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Functional annotation and network reconstruction through cross-platform integration of microarray data

Nature Biotechnology volume 23, pages 238–243 (2005)Cite this article

Abstract

The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2nd-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1st-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.

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Figure 1: The expression profiles and 1st-order expression correlation profiles of gene pairs POG1-MPT5 and SDA1-CDC5 over six microarray data sets (data set details in Supplementary Methods online).
Figure 2: Northern blot analysis showing the abundance of different cellular rRNAs in wild-type and ΔYOR309C cells.
Figure 3: Reconstruction of regulatory networks by 2nd-order expression analysis.
Figure 4: Hierarchical clustering of transcription modules based on their average 1st-order expression correlation profiles.

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Acknowledgements

We thank Robert Gentleman for making his computer resources available for part of this project, Timothy Hughes for technical advice and Michelle Arbeitman for sharing her lab space. We also thank two anonymous reviewers for their helpful comments. The work of X.J.Z. was supported by the National Science Foundation grant DMS0090166 to W.H.W., the Faculty Setup Grant from USC and the National Institutes of Health (NIH) grant R01GM067243 to Simon Tavaré. The work of M.-C.J.K was supported by a Howard Hughes Pre-doctoral Fellowship. The work of H.H. was supported by the NIH grant P20CA96470 to W.H.W. and the Faculty Setup Grant from UC Berkeley. The work of W.H.W. was supported by the NIH grant R01HG02341.

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Authors and Affiliations

  1. Program in Molecular and Computational Biology, University of Southern California, Los Angeles, 90089-1113, California, USA

    Xianghong Jasmine Zhou, Angela Wong, Juan Nunez-Iglesias, Oscar M Aparicio, Caleb E Finch & Todd E Morgan

  2. Department of Biostatistics, Harvard School of Public Health, Boston, 02115, Massachusetts, USA

    Xianghong Jasmine Zhou, Ming-Chih J Kao, Haiyan Huang & Wing Hung Wong

  3. School of Medicine, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Ming-Chih J Kao

  4. Department of Statistics, University of California, Berkeley, 94720, California, USA

    Haiyan Huang

  5. Andrus Gerontology Center, University of Southern California, Los Angeles, 90089-0191, California, USA

    Angela Wong, Caleb E Finch & Todd E Morgan

  6. Biozentrum & Swiss Institute of Bioinformatics, University of Basel, Basel, CH-4056, Switzerland

    Michael Primig

  7. Department of Statistics, Harvard University, Boston, 02138-2901, Massachusetts, USA

    Wing Hung Wong

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  1. Xianghong Jasmine Zhou
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Correspondence to Xianghong Jasmine Zhou or Wing Hung Wong.

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Supplementary information

Supplementary Table 1

Functional prediction of unknown yeast genes (PDF 18 kb)

Supplementary Table 2

60 derived transcription modules (PDF 15 kb)

Supplementary Methods (PDF 33 kb)

Supplementary Notes (PDF 64 kb)

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Zhou, X., Kao, MC., Huang, H. et al. Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat Biotechnol 23, 238–243 (2005). https://doi.org/10.1038/nbt1058

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