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. 2009 Dec 3;10 Suppl 15(Suppl 15):S4.
doi: 10.1186/1471-2105-10-S15-S4.

Sub-population analysis based on temporal features of high content images

Merlin Veronika  1 , James Evans, Paul Matsudaira, Roy Welsch, Jagath Rajapakse
Affiliations

Sub-population analysis based on temporal features of high content images

Merlin Veronika et al. BMC Bioinformatics. .

Abstract

Background: High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods.

Results: In this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. We used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. The sub-populations were obtained by clustering static, dynamic and a combination of both features. We were able to identify three unique sub-populations in combined clustering.

Conclusion: We report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. These advances will facilitate the application of high content screening data analysis to new and complex biological problems.

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Figures

Figure 1
Figure 1
Segmentation by level set method. (a) Snapshot of the image data used for analysis (b) Initial (green) and final (red) contour representing segmented cells.
Figure 2
Figure 2
Comparison of level set segmentation with Otsu's and fuzzy c-means method. (a) Snapshot of unprocessed image. (b) Otsu's method. (c) Fuzzy c-means and (d) Level set segmentation.
Figure 3
Figure 3
Tracks of cells identified by AR Model. Red spots represent initial position of the cell and dragon tail in black represent the trajectories.
Figure 4
Figure 4
Optimal clusters from static features. For static features, the point of inflection is in cluster 3.
Figure 5
Figure 5
Optimal clusters from dynamic features. For dynamic features, the point of inflection is in cluster 4.
Figure 6
Figure 6
Optimal clusters from static and dynamic features. Adding dynamic features has increased the resolution of cluster size bringing the optimal cluster size to 3.
Figure 7
Figure 7
Wind rose plot for cluster 1. Wind rose plot for cluster 1. The numbers in red indicate total path length in μ
Figure 8
Figure 8
Wind rose plot for cluster 2. Wind rose plot for cluster 2. The numbers in red indicate total path length in μ
Figure 9
Figure 9
Wind rose plot for cluster 3. Wind rose plot for cluster 3. The numbers in red indicate total path length in μ
See this image and copyright information in PMC

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