Published in

Portland Press, Biochemical Journal, 11(479), p. 1257-1263, 2022

DOI: 10.1042/bcj20220053

Links

Tools

Export citation

Search in Google Scholar

Towards ‘end-to-end’ analysis and understanding of biological timecourse data

Journal article published in 2022 by Siddhartha G. Jena ORCID, Alexander G. Goglia ORCID, Barbara E. Engelhardt ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Red circle
Preprint: archiving forbidden
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Petabytes of increasingly complex and multidimensional live cell and tissue imaging data are generated every year. These videos hold large promise for understanding biology at a deep and fundamental level, as they capture single-cell and multicellular events occurring over time and space. However, the current modalities for analysis and mining of these data are scattered and user-specific, preventing more unified analyses from being performed over different datasets and obscuring possible scientific insights. Here, we propose a unified pipeline for storage, segmentation, analysis, and statistical parametrization of live cell imaging datasets.