Co-registration and analysis of multiple imaging mass spectrometry datasets targeting different analytes
Bibliographic record
Abstract
MOTIVATION: MALDI imaging mass spectrometry (IMS) has been successfully used to image a variety of biomolecules. Imaging of the many classes of biomolecules is often achieved through several incompatible sample preparations. Thus, multiple datasets must be acquired from multiple tissue sections to obtain a total molecular overview of a single sample. Addressing the need for single datasets from multiple IMS analyses, we developed the R package RegCombIMS as an extension of R package Cardinal to co-register, combine and create single IMS datasets acquired from serial sections of tissue. RESULTS: Dataset recombination and analysis is achieved by registration of the IMS datasets to a single coordinate space. The workflow allows for correlation of ions from IMS acquisitions that require incompatible sample preparations as well as multivariate analysis to mine the combined dataset for rapid and more thorough molecular query. AVAILABILITY AND IMPLEMENTATION: The source code and example data are freely available at https://github.com/NHPatterson/RegCombIMS. All code was implemented in R. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".