Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC)
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...
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Published in | PLoS computational biology Vol. 6; no. 5; p. e1000792 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
01.05.2010
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
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Summary: | 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 network 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: JAE NDP DG. Performed the experiments: JAE. Analyzed the data: JAE LH NDP DG. Wrote the paper: JAE LH NDP DG. |
ISSN: | 1553-7358 1553-734X 1553-7358 |
DOI: | 10.1371/journal.pcbi.1000792 |