to qualitatively evaluate the spatial heterogeneity of the sample.

Usage

## S4 method for signature 'msi.dataset'
numDetectedMSI(object)

Arguments

Value

ms.image-class object representing the detected ions per pixel.

Load the example DESI-MSI data.

Description

Loads a single human ovarian cancer DESI mass spectrometry imaging dataset acquired in negative ionization mode using Waters XEVO-GS2 qToF. The dataset is available at "https://raw.github.com/paoloinglese/SPUTNIKexamples/master/data/ovarian_xevo_prepr_MALDIquant.RData" The dataset is loaded in the R environment under the variable name maldiData.

Usage

ovarianDESIDoria2016(verbose = TRUE)

Arguments

Value

maldiData MS intensity matrix. Rows represent pixels, columns represent matched peaks.

References

Doria, M. L., McKenzie, J. S., Mroz, A., Phelps, D. L., Speller, A., Rosini, F., ... & Ghaem-Maghami, S. (2016). Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging. Scientific reports, 6, 39219.

Visualize an MS image. plot extends the generic function to ms.image-class objects.

Description

Visualize an MS image. plot extends the generic function to ms.image-class objects.

Usage

## S4 method for signature 'ms.image,missing'
plot(x, palette = "inferno")

Arguments

Value

a ggplot2 plot.

Examples

## Load package


  library("SPUTNIK")

  ## Create ms.image-class object
  msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)
  
  ## Plot the image
  plot(msIm)

Calculate the binary reference image using k-means clustering. K-Means is run on the first 'npcs' principal components to speed up the calculations.

Description

Calculate the binary reference image using k-means clustering. K-Means is run on the first 'npcs' principal components to speed up the calculations.

Usage

refImageBinaryKmeans(
  dataset,
  npcs = 10,
  alignTo = "detected",
  invertAligned = FALSE
)

Arguments

Value

ms.image-class object with binary intensities.

Calculate the binary reference image using k-means clustering with multi-cluster merging. K-means is run on the first 'npcs' principal components to speed up the calculations.

Description

Calculate the binary reference image using k-means clustering with multi-cluster merging. K-means is run on the first 'npcs' principal components to speed up the calculations.

Usage

refImageBinaryKmeansMulti(
  dataset,
  npcs = 10,
  mzQuery = numeric(),
  mzTolerance = Inf,
  useFullMZ = TRUE,
  numClusters = 4,
  kernelSize = 5,
  cores = 1,
  verbose = TRUE
)

Arguments

Value

ms.image-class object with binary intensities.

Calculate the binary reference image using Otsu's thresholding.

Description

Calculate the binary reference image using Otsu's thresholding.

Usage

refImageBinaryOtsu(image)

Arguments

Value

ms.image-class object with binary intensities. #'

Calculate the binary reference image using linear SVM trained on manually selected pixels.

Description

Calculate the binary reference image using linear SVM trained on manually selected pixels.

Usage

refImageBinarySVM(
  dataset,
  mzQueryRef = numeric(),
  mzTolerance = Inf,
  useFullMZ = TRUE
)

Arguments

Value

ms.image-class object with binary intensities.

refImageContinuous returns the reference image, calculated using the method. This image represents the basic measure for the filters in SPUTNIK.

Description

refImageContinuous returns the reference image, calculated using the method. This image represents the basic measure for the filters in SPUTNIK.

Usage

refImageContinuous(
  msiData,
  method = "sum",
  mzQueryRef = numeric(),
  mzTolerance = Inf,
  useFullMZRef = TRUE,
  doSmooth = FALSE,
  smoothSigma = 2,
  alignTo = "detected",
  invertAligned = FALSE,
  verbose = TRUE
)

Arguments

Details

Function to extract the continuous reference image from a msi.dataset-class object. The continuous reference image represents the spatial location of the sample. By default, it is aligned with either the image representing the number of detected peaks, or the total-ion-count in all pixels. It is expected to be higher in the region occupied by the sample (positive correlation with the mask representing the real sample pixels). In some cases, the alignment images can have higher values in the pixels outside of the sample. In these circumstances, the argument invertAligned should be set to TRUE.

