title: Sub-population analysis based on temporal features of high content images.
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.
  
  
  CONCLUSIONS
  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.
authors: Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore. merlin@pmail.ntu.edu.sg
authors: Merlin
authors: M
authors: Veronika
authors: James
authors: J
authors: Evans
authors: Paul
authors: P
authors: Matsudaira
authors: Roy
authors: R
authors: Welsch
authors: Jagath
authors: J
authors: Rajapakse
mesh_terms: D000818:Animals; D002465:Cell Movement / Q000502:physiology*; D002477:Cells / Q000166:cytology; D002478:Cells, Cultured; D019295:Computational Biology / Q000379:methods*; D008264:Macrophages / Q000166:cytology* / Q000378:metabolism; D051379:Mice; D010363:Pattern Recognition, Automated