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Data Integration in Multi-dimensional Data Sets: Informational Asymmetry in the Valid Correlation of Subdivided Samples

  • Qing T. Zeng
  • Juan Pablo Pratt
  • Jane Pak
  • Eun-Young Kim
  • Dino Ravnic
  • Harold Huss
  • Steven J. Mentzer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)

Abstract

Background: Flow cytometry is the only currently available high throughput technology that can measure multiple physical and molecular characteristics of individual cells. It is common in flow cytometry to measure a relatively large number of characteristics or features by performing separate experiments on subdivided samples. Correlating data from multiple experiments using certain shared features (e.g. cell size) could provide useful information on the combination pattern of the not shared features. Such correlation, however, are not always reliable. Methods: We developed a method to assess the correlation reliability by estimating the percentage of cells that can be unambiguously correlated between two samples. This method was evaluated using 81 pairs of subdivided samples of microspheres (artificial cells) with known molecular characteristics. Results: Strong correlation (R=0.85) was found between the estimated and actual percentage of unambiguous correlation. Conclusion: The correlation reliability we developed can be used to support data integration of experiments on subdivided samples.

Keywords

correlation data integration subdivided sample flow cytometry 

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References

  1. 1.
    Shapiro, H.M.: Practical Flow Cytometry, 4th edn. Wiley-Liss Inc., New York (2002)Google Scholar
  2. 2.
    Saeed, M., Mark, R.G.: Efficient hemodynamic event detection utilizing relational databases and wavelet analysis. Comput. Cardiol 28, 153–156 (2001)Google Scholar
  3. 3.
    Chu, S.C., Thom, J.B.: Database issues in object-oriented clinical information systems design. Stud. Health Technol. Inform. 46, 376–382 (1997)Google Scholar
  4. 4.
    Montgomery Jr., E.B., Huang, H., Assadi, A.: Unsupervised clustering algorithm for N-dimensional data. J. Neurosci. Methods 144(1), 19–24 (2005)CrossRefGoogle Scholar
  5. 5.
    Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Pac. Symp. Biocomput., pp. 6–17 (2002)Google Scholar
  6. 6.
    Dudoit, S., Fridlyand, J.: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome. Biol. 3(7) (2002); RESEARCH0036Google Scholar
  7. 7.
    Lange, T., Roth, V., Braun, M.L., Buhmann, J.: Stability-based validation of clustering solutions. Neural Comput. 16(6), 1299–1323 (2004)zbMATHCrossRefGoogle Scholar
  8. 8.
    Lee, S., Crawford, M.M.: Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure. IEEE Trans Image Process 14(3), 312–320 (2005)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Chaudhuri, P., Marron, J.S.: SiZer for exploration of structures in curves. Journal of the American Statistical Association 94, 807–823 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc, Chichester (2001)zbMATHGoogle Scholar
  11. 11.
    Brecher, G., Ms, M., Williams, G.Z.: Evaluation of electronic red blood cell counter. Am.J.Clin.Pathol. 26, 1439–1449 (1956)Google Scholar
  12. 12.
    Young, I.T.: Proof without prejudice: use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources. J. Histochem. Cytochem. 25(7), 935–941 (1977)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qing T. Zeng
    • 1
    • 3
  • Juan Pablo Pratt
    • 2
  • Jane Pak
    • 1
    • 2
  • Eun-Young Kim
    • 1
    • 4
  • Dino Ravnic
    • 2
  • Harold Huss
    • 2
  • Steven J. Mentzer
    • 2
  1. 1.Decision Systems Group 
  2. 2.Department of SurgeryBrigham and Women’s Hospital, Harvard Medical SchoolBoston
  3. 3.Harvard-MIT Division of Human Sciences and TechnologyCambridge
  4. 4.Department of Clinical PharmacologyInje University Busan Paik Hospital 

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