maldi-msi

Shiny App for Mass Spectrometry Imaging

A tool for mass spectrometry data analysis.

SPUTNIK R package

This JupyterNotebook-based code implements a Mass Spectrometry Imaging (MSI) visualiser tool that allows researchers to explore molecular distributions across various samples for MALDI and DESI images using imzml file.

Script for analyzing intensity of olefin species from AgLDI IMS data. Uses MALDIquant, Cardinal and Tidyverse

A standalone, GUI for Mass Spectrometry Imaging (MSI) analysis, combining unsupervised tissue segmentation, spatial molecular profiling, and supervised classification.

IonScell is a Python pipeline, Jupyter/Colab workflow and GUI for end-to-end analysis of single-cell Mass Spectrometry Imaging (MSI) data, from raw imzML files to biological insight. It integrates segmentation, spectral extraction, quality control, soft clustering, cells population analysis and more , in a reproducible modular workflow.

Default preprocessing pipeline for MSI data in raw ASCII format based on Gaussian Mixture Modeling (GMM)

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

Supervised topological data analysis for MALDI-MSI data — persistence transformation for spectral feature extraction and classification, applied to public and proprietary clinical datasets.

Codes for analyzing the relative ganglioside intensities MS results of brain sections. Calculates total ion current using MALDIquant.

Master thesis — unsupervised deep clustering of MALDI-MSI ion images, University of Padova 2024

Data analysis pipeline for strawberry imaging mass spectrometry data in Food Chemistry publication. Uses Cardinal, MALDIquant and Tidyverse

Script for unsupervised analysis of microdissected fly tubule phospholipid signals as well as fly sections. Uses Cardinal

Tutorial preprocessing MSI using MALDIquant

A complete end-to-end MALDI-MSI analysis pipeline applied to a public mouse urinary bladder dataset. The pipeline covers raw data loading, spectral preprocessing, unsupervised tissue segmentation, and statistical identification of region-specific lipid markers. Built as tutoring material to demonstrate best practices in mass spectrometry