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Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

Abu Sammour, D.; Cairns, J. L.; Boskamp, T.; Guevara, C. R.; Panitz, V.; Sadik, A.; Cordes, J.; Marsching, C.; Friedrich, M.; Platten, M.; Wolf, I.; von Deimling, A.; Opitz, C. A.; Wick, W.; Hopf, C.

2021-10-28 bioinformatics
10.1101/2021.10.27.466114 bioRxiv
Show abstract

Mass spectrometry imaging (MSI) vows to enable simultaneous spatially-resolved investigation of hundreds of metabolites in tissue sections, but it still relies on poorly defined ion images for data interpretation. Here, we outline moleculaR, a computational framework (https://github.com/CeMOS-Mannheim/moleculaR) that introduces probabilistic mapping and point-for-point statistical testing of metabolites in tissue. It enables collective molecular projections and consequently spatially-resolved investigation of ion milieus, lipid pathways or user-defined biomolecular ensembles within the same image.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Nature Methods
○ 385 papers in training set
Top 0.1%
44.9%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 7%
19.6%
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50% of probability mass above
3
Nature Biotechnology
○ 172 papers in training set
Top 0.9%
4.3%
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4
Bioinformatics
◐ 1204 papers in training set
Top 5%
3.7%
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5
Nature Machine Intelligence
○ 70 papers in training set
Top 0.9%
2.8%
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6
Communications Biology
○ 993 papers in training set
Top 13%
1.8%
7
Cell Reports Methods
○ 165 papers in training set
Top 1%
1.8%
8
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.6%
9
Communications Chemistry
○ 48 papers in training set
Top 0.7%
1.2%
10
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.1%
11
Advanced Science
○ 286 papers in training set
Top 7%
1.1%
12
Analytical Chemistry
● 218 papers in training set
Top 2%
0.9%
13
Genome Biology
○ 637 papers in training set
Top 9%
0.6%
14
PLOS Computational Biology
● 1863 papers in training set
Top 21%
0.6%
15
Chemical Science
● 73 papers in training set
Top 2%
0.6%
16
eLife
● 5828 papers in training set
Top 72%
0.5%
17
Nucleic Acids Research
◐ 1281 papers in training set
Top 16%
0.5%
18
Science Advances
● 1243 papers in training set
Top 35%
0.5%
19
Nature Metabolism
○ 69 papers in training set
Top 2%
0.5%
20
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 46%
0.5%
21
Cell Systems
○ 201 papers in training set
Top 6%
0.5%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.