Identifying tightly regulated and variably expressed networks by differential rank conservation (DIRAC)

Eddy, James A.

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https://hdl.handle.net/2142/18432 Copy

Description

Title

Identifying tightly regulated and variably expressed networks by differential rank conservation (DIRAC)

Author(s)

Eddy, James A.

Issue Date

2011-01-14T22:50:44Z

Director of Research (if dissertation) or Advisor (if thesis)

Price, Nathan D.

Department of Study

Bioengineering

Discipline

Bioengineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Systems biology
• network analysis
• gene expression
• disease classification

Abstract

A powerful way to separate signal from noise in biology is to convert the molecular data from individual genes or proteins into an analysis of comparative biological network behaviors. One of the limitations of previous network analyses is that they do not take into account the combinatorial nature of gene interactions within the network. We report here a new technique, Differential Rank Conservation (DIRAC), which permits one to assess these combinatorial interactions to quantify various biological pathways or networks in a comparative sense, and to determine how they change in different individuals experiencing the same disease process. This approach is based on the relative expression values of participating genes—i.e., the ordering of expression within pathway profiles. DIRAC provides quantitative measures of how network rankings differ either among networks for a selected phenotype or among phenotypes for a selected network. We examined disease phenotypes including cancer subtypes and neurological disorders and identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. At a sample level, DIRAC can detect a change in ranking between phenotypes for any selected network. Variably expressed networks represent statistically robust differences between disease states and serve as signatures for accurate molecular classification, validating the information about expression patterns captured by DIRAC. Importantly, DIRAC can be applied not only to transcriptomic data but to any ordinal data type.

Graduation Semester

2010-12

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http://hdl.handle.net/2142/18432

Copyright and License Information

Copyright 2010 James A. Eddy

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