PUBLICATION
Automated gene oscillation phase classification for zebrafish presomitic mesoderm cells
- Authors
- Lu, Y., Lu, J., Liu, T., and Yang, J.
- ID
- ZDB-PUB-110629-41
- Date
- 2011
- Source
- Cytometry. Part A : the journal of the International Society for Analytical Cytology 79(9): 727-35 (Journal)
- Registered Authors
- Liu, Tianming
- Keywords
- none
- MeSH Terms
-
- Animals
- Cell Nucleus/genetics
- Cell Nucleus/metabolism
- Gene Expression Regulation, Developmental
- Genetic Techniques*
- Image Processing, Computer-Assisted
- Mesoderm/cytology*
- Mesoderm/embryology
- Pattern Recognition, Automated/methods*
- RNA, Messenger/analysis
- Staining and Labeling
- Support Vector Machine
- Zebrafish/embryology
- Zebrafish/genetics*
- Zebrafish Proteins/genetics*
- Zebrafish Proteins/metabolism
- PubMed
- 21710640 Full text @ Cytometry A
Citation
Lu, Y., Lu, J., Liu, T., and Yang, J. (2011) Automated gene oscillation phase classification for zebrafish presomitic mesoderm cells. Cytometry. Part A : the journal of the International Society for Analytical Cytology. 79(9):727-35.
Abstract
Zebrafish somitogenesis is governed by a segmentation clock that generates oscillations of gene expression in the zebrafish presomitic mesoderm (PSM) cells. The segmentation clock causes cells to undergo repeated cycles of transcriptional activation and repression, which can be divided into eight phases based on their distinct mRNA co-localizations. Recognizing different gene oscillation phases of cells is important in zebrafish research, but manual analysis is time-consuming and difficult. In this article, an effective automated gene oscillation phase classification framework is established for zebrafish PSM cell images. The framework consists of three major steps: (1) identify the individual cells by a two-stage segmentation procedure; (2) extract multiple features on each cell patch to measure the subcellular mRNA distribution; (3) employ a support vector machine (SVM) with a combined kernel to complete feature fusion and classification. To evaluate the effectiveness of this framework, a dataset containing 2,227 cell samples is constructed. Experimental results on this dataset indicate that our approach can achieve reasonably good performance for this gene oscillation classification problem. The feature sets NF9 and SPIN introduced in this article have proved to be superior to other cell features in this problem. Besides, the kernel fusion method used in the third step provides a way to combine heterogeneous features together, i.e., numerical feature set and histogram-based feature set, and classification performance with the combined kernel is better than single feature.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping