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Related Experiment Video

Updated: Jun 21, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Reconstructing spatiotemporal gene expression data from partial observations.

Dustin A Cartwright1, Siobhan M Brady, David A Orlando

  • 1Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu

Bioinformatics (Oxford, England)
|July 18, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces a new computational method to integrate spatial and temporal gene expression data from Arabidopsis roots. The approach reconstructs high-resolution spatiotemporal patterns from incomplete data.

Area of Science:

  • Plant biology
  • Genomics
  • Bioinformatics

Background:

  • Developmental transcriptional networks are crucial in plants and animals, operating across space and time.
  • Understanding these networks requires high spatiotemporal resolution whole-genome expression data.
  • Existing Arabidopsis root expression data is spatially and temporally rich but not fully integrated, presenting challenges due to data heterogeneity and incompleteness.

Purpose of the Study:

  • To develop a novel method for reconstructing integrated, high-resolution spatiotemporal gene expression data.
  • To address the challenge of integrating heterogeneous and incomplete spatial and temporal expression datasets.
  • To enable a more comprehensive understanding of plant developmental transcriptional networks.

Main Methods:

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Related Experiment Videos

Last Updated: Jun 21, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

  • Developed a novel iterative algorithm for solving systems of bilinear equations.
  • Applied the algorithm to reconstruct integrated spatiotemporal gene expression patterns.
  • Utilized existing spatial and temporal microarray expression data from Arabidopsis roots.

    • Main Results:

    • Successfully reconstructed integrated high-resolution spatiotemporal gene expression data.
    • Demonstrated a method to overcome data heterogeneity and incompleteness in expression datasets.
    • Provided a framework for analyzing complex spatiotemporal biological processes.

    Conclusions:

    • The novel iterative algorithm effectively reconstructs integrated spatiotemporal expression data.
    • This method enhances the analysis of plant developmental transcriptional networks.
    • The approach offers a valuable tool for systems biology research in plants.