Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

  • Muhammad Shoaib B. Sehgal
  • Iqbal Gondal
  • Laurence Dooley
  • Ross Coppel
  • Goh Kiah Mok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

Microarray gene expression data is used to model differential activity in Gene Regulatory Networks (GRN) to elucidate complex cellular processes, though network modeling is susceptible to errors due to both noisy nature of gene expression data and platform bias. This intuitively provided the motivation for the development of an innovative technique, which effectively integrates GRN using cross-platform data to minimize the two aforementioned effects. This paper presents a GRN integration (GeNi) framework that fuses cross-platform GRN to remove platform and experimental bias using the Dempster Shafer Theory of Evidence. The proposed model estimates gene co-regulation strength by using mutual information and removes spurious co-regulations through data processing inequality. The method automatically adapts to the data distribution using Belief theory, which does not require a preset threshold to accept co-regulated links. GeNi is applied to identify common cancer-related regulatory links in ten different datasets generated by different microarray platforms including cDNA and Affymetrix arrays. Experimental results demonstrate that GeNi can be effectively applied for GRN reconstruction and cross-platform gene network fusion for any gene expression data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Choi, J.K., Yu, U., Yoo, O.J., Kim, S.: Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21, 4348–4355 (2005)CrossRefGoogle Scholar
  2. [2]
    Fort, G., Lambert-Lacroix, S.: Classification using partial least squares with penalized logistic regression. Bioinformatics 21, 1104–1111 (2005)CrossRefGoogle Scholar
  3. [3]
    Zhou, X.J., Ming-Chih, Kao, J., Huang, H., Wong, A., Nunez-Iglesias, J., Primig, M., Aparicio, O.M., Finch, C.E., Morgan, T.E., Wong, W.H.: Functional annotation and network reconstruction through cross-platform integration of microarray data. Nature Biotechnology 23, 238–243 (2005)CrossRefGoogle Scholar
  4. [4]
    Basso, K., Margolin, A.A., Stolovitzky, G., Klein, U., Dalla-Favera, R., Califano, A.: Reverse engineering of regulatory networks in human B cells. Nature Genetics 37, 382–390 (2005)CrossRefGoogle Scholar
  5. [5]
    Shafer, G.: Mathematical Theory of Evidence. Princeton Univ. Press, Princeton, NJ (1976)MATHGoogle Scholar
  6. [6]
    Zhao, W., Serpedin, E., Dougherty, E.R.: Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics 22(17), 2129–2135 (2006)CrossRefGoogle Scholar
  7. [7]
    Casella, G., Robert, C.P.: Monte Carlo Statistical Methods. Springer, Heidelberg (2005)Google Scholar
  8. [8]
    Malpicaa, J.A., Alonsoa, M.C., Sanz, M.A.: Dempster–Shafer Theory in geographic information systems: A survey, Expert Systems with Applications, vol. 32. Elsevier, Amsterdam (2007)Google Scholar
  9. [9]
    Hegarat-Mascle, S.L., Bloch, I., Vidal-Madjar, D.: Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans. Geosci. Remote Sensing 35, 1018–1031 (1997)CrossRefGoogle Scholar
  10. [10]
    Bloch, I.: Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recognition Letters 17, 905–919 (1996)CrossRefGoogle Scholar
  11. [11]
    Rombaut, M., Zhu, Y.M.: Study of Dempster–Shafer for image segmentation applications. Image Vision Comput. 20, 15–23 (2002)CrossRefGoogle Scholar
  12. [12]
    Barnett, J.A.: Calculating Dempster-Shafer plausibility. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 599–602 (1991)CrossRefGoogle Scholar
  13. [13]
    Murphy, R.R.: Dempster-Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE Transactions on Robotics and Automation 14, 197–206 (1998)CrossRefGoogle Scholar
  14. [14]
    Sorlie, T., Perou, C., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M., Rijn, M.v.d., Jeffrey, S., Thorsen, T., Quist, H., Matese, J., Brown, P., Botstein, D., Lonning, P.E., Borresen-Dale, A.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. 11, 98(19), 10869–10874 (2001)CrossRefGoogle Scholar
  15. [15]
    Notterman, D.A., Alon, U., Sierk, A.J., Levine, A.J.: Transcriptional Gene Expression Profiles of Colorectal Adenoma, Adenocarcinoma, and Normal Tissue Examined by Oligonucleotide Arrays. Cancer Res. 61, 3124–3130 (2001)Google Scholar
  16. [16]
    Boer, J.M., et al.: Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array. Genome Research 11, 1861–1870 (2001)Google Scholar
  17. [17]
    Chen, X., Cheung, S.T., So, S., Fan, S.T., et al.: Gene Expression Patterns in Human Liver Cancers. Mol. Biol. Cell 13, 1929–1939 (2002)CrossRefGoogle Scholar
  18. [18]
    Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., Loda, M., Weber, G., Mark, E.F., Lander, E.S., Wong, W., Johnson, B.E., Golub, T.R., Sugarbaker, D.J., Meyerson, M.: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. 13790–13795 (2001)Google Scholar
  19. [19]
    Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)CrossRefGoogle Scholar
  20. [20]
    Lacobuzio-Donahue, C.A., et al.: Exploration of Global Gene Expression Patterns in Pancreatic Adenocarcinoma Using cDNA Microarrays. Am. J. Pathol. 162, 1151–1162 (2003)Google Scholar
  21. [21]
    Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002)Google Scholar
  22. [22]
    Chen, X., et al.: Variation in gene expression patterns in human gastric cancers. Mol. Biol. Cell 14, 3208–3215 (2003)CrossRefGoogle Scholar
  23. [23]
    Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumour gene expression signatures. Proc. Natl. Acad. Sci. 98(26), 15149–15154 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Muhammad Shoaib B. Sehgal
    • 1
    • 3
  • Iqbal Gondal
    • 1
    • 3
  • Laurence Dooley
    • 1
  • Ross Coppel
    • 2
    • 3
  • Goh Kiah Mok
    • 4
  1. 1.Faculty of IT, Monash University, Churchill VIC. 3842Australia
  2. 2.Department of Microbiology, Monash University Clayton VIC. 3800Australia
  3. 3.Victorian Bioinformatics Consortium Wellington Road, Clayton VIC., 3800Australia
  4. 4.SIMTech Institute of Technology, Nanyang DriveSingapore

Personalised recommendations