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Related Experiment Videos

RaPiDS: an algorithm for rapid expression profile database search.

Paul B Horton1, Larisa Kiseleva, Wataru Fujibuchi

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. horton-p@aist.go.jp

Genome Informatics. International Conference on Genome Informatics
|May 16, 2007
PubMed
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This study introduces a fast algorithm for Spearman rank correlation (SRC) in gene expression analysis, achieving a 100-fold speed-up. SRC shows promise for classifying cell types from gene expression data under similar experimental conditions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression profiling generates large datasets requiring efficient similarity search.
  • Spearman rank correlation (SRC) is a potential metric for comparing expression profiles.
  • Existing methods for SRC computation can be computationally intensive.

Purpose of the Study:

  • To develop a fast algorithm and C++ implementation for computing SRC between expression profiles.
  • To evaluate the utility of SRC as a similarity measure for classifying human cell types using gene expression data.

Main Methods:

  • A novel algorithm with linear time complexity for SRC computation was developed.
  • The algorithm efficiently handles multiple profile platforms and missing values.

Related Experiment Videos

  • A k-nearest neighbor classifier utilizing SRC was applied to microarray data for cell type classification.

    • Main Results:

    • The specialized algorithm achieved an approximate 100-fold speed-up compared to a baseline implementation.
    • Preliminary classification accuracy of 64% was achieved for normal human cell types using SRC on profiles from similar experimental conditions.
    • Performance decreased when comparing profiles from different experimental series, indicating a need for further refinement.

    Conclusions:

    • The developed SRC algorithm is highly efficient for large-scale similarity searches in expression profile databases.
    • SRC demonstrates utility as a similarity measure for gene expression-based cell type classification, particularly for data from consistent experimental setups.
    • Further research is needed to improve SRC's robustness for cross-experimental series comparisons.