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PLoS Comput Biol. 2017 Sep 18;13(9):e1005742. doi: 10.1371/journal.pcbi.1005742. eCollection 2017 Sep.

Synthesizing developmental trajectories.

Author information

1
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.
2
Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, United States of America.
3
Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America.
4
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
5
Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
6
Department of Mathematics, Princeton University, Princeton, New Jersey, United States of America.

Abstract

Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics.

PMID:
28922353
PMCID:
PMC5619836
DOI:
10.1371/journal.pcbi.1005742
[Indexed for MEDLINE]
Free PMC Article

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