PUBLICATION
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling
- Authors
- Nguyen, H.D., Ullmann, J.F.P., McLachlan, G.J., Voleti, V., Li, W., Hillman, E.M.C., Reutens, D.C., Janke, A.L.
- ID
- ZDB-PUB-180508-4
- Date
- 2018
- Source
- Statistical analysis and data mining 11: 5-16 (Journal)
- Registered Authors
- Ullmann, Jeremy
- Keywords
- Calcium imaging, Mixture model, Time series data, Zebrafish
- MeSH Terms
- none
- PubMed
- 29725490 Full text @ Stat Anal Data Min
Citation
Nguyen, H.D., Ullmann, J.F.P., McLachlan, G.J., Voleti, V., Li, W., Hillman, E.M.C., Reutens, D.C., Janke, A.L. (2018) Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling. Statistical analysis and data mining. 11:5-16.
Abstract
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping