Companion code for the publication:
Inglese, P., Huang, H., Wu, V., Lewis, M.R. and Takats, Z., 2021. Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions. bioRxiv.
https://www.biorxiv.org/content/10.1101/2021.03.29.437482v2
NOTE: this code is related to the latest version of the preprint.
joblib>=1.1.0
KDEpy>=1.1.0
matplotlib>=3.5.1
numpy>=1.22.2
pandas>=1.4.1
Pillow>=9.0.1
pygam>=0.8.0
pyimzML>=1.4.1
PyQt5>=5.15.6
scikit_image>=0.19.1
scikit_learn>=1.0.2
scipy>=1.8.0
statsmodels>=0.13.2
tqdm>=4.62.3
opencv==4.5.0
python select_roi.py
The centroided imzML file is loaded using the File -> Open raw peaks ...
button (shortcut = CTRL + O). Once the reference image is displayed, the user
can start annotating with the selected label (top right checkbox), by drawing a
closed contour using the mouse.
In case of mistakes during the drawing, the user can delete the current curve by
pressing the Delete selection button (B).
Once the region is drawn, the user can confirm by pressing the Add selection
button (A). The image is updated showing the currently annotated pixels.
When enough pixels are annotated with both labels, the image can be segmented
pressing the Process... button (E).
The current segmentation mask is saved in a CSV file in the same folder of the
imzML file by pressing the Save... button (F). All connected regions smaller
than the selected value in Smallest ROI size: (D) are assigned to the
background.
The current annotations or segmentation can be reset by pressing the Reset
button (C).
The RGB colors of the image can be controlled through the sliders below the
image.
usage: desi_recalibration.py [-h]
[--db database]
--analyzer {tof,orbitrap}
--ion-mode {pos,neg}
[--search-tol SEARCH_TOL]
[--kde-bw KDE_BW]
[--degree DEGREE]
[--max-res-smooth SMOOTH]
[--max-dispersion MAX_DISP]
[--min-coverage MIN_COVERAGE]
[--plot-ref-imgs]
[--parallel]
input output roi
DESI-MSI recalibration tool
positional arguments:
input Input imzML file (centroided).
output Output imzML file.
roi Sample ROI mask CSV file. If set equal to 'full',
the entire image is analyzed.
optional arguments:
-h, --help show this help message and exit
--db DB Path of the reference masses database. If equal to
'builtin' a combination of public databases
will be used (default='builtin')
--analyzer {tof,orbitrap}
MS analyzer.
--ion-mode {pos,neg} ES Polarization mode.
--search-tol SEARCH_TOL
Search tolerance expressed in ppm. If 'auto', default
value for MS analyzer is used.
--kde-bw KDE_BW KDE bandwidth. It can be numeric, 'silverman' or
'silverman_robust' (default='silverman').
--degree DEGREE Degree of regression model for pixel masses
recalibration. If 'auto', 1 is used for Orbitrap,
5 is used for Tof (default=auto).
--max-res-smooth SMOOTH
Smoothing parameter for spline. It represents the
maximum sum of squared errors. If set to
'cv', it is determined by cross-validation
(default = 'cv').
--max-dispersion MAX_DISP
Max dispersion in ppm for outlier detection
(default=10.0).
--min-coverage MIN_COVERAGE
Min. coverage percentage for hits filtering
(default=75.0).
--plot-ref-imgs Save the intensity images of the reference masses.
It can slow down the process (default=False).
--parallel Use multithreading.
The code saves the images of the candidate reference ions in the subfolder of
the output folder, called runID + '_recal_imgs', where runID is the name of
the input imzML file.
By defaults, KDE bandwidth is set to rule-of-thumb value "silverman", and spline
smoothing parameter is determined by cross-validation.
In some cases, the user may be interested in using a custom value for those
parameters.
The script check_kde_bw.py tests various values and plots the results in a
subfolder of the input imzML folder.
Various values are tested on the 3 ions with the smallest median absolute error
from the nominal mass, that are detected in more than MIN_COVERAGE ROI pixels.
The script saves the scatter plots of the outliers and inliers matched masses in
the subfolder _calib of the input folder.
usage: check_kde_bw.py [-h] [--analyzer {tof,orbitrap}] --ion-mode {pos,neg}
[--search-tol SEARCH_TOL]
[--min-coverage MIN_COVERAGE]
input roi
DESI-MSI recalibration. Test time series parameters
positional arguments:
input Input imzML file.
roi Sample ROI mask CSV file. If set equal to 'full',
the entire image is analyzed.
optional arguments:
-h, --help show this help message and exit
--analyzer {tof,orbitrap}
MS analyzer.
--ion-mode {pos,neg} ES Polarization mode.
--search-tol SEARCH_TOL
Search tolerance expressed in ppm. If 'auto', default
value for MS analyzer is used.
--min-coverage MIN_COVERAGE
Min. coverage percentage for hits filtering
(default=75.0).
Example:
The outliers are plotted in red and the inliers (used to fit the GAM) are
plotted in blue. The dispersion is reported in the title (disp).
In this case, a value of bw=0.01 is optimal since inliers are detected in the
initial pixels (low value of pixel order). For the smooth parameter, no
difference is observed, so it is suggested to keep it equal to 'cv'.