SPatially aUTomatic deNoising for Ims toolKit
A set of tools for the peak filtering of mass spectrometry
imaging data (MSI or IMS) based on spatial distribution of signal. Given a
region-of-interest (ROI), 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 dimensionality and increase its information vs noise ratio,
improving the quality of the unsupervised analysis results, reducing data
dimensionality and simplifying the chemical interpretation.
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog,
and this project adheres to Semantic Versioning.
[1.1.1] - 2018-10-25
- Fixed bug in the global reference filter that won't allow the calculation of
the similarity measures when constant columns are present in the intensity matrix.
[1.1] - 2018-10-19
- New ROI detection using linear SVM. The user must select representative regions
of the off-sample and sample-related areas. Then, a linear SVM performs the
segmentation of the data, generating the ROI.
- New ROI detection using k-means with a larger number of clusters than 2. This
allows a finer detection of the sample-related region.
- Now the covariate image for the Kolmogorov-Smirnov test can be passed as argument
of the function 'CSRPeaksFilter' (see doc).
- New functions 'addBorderImage' and 'remBorderImage' to add or remove a border
of N pixels from an MS image.
- Fixed the SSIM function (it does not require scaling)
- Fixed typos in the help
[188.8.131.52] - 2018-10-08
- Removed dependency from 'autothresholdr' package. Now Otsu is performed using
the function threshold(x, 'auto') from 'imager'.
[1.0.4] - 2018-10-06
- Fixed a bug in the function .match.mz.array.
- Improved the comments in the function .match.mz.array.