Value

A continuous valued reference image (see msImage).

See Also

msiDataset, msImage

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(200), 20, 40)
X[X < 0] <- 0

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 900, by = (900 - 600) / 39)

## Read the image size
imSize <- c(5, 4)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Calculate the reference and ROI images from the ms.dataset-class object msiX.
## The reference is calculated as the first principal component scores scaled
## in [0, 1]; the binary ROI is calculated applying k-means on the entire dataset.
## Use only m/z values in the range of [700, 900]. The interval extremal values
## are matched within a tolerance of 50 ppm.

refImg <- refImageContinuous(msiX, method = "sum")
roiImg <- refImageBinaryOtsu(refImg)

## Plot the reference and region of interest ROI
## plot(ref.roi$Reference)
## plot(ref.roi$ROI)

Remove binary ROI objects smaller than user-defined number of pixels

Description

Remove binary ROI objects smaller than user-defined number of pixels

Usage

## S4 method for signature 'ms.image'
removeSmallObjects(object, threshold = 5, border = 3)

Arguments

Value

ms.image-class object after filtering.

Examples

library(SPUTNIK)

fakeBinImage <- matrix(0, 100, 100)
fakeBinImage[sample(prod(dim(fakeBinImage)), 2000)] <- 1

fakeBinMsImage <- msImage(values = fakeBinImage, name = "ROI", scale = FALSE)

# Remove the objects with a number of connected pixels smaller than 5
fakeBinMsImage <- removeSmallObjects(fakeBinMsImage, threshold = 5)

Pixel scatteredness ratio.

Description

scatter.ratio returns a measure of image scatteredness represented by the ratio between the number of connected components and the total number of non-zero pixels. The number of connected components is calculated from the binarized image using Otsu's method.

Usage

scatter.ratio(im)

Arguments

Value

calculated scatter ratio.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.

See Also

gini.index spatial.chaos

Examples

## Load package
library("SPUTNIK")

## Image
im <- matrix(rnorm(100), 10, 10)
im[im < 0] <- 0

## Spatial chaos
sc <- spatial.chaos(im, levels = 30, morph = TRUE)
stopifnot(sc <= 1)

## Gini index
gi <- gini.index(im, levels = 16)
stopifnot(gi >= -1 && gi <= 1)

## Scatter ratio
sr <- scatter.ratio(im)
stopifnot(sr <= 1)

Apply Gaussian smoothing to an MS image.

Description

Apply Gaussian smoothing to an MS image.

Usage

## S4 method for signature 'ms.image'
smoothImage(object, sigma = 2)

Arguments

Value

ms.image-class smoothed msImage.

Examples

## Load package
library("SPUTNIK")

## Create ms.image-class object
msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)

## Smooth the image colors
msImSmoothed <- smoothImage(msIm, sigma = 5)

Spatial chaos measure.

Description

spatial.chaos returns the 'spatial chaos' randomness measure for imaging data.

Usage

spatial.chaos(im, levels = 30, morph = TRUE)

Arguments

Value

A value between 0 and 1. A value close to 1 represents a high level of spatial scatteredness, a value close to 0 represents a less level of spatial scatteredness. Maximum possible value is 1 - 1 / (# histogram bins)

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Palmer, A., Phapale, P., Chernyavsky, I., Lavigne, R., Fay, D., Tarasov, A., ... & Becker, M. (2017). FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nature methods, 14(1), 57.

See Also

gini.index scatter.ratio

Examples

## Load package
library("SPUTNIK")

## Image
im <- matrix(rnorm(100), 10, 10)
im[im < 0] <- 0

## Spatial chaos
sc <- spatial.chaos(im, levels = 30, morph = TRUE)
stopifnot(sc <= 1)

## Gini index
gi <- gini.index(im, levels = 16)
stopifnot(gi >= -1 && gi <= 1)

## Scatter ratio
sr <- scatter.ratio(im)
stopifnot(sr <= 1)

Test for the presence of split peaks.

