Your browser doesn't support javascript.
loading
CellTrackVis: interactive browser-based visualization for analyzing cell trajectories and lineages.
Shim, Changbeom; Kim, Wooil; Nguyen, Tran Thien Dat; Kim, Du Yong; Choi, Yu Suk; Chung, Yon Dohn.
Afiliação
  • Shim C; School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia.
  • Kim W; Data Intelligence Team, Samsung Research, Seoul, South Korea.
  • Nguyen TTD; School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia.
  • Kim DY; School of Engineering, RMIT University, Melbourne, Australia.
  • Choi YS; School of Human Sciences, University of Western Australia, Perth, Australia.
  • Chung YD; Department of Computer Science and Engineering, Korea University, Seoul, South Korea. ydchung@korea.ac.kr.
BMC Bioinformatics ; 24(1): 124, 2023 Mar 29.
Article em En | MEDLINE | ID: mdl-36991341
BACKGROUND: Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. RESULTS: This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. CONCLUSIONS: CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial .
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia / Software Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia / Software Idioma: En Ano de publicação: 2023 Tipo de documento: Article