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Research Article

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis

Lisa Pham, Lisa Christadore, Scott Schaus, and Eric D. Kolaczyk
  1. aProgram in Bioinformatics, and Departments of
  2. bChemistry and
  3. cMathematics and Statistics, Boston University, Boston, MA 02215

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PNAS August 9, 2011 108 (32) 13347-13352; first published July 25, 2011; https://doi.org/10.1073/pnas.1100891108
Lisa Pham
aProgram in Bioinformatics, and Departments of
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Lisa Christadore
bChemistry and
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Scott Schaus
bChemistry and
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Eric D. Kolaczyk
cMathematics and Statistics, Boston University, Boston, MA 02215
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  • For correspondence: kolaczyk@bu.edu
  1. Edited by Peter J. Bickel, University of California, Berkeley, CA, and approved June 21, 2011 (received for review January 19, 2011)

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Abstract

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (i) metastatic cancer progression, (ii) drug treatment in human lung carcinoma cells, and (iii) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.

  • centrality
  • microarray
  • pathway network

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: kolaczyk{at}bu.edu.
  • Author contributions: S.S. and E.D.K. designed research; L.P. and L.C. performed research; L.P. and L.C. analyzed data; and L.P., S.S., and E.D.K. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE5434).

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1100891108/-/DCSupplemental.

Freely available online through the PNAS open access option.

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Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis
Lisa Pham, Lisa Christadore, Scott Schaus, Eric D. Kolaczyk
Proceedings of the National Academy of Sciences Aug 2011, 108 (32) 13347-13352; DOI: 10.1073/pnas.1100891108

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Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis
Lisa Pham, Lisa Christadore, Scott Schaus, Eric D. Kolaczyk
Proceedings of the National Academy of Sciences Aug 2011, 108 (32) 13347-13352; DOI: 10.1073/pnas.1100891108
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Proceedings of the National Academy of Sciences: 108 (32)
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