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desi_recalibration.py
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desi_recalibration.py
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# Copyright 2018 by Paolo Inglese, National Phenome Centre, Imperial College
# London
# All rights reserved.
# This file is part of DESI-MSI recalibration, and is released under the
# "MIT License Agreement".
# Please see the LICENSE file that should have been included as part of this
# package.
import argparse
import os
from typing import Dict
import numpy as np
from tools.functions import gen_ref_list, search_ref_masses, KDEMassRecal, \
del_all_files_dir, check_float
from tools.msi import MSI
def __parse_arg():
parser_ = argparse.ArgumentParser(description='DESI-MSI recalibration tool')
parser_.add_argument('input', type=str,
help='Input imzML file (centroided).')
parser_.add_argument('output', type=str, help='Output imzML file.')
parser_.add_argument('roi', type=str,
help='Sample ROI mask CSV file. If set equal to '
'\'full\', the entire image is analyzed.')
parser_.add_argument('--db', type=str, default='builtin',
help='Path of the reference masses database. '
'If equal to \'builtin\' a combination of public '
'databases will be used (default=\'builtin\')')
parser_.add_argument('--analyzer', choices=['tof', 'orbitrap'],
help='MS analyzer.', required=True)
parser_.add_argument('--ion-mode', choices=['pos', 'neg'],
help='ES Polarization mode.', required=True)
parser_.add_argument('--search-tol', default='auto',
help='Search tolerance expressed in ppm. If \'auto\', '
'default value for MS analyzer is used.')
parser_.add_argument('--kde-bw', default='silverman',
help='KDE bandwidth. It can be numeric, '
'\'silverman\' or \'silverman_robust\''
' (default=\'silverman\').')
parser_.add_argument('--degree', default='auto',
help='Degree of regression model for pixel masses '
'recalibration. If \'auto\', 1 is used for '
'Orbitrap, 5 is used for Tof (default=auto).')
parser_.add_argument('--max-res-smooth', default='cv', dest='smooth',
help='Smoothing parameter for spline. It represents '
'the maximum sum of squared errors. If set to '
'\'cv\', it is determined by cross-validation '
'(default = \'cv\').')
parser_.add_argument('--max-dispersion', default=10.0, type=float,
help='Max dispersion in ppm for outlier detection '
'(default=10.0).', dest='max_disp')
parser_.add_argument('--min-coverage', default=75.0, type=float,
help='Min. coverage percentage for hits filtering '
'(default=75.0).')
parser_.add_argument('--plot-ref-imgs', default=False, action='store_true',
dest='plot',
help='Save the intensity images of the reference '
'masses. It can slow down the process '
'(default=False).')
parser_.add_argument('--parallel', action='store_true', dest='parallel',
default=False, help='Use multithreading.')
return parser_
def set_params_dict(args_) -> Dict:
default_max_tol = {'orbitrap': 20.0, 'tof': 100.0}
if args_.degree == 'auto':
degree = 1 if args_.analyzer == 'orbitrap' else 5
else:
err_msg = '\'degree\' must be either \'auto\' or an integer.'
try:
degree = int(args_.degree)
except ValueError:
print(err_msg)
raise
params_ = {
'input': args_.input,
'output': args_.output,
'roi': args_.roi,
'analyzer': args_.analyzer,
'db': args_.db,
'bw': float(args_.kde_bw) if check_float(
args_.kde_bw) else args_.kde_bw,
'ion_mode': 'ES-' if args_.ion_mode == 'neg' else 'ES+',
'max_tol': args_.search_tol if args_.search_tol != 'auto' else
default_max_tol[args_.analyzer],
'min_cov': args_.min_coverage,
'max_disp': args_.max_disp,
'max_degree': degree,
'parallel': args_.parallel,
'plot': args_.plot,
'smooth': float(args_.smooth) if check_float(
args_.smooth) else args_.smooth,
'transform': 'none' if args_.analyzer == 'orbitrap' else 'sqrt'
}
return params_
def main():
parser = __parse_arg()
args = parser.parse_args()
params = set_params_dict(args)
# Load MSI
msi = MSI(imzml=params['input'], meta=params)
# Load ROI
if params['roi'] != 'full':
roi = np.loadtxt(params['roi'], delimiter=',')
if not np.all(roi.shape == msi.dim_xy[::-1]):
raise ValueError('ROI has incompatible dimensions.')
print('Num. ROI pixels = {}'.format(int(np.sum(roi))))
# Remove non-ROI pixels
outpx = np.where(roi.ravel() == 0)[0]
delpx = np.where(np.isin(msi.pixels_indices, outpx))[0]
delpx = np.sort(delpx)
msi.del_pixel(list(delpx))
# Creating match images dir
if params['plot']:
plots_dir = os.path.join(
os.path.dirname(params['output']), msi.ID + '_recal_imgs')
print('Intensity images will be saved in {}'.format(plots_dir))
if not os.path.isdir(plots_dir):
print('Creating plots dir {} ...'.format(plots_dir))
os.makedirs(plots_dir)
else:
del_all_files_dir(plots_dir)
else:
plots_dir = None
# RECALIBRATION ---------------------------
if params['db'] == 'builtin':
ref_masses = gen_ref_list(ion_mode=params['ion_mode'], verbose=True)
else:
print('Loading mass DB from {}...'.format(params['db']))
ref_masses = np.loadtxt(params['db'])
print('Searching lock masses within {} ppm ...'.format(
np.round(params['max_tol'], 2)))
matches = search_ref_masses(msiobj=msi, ref_masses=ref_masses,
max_tolerance=params['max_tol'],
coverage=params['min_cov'])
recal = \
KDEMassRecal(min_pct=params['min_cov'],
transform=params['transform'],
max_poly_degree=params['max_degree'],
max_disp_ppm=params['max_disp'],
kde_bw=params['bw'],
grid_size=2 ** 10, smooth=params['smooth'],
parallel=params['parallel'], plot=params['plot'],
plot_dir=plots_dir, plot_dim_xy=msi.dim_xy)
msi = recal.recalibrate(msi, matches)
print('Saving recalibrated ROI imzML ...')
msi.to_imzml(output_path=params['output'])
if __name__ == '__main__':
main()