PMID- 35345884
OWN - NLM
STAT- MEDLINE
DCOM- 20220509
LR  - 20220509
IS  - 1559-4106 (Electronic)
IS  - 1559-4106 (Linking)
VI  - 17
IP  - 2
DP  - 2022 Mar 28
TI  - Applications of multivariate analysis and unsupervised machine learning to 
      ToF-SIMS images of organic, bioorganic, and biological systems.
PG  - 020802
LID - 10.1116/6.0001590 [doi]
AB  - Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a 
      powerful, label-free method for exploring organic, bioorganic, and biological 
      systems. The technique is capable of very high spatial resolution, while also 
      producing an enormous amount of information about the chemical and molecular 
      composition of a surface. However, this information is inherently complex, making 
      interpretation and analysis of the vast amount of data produced by a single 
      ToF-SIMS experiment a considerable challenge. Much research over the past few 
      decades has focused on the application and development of multivariate analysis 
      (MVA) and machine learning (ML) techniques that find meaningful patterns and 
      relationships in these datasets. Here, we review the unsupervised algorithms-that 
      is, algorithms that do not require ground truth labels-that have been applied to 
      ToF-SIMS images, as well as other algorithms and approaches that have been used 
      in the broader family of mass spectrometry imaging (MSI) techniques. We first 
      give a nontechnical overview of several commonly used classes of unsupervised 
      algorithms, such as matrix factorization, clustering, and nonlinear 
      dimensionality reduction. We then review the application of unsupervised 
      algorithms to various organic, bioorganic, and biological systems including cells 
      and tissues, organic films, residues and coatings, and spatially structured 
      systems such as polymer microarrays. We then cover several novel algorithms 
      employed for other MSI techniques that have received little attention from 
      ToF-SIMS imaging researchers. We conclude with a brief outline of potential 
      future directions for the application of MVA and ML algorithms to ToF-SIMS 
      images.
FAU - Gardner, Wil
AU  - Gardner W
AUID- ORCID: 0000000269031978
AD  - Centre for Materials and Surface Science and Department of Chemistry and Physics, 
      La Trobe University, Bundoora, Victoria 3086, Australia.
FAU - Winkler, David A
AU  - Winkler DA
AUID- ORCID: 0000000273016076
AD  - La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, 
      Victoria 3086, Australia.
FAU - Muir, Benjamin W
AU  - Muir BW
AUID- ORCID: 0000000288583217
AD  - CSIRO Manufacturing, Clayton, Victoria 3086, Australia.
FAU - Pigram, Paul J
AU  - Pigram PJ
AUID- ORCID: 000000027972492X
AD  - Centre for Materials and Surface Science and Department of Chemistry and Physics, 
      La Trobe University, Bundoora, Victoria 3086, Australia.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20220328
PL  - United States
TA  - Biointerphases
JT  - Biointerphases
JID - 101275679
SB  - IM
MH  - Algorithms
MH  - Machine Learning
MH  - Multivariate Analysis
MH  - *Spectrometry, Mass, Secondary Ion/methods
MH  - *Unsupervised Machine Learning
EDAT- 2022/03/30 06:00
MHDA- 2022/05/10 06:00
CRDT- 2022/03/29 05:37
PHST- 2022/03/29 05:37 [entrez]
PHST- 2022/03/30 06:00 [pubmed]
PHST- 2022/05/10 06:00 [medline]
AID - 10.1116/6.0001590 [doi]
PST - epublish
SO  - Biointerphases. 2022 Mar 28;17(2):020802. doi: 10.1116/6.0001590.