imzML Writer: An Easy-to-Use Python Pipeline for Conversion of Continuously Acquired Raw Mass Spectrometry Imaging Data to imzML Format
Bibliographic record
Abstract
Mass spectrometry imaging (MSI) is a technique that uncovers the contextual distribution of biomolecules in tissue. This involves collecting large data sets with information-rich mass spectra in each pixel. To streamline image processing and interpretation, the MSI community has developed toolboxes for image preprocessing, segmentation, statistical analysis, and visualization. These generally require data to be input as imzML files, an Extensible Markup Language file with vocabulary for mass spectrometry and imaging-specific parameters. While commercial systems (e.g., MALDI) come with proprietary file converters, to our knowledge, no open-access user-friendly converters exist for continuously acquired imaging data (e.g., nano-DESI, DESI). Here, we present imzML Writer, an easy-to-use Python application with a graphical user interface to convert data from vendor format into pixel-aligned imzML files. We package this application with imzML Scout, allowing visualization of the resulting file(s) and batch export of ion images across a range of image and data formats (e.g., PNG, TIF, CSV). To demonstrate the utility of files generated by imzML Writer, we processed nano-DESI data with popular tools such as Cardinal MSI and METASPACE. Overall, this work provides a simple open-access tool for emerging MSI modality users to access advanced MSI processing tools reliant on imzML format. ImzML Writer is available as a distributable Python package via pip or as a standalone program for Mac and PC at https://github.com/VIU-Metabolomics/imzML_Writer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.067 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".