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Reconstructing the temporal ordering of biological samples using microarray data.

Paul M Magwene1, Paul Lizardi, Junhyong Kim

  • 1Department of Ecology and Evolutionary Biology, Yale University School of Medicine, New Haven, CT, USA.

Bioinformatics (Oxford, England)
|May 2, 2003
PubMed
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This study introduces new algorithms to reconstruct biological time series from unordered DNA microarray data. These methods improve temporal ordering and assess data quality, aiding in the analysis of complex biological processes.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Estimating accurate biological time series is challenging due to synchronization issues, temporal sampling, and rate heterogeneity.
  • Multi-dimensional data from DNA microarray experiments require methods to reconstruct time series from unordered observations.

Purpose of the Study:

  • To develop and present algorithms for estimating temporal orderings from unordered sample sets.
  • To provide heuristics for assessing dataset properties like noise and sampling intensity.
  • To demonstrate the utility of PQ-trees for representing uncertainty in reconstructed orderings.

Main Methods:

  • Algorithms based on modifications of minimum-spanning trees calculated from weighted, undirected graphs.
  • Application of techniques to artificial and gene expression datasets from DNA microarray experiments.

Related Experiment Videos

  • Utilizing PQ-trees to represent uncertainty in temporal ordering.

    • Main Results:

    • Successfully estimated temporal orderings from unordered sample elements.
    • Demonstrated efficacy on both artificial and real-world gene expression data.
    • Developed heuristics for assessing noise and sampling intensity in datasets.
    • Showcased PQ-tree application for uncertainty representation.

    Conclusions:

    • The presented algorithms effectively reconstruct biological time series from unordered data.
    • The methods offer valuable insights into data quality and ordering uncertainty.
    • These techniques enhance the analysis of complex biological processes using high-dimensional data.