Spatially Automatic Denoising for Imaging Mass Spectrometry Toolkit
Apply the results of a peaks filter.
Return a binary mask generated applying k-means clustering on first 10...
Return a binary mask generated applying k-means clustering on peaks in...
Binarize MS image using Otsu's thresholding.
Return a binary mask generated applying a supervised classifier on pea...
Load the example MALDI-MSI data.
Apply morphological closing to binary image.
Filter based on the minimum number of connected pixels in the ROI.
Generate a peak filter object.
Performs the peak selection based on complete spatial randomness test.
Return the peaks intensity matrix.
Return the m/z vector.
Returns the geometrical shape of MSI dataset
Gini index.
Reference similarity based peak selection.
Invert the colors of an MS image.
ms.image-class definition.
msi.dataset-class S4 class definition containing the information about...
Constructor for msi.dataset-class objects.
Constructor for ms.image-class objects.
Normalized mutual information (NMI).
Normalize the peaks intensities.
Generates an msImage representing the number of detected peaks per pix...
Load the example DESI-MSI data.
Generates an RGB msImage representing the first 3 principal components...
Visualize an MS image. plot extends the generic function to ms.image...
Calculate the binary reference image using k-means clustering. K-Means...
Calculate the binary reference image using k-means clustering with mul...
Calculate the binary reference image using Otsu's thresholding.
Calculate the binary reference image using linear SVM trained on manua...
refImageContinuous returns the reference image, calculated using the...
Remove binary ROI objects smaller than user-defined number of pixels
Pixel scatteredness ratio.
Apply Gaussian smoothing to an MS image.
Spatial chaos measure.
Test for the presence of split peaks.
Structural similarity index (SSIM).
Generates an msImage representing pixels total-ion-counts. This image ...
Variance stabilizing transformation.
Set of tools for peak filtering of mass spectrometry imaging data based on spatial distribution of signal. Given a region-of-interest, representing the spatial region where the informative signal is expected to be localized, a series of filters determine which peak signals are characterized by an implausible spatial distribution. The filters reduce the dataset dimension and increase its information vs noise ratio, improving the quality of the unsupervised analysis results, reducing data dimension and simplifying the chemical interpretation. The methods are described in Inglese P. et al (2019) <doi:10.1093/bioinformatics/bty622>.