Description

splitPeaksFilter returns a list of estimated split peak indices. Each element of the list contains an array of the original peak indices that can be merged. The name of the list element is the new m/z value associated with the merged peaks.

Usage

splitPeaksFilter(
  msiData,
  mzTolerance = 5,
  sharedPixelsRatio = 0,
  sparseness = "scatter.ratio",
  threshold = 0.5,
  returnDetails = TRUE,
  verbose = TRUE
)

Arguments

Details

splitPeaksFilter determines whether close peaks represent the same signal. This estimation is based on multiple conditions:

1. peaks m/z values should be closer than mzTolerance PPM

2. at least one of the peak images should be structured, accordingly to the sparseness measure. The threshold determines whether the pixel images are structured or not. The possible measures are:

   • "scatter.ratio": ratio between the number of non-zero pixels and the image size after binarization using Otsu's thresholding. A value close to 0 is associated with a more structured image, whereas a value close to 1 is associated with a less structured image. A suggested parameter of threshold = 0.5 represents the maximum value for this measure for a structured image. Minimum possible value is 1 / ( # non-zero pixels ).

   • "spatial.chaos": similar to the scatter ratio taking into account of the color histogram. A value close to 1 represents a structured image, whereas a value close to 0 represents a more scattered image. A suggested parameter of threshold = 0.8 represents the minimum value for this measure for a structured image. Maximum possible value is 1 - 1 / ( # histogram bins ). Here, we use the default number of bins equal to 30.

   • "gini.index": Gini index measures the image sparsity. A value close to 1 is associated with a sparse image whereas a value close to 0 is associated with a more uniform image. A suggested value of threshold = 0.9 represents the maximum value of this measure for a structured image.

3. the merged peaks image should be more structured than the single peak images, accordingly to the selected sparseness.

Value

peak.filter object. See applyPeaksFilter.

Author(s)

Paolo Inglese p.inglese14@imperial.ac.uk

References

Palmer, A., Phapale, P., Chernyavsky, I., Lavigne, R., Fay, D., Tarasov, A., ... & Becker, M. (2017). FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nature methods, 14(1), 57.

Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723-4741.

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(200), 20, 40)
X[X < 0] <- 0

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 601, by = (601 - 600) / 39)

## Read the image size
imSize <- c(5, 4)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Determine split peaks
sp.filter <- splitPeaksFilter(
  msiData = msiX, mzTolerance = 50,
  sharedPixelsRatio = 0,
  sparseness = "spatial.chaos", threshold = 0.5
)

Generates an msImage representing pixels total-ion-counts. This image can be used to qualitatively evaluate the spatial heterogeneity of the sample.

Description

Generates an msImage representing pixels total-ion-counts. This image can be used to qualitatively evaluate the spatial heterogeneity of the sample.

Usage

## S4 method for signature 'msi.dataset'
totalIonCountMSI(object)

Arguments

Value

ms.image-class object representing the total ion counts.

Variance stabilizing transformation.

Description

varTransform transforms the MS intensities in order to reduce heteroscedasticity.

Usage

## S4 method for signature 'msi.dataset'
varTransform(object, method = "log", offsetZero = 1)

Arguments

Value

msi.dataset-class object with transformed peaks intensities.

Examples

## Load package
library("SPUTNIK")

## Create the msi.dataset-class object
sz <- c(40, 40)
x <- matrix(rnorm(sz[1] * sz[2] * 20) * 1000, sz[1] * sz[2], 20)
x[x < 0] <- 0 # MS data is positive
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Normalize and log-transform
msiX <- normIntensity(msiX, "median")
msiX <- varTransform(msiX, "log")

## Create the msi.dataset-class object
sz <- c(40, 40)
x <- matrix(rnorm(sz[1] * sz[2] * 20) * 1000, sz[1] * sz[2], 20)
x[x < 0] <- 0 # MS data is positive
mz <- sort(sample(100, ncol(x)))
msiX <- msiDataset(x, mz, sz[1], sz[2])

## Normalize using PQN
msiX <- normIntensity(msiX, "PQN")