Sub-population analysis based on temporal features of high content images
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 an...
| Hoofdauteurs: | , , , , |
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| Formaat: | Artikel |
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BMC Bioinformatics
2019
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| Online toegang: | http://hdl.handle.net/1721.1/121011 https://orcid.org/0000-0002-9038-1622 |
| _version_ | 1826211014836748288 |
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| author | Veronika, Merlin Evans, James Matsudaira, Paul Rajapakse, Jagath Welsch, Roy E |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Veronika, Merlin Evans, James Matsudaira, Paul Rajapakse, Jagath Welsch, Roy E |
| author_sort | Veronika, Merlin |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:59:04Z |
| format | Article |
| id | mit-1721.1/121011 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:59:04Z |
| publishDate | 2019 |
| publisher | BMC Bioinformatics |
| record_format | dspace |
| spelling | mit-1721.1/1210112022-09-29T11:54:29Z Sub-population analysis based on temporal features of high content images Veronika, Merlin Evans, James Matsudaira, Paul Rajapakse, Jagath Welsch, Roy E Sloan School of Management Welsch, Roy E 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. Computation and Systems Biology Programme of Singapore--Massachusetts Institute of Technology Alliance 2019-03-18T14:59:20Z 2019-03-18T14:59:20Z 2009-12 2019-03-06T13:33:40Z Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/121011 Veronika, Merlin, James Evans, Paul Matsudaira, Roy Welsch, and Jagath Rajapakse. “Sub-Population Analysis Based on Temporal Features of High Content Images.” BMC Bioinformatics 10, no. S15 (December 2009). © 2009 Veronika et al. https://orcid.org/0000-0002-9038-1622 http://dx.doi.org/10.1186/1471-2105-10-S15-S4 BMC Bioinformatics Creative Commons Attribution 2.0 Generic license https://creativecommons.org/licenses/by/2.0 application/pdf BMC Bioinformatics BioMed Central (BMC) |
| spellingShingle | Veronika, Merlin Evans, James Matsudaira, Paul Rajapakse, Jagath Welsch, Roy E Sub-population analysis based on temporal features of high content images |
| title | Sub-population analysis based on temporal features of high content images |
| title_full | Sub-population analysis based on temporal features of high content images |
| title_fullStr | Sub-population analysis based on temporal features of high content images |
| title_full_unstemmed | Sub-population analysis based on temporal features of high content images |
| title_short | Sub-population analysis based on temporal features of high content images |
| title_sort | sub population analysis based on temporal features of high content images |
| url | http://hdl.handle.net/1721.1/121011 https://orcid.org/0000-0002-9038-1622 |
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