imzML Writer: An Easy-to-Use Python Pipeline for Conversion of Continuously Acquired Raw Mass Spectrometry Imaging Files to imzML Format
Bibliographic record
Abstract
Mass spectrometry imaging (MSI) is a powerful tool which reveals the contextual distribution of biomolecules in tissues. Acquiring these images involves collecting an information-rich mass spectrum for each pixel of the ion image, which results in large datasets typically exceeding 1 GB. To streamline data processing and interpretation, various toolboxes have been developed for image pre-processing, segmentation, statistical analysis, and visualization. These generally require imaging data to be input in ‘imzML’ format, an Extensible Markup Language file with controlled vocabulary for mass spectrometry and MSI-specific parameters. While major/commercial MSI modalities (e.g. MALDI) come with proprietary file converters, to our knowledge, no open-access user-friendly converters exist for continuously acquired MSI data (e.g. nano-DESI, DESI). Here, we present imzML Writer, an open-access python application which is easy to install and easy to use. imzML Writer has a simple graphical user interface to convert data from MS vendor format into pixel-aligned imzML files suitable for further analysis. We package this application with imzML Scout, a tool to quickly visualize the resulting file(s) and batch export ion images across a range of image/data formats (PNG, TIF, CSV). To demonstrate the utility of files generated by imzML Writer, we processed a nano-DESI image using previously inaccessible toolboxes/data repositories such as Cardinal MSI and METASPACE. Overall, this work provides a simple tool for emerging MSI modality users to access the wealth of advanced MSI processing tools reliant on imzML format.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.076 |
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".