%0 Journal Article %1 Eddy2010Identifying %A Eddy, James A. %A Hood, Leroy %A Price, Nathan D. %A Geman, Donald %D 2010 %I Public Library of Science %J PLoS Comput Biol %K differential-analysis expression network-analyses %N 5 %P e1000792+ %R 10.1371/journal.pcbi.1000792 %T Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation (DIRAC) %U http://dx.doi.org/10.1371/journal.pcbi.1000792 %V 6 %X 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. The systems approach to medicine derives from the idea that diseased cells arise from one or more perturbed biological networks due to the net effect of interactions among multiple molecular agents; by measuring differences in the abundance of biomolecules (e.g., mRNA, proteins, metabolites) we can identify reporters of network states and uncover molecular signatures of disease. However, a major limitation of previously published network analyses is the focus on small numbers of individual, differentially-expressed genes, hence the failure to take into account combinatorial interactions. We report a new technique, Differential Rank Conservation, for identifying and measuring network-level perturbations. Our rank conservation index is based entirely on the relative levels of expression for participating genes and allows us to detect differences in network orderings between networks for a given phenotype and between phenotypes for a given network. In examining cancer subtypes and neurological disorders, we identified networks that are tightly and loosely regulated, as defined by the level of conservation of transcript ordering, and observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. We also demonstrate that variably expressed networks represent robust differences between disease states.

@article{Eddy2010Identifying, 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 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. The systems approach to medicine derives from the idea that diseased cells arise from one or more perturbed biological networks due to the net effect of interactions among multiple molecular agents; by measuring differences in the abundance of biomolecules (e.g., {mRNA}, proteins, metabolites) we can identify reporters of network states and uncover molecular signatures of disease. However, a major limitation of previously published network analyses is the focus on small numbers of individual, differentially-expressed genes, hence the failure to take into account combinatorial interactions. We report a new technique, Differential Rank Conservation, for identifying and measuring network-level perturbations. Our rank conservation index is based entirely on the relative levels of expression for participating genes and allows us to detect differences in network orderings between networks for a given phenotype and between phenotypes for a given network. In examining cancer subtypes and neurological disorders, we identified networks that are tightly and loosely regulated, as defined by the level of conservation of transcript ordering, and observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. We also demonstrate that variably expressed networks represent robust differences between disease states.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Eddy, James A. and Hood, Leroy and Price, Nathan D. and Geman, Donald}, biburl = {https://www.bibsonomy.org/bibtex/22bfd0903d8b16c06758ef4783f839ea7/karthikraman}, citeulike-article-id = {7227837}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000792}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/20523739}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=20523739}, day = 27, doi = {10.1371/journal.pcbi.1000792}, interhash = {94acece5d167add60bb91ea1b5d4490f}, intrahash = {2bfd0903d8b16c06758ef4783f839ea7}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {differential-analysis expression network-analyses}, month = may, number = 5, pages = {e1000792+}, pmid = {20523739}, posted-at = {2010-05-30 14:17:11}, priority = {5}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation ({DIRAC})}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000792}, volume = 6, year = 2010 }