Documentation
Click on the links below to go to the documentation page for each repository. Note that most repositories are private.
File conversion
| Repository | Description | Doc |
|---|---|---|
| papillon | Python code with Waters .dll files to convert 1D and 2D .raw files to H5 format. This performs no filtering or processing of spectra and aims to reproduce the data exactly as in the .raw file, but in the non-propietary .h5 format. | Doc |
| ~ | ~ | ~ |
Pre-processing
| Repository | Description | Doc |
|---|---|---|
| seascape | MS pre-processing of Waters .raw files (also converted H5 files). Raw files are supported via MassLynx .dlls on Windows, but conversion of files to H5 format using papillon allows platform neutral analysis. | Doc |
| reimsPP | An updated implementation of Alvaro’s original pipeline for processing REIMS data. This repository breaks down the workflow into a Python part (for conversion and peak picking of the files) and an R part (for alignment and creation of the data matrix). Specific packages (and package versions) are required for this to function, so precisely following the installation instructions is required. | Doc |
| imzmlProc | Pre-processing of imzML/H5 format imaging files. The various stages are: recalibration, peak picking, extraction, annotation and coregistration. Simple statistical analysis is included, although further developments will be incorporated into maml. | Doc |
Statistical analysis
| Repository | Description | Doc |
|---|---|---|
| maml | Matlab machine learning for statistical analysis of both one- and two-dimensional MS data. 1D data processed via seascape is supported, and for imaging data generated via imzmlProc. The repository workflows detail the methods available for the individual classes. All future statistical functions in Matlab will be incorporated into this repository. | Doc |
| dapy | Data analysis in Python: similar to maml but Python functions | Doc |
Image processing and manipulation
| Repository | Description | Doc |
|---|---|---|
| lazycheetah | Segmentation of TMA data into individual cores | Doc |
| ~ | ~ | Doc |