ArticleDevelopment (Cambridge, England)2015
Temporal ordering and registration of images in studies of developmental dynamics.
Article in Development (Cambridge, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Tissue maps in motion.Nature methods · 2026Article
- Uncovering developmental time and tempo using deep learning.Nature methods · 2023Article
- Article
- Quantitative analysis of cell shape and the cytoskeleton in developmental biology.Wiley interdisciplinary reviews. Developmental biology · 2018Review
- Data-driven Evolution Equation Reconstruction for Parameter-Dependent Nonlinear Dynamical Systems.Israel journal of chemistry · 2018Article
- An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning.IEEE access : practical innovations, open solutions · 2018Article
- Reconstruction of normal forms by learning informed observation geometries from data.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Synthesizing developmental trajectories.PLoS computational biology · 2017Article
- Reconstructing ERK Signaling in the Drosophila Embryo from Fixed Images.Methods in molecular biology (Clifton, N.J.) · 2017Article
- New Twists in Drosophila Cell Signaling.The Journal of biological chemistry · 2016Review
- A Transport Model for Estimating the Time Course of ERK Activation in the C. elegans Germline.Biophysical journal · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Progress of development is commonly reconstructed from imaging snapshots of chemical or mechanical processes in fixed tissues. As a first step in these reconstructions, snapshots must be spatially registered and ordered in time. Currently, image registration and ordering are often done manually, requiring a significant amount of expertise with a specific system. However, as the sizes of imaging data sets grow, these tasks become increasingly difficult, especially when the images are noisy and the developmental changes being examined are subtle. To address these challenges, we present an automated approach to simultaneously register and temporally order imaging data sets. The approach is based on vector diffusion maps, a manifold learning technique that does not require a priori knowledge of image features or a parametric model of the developmental dynamics. We illustrate this approach by registering and ordering data from imaging studies of pattern formation and morphogenesis in three model systems. We also provide software to aid in the application of our methodology to other experimental data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.