ac0c03833_si_003.xlsx (22.25 kB)
MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging
dataset
posted on 2020-12-02, 23:30 authored by Jonatan
O. Eriksson, Alejandro Sánchez Brotons, Melinda Rezeli, Frank Suits, György Markó-Varga, Peter HorvatovichMass spectrometry imaging (MSI) is
a technique that provides comprehensive
molecular information with high spatial resolution from tissue. Today,
there is a strong push toward sharing data sets through public repositories
in many research fields where MSI is commonly applied; yet, there
is no standardized protocol for analyzing these data sets in a reproducible
manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can
make it impossible to distinguish one compound from another. Here,
we present a label-free m/z alignment
approach that is compatible with multiple instrument types and makes
no assumptions on the sample’s molecular composition. Our approach,
MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function
by maximizing a similarity score that considers both the intensity
and m/z position of peaks matched
between two spectra. MSIWarp requires only centroid spectra to find
the recalibration function and is thereby readily applicable to almost
any MSI data set. To deal with particularly misaligned or peak-sparse
spectra, we provide an option to detect and exclude spurious peak
matches with a tailored random sample consensus (RANSAC) procedure.
We evaluate our approach with four publicly available data sets from
both time-of-flight (TOF) and Orbitrap instruments and demonstrate
up to 88% improvement in m/z alignment.