Identifying Network Perturbation in Cancer.
We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4856318?pdf=render |
| _version_ | 1818007225729286144 |
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| author | Maxim Grechkin Benjamin A Logsdon Andrew J Gentles Su-In Lee |
| author_facet | Maxim Grechkin Benjamin A Logsdon Andrew J Gentles Su-In Lee |
| author_sort | Maxim Grechkin |
| collection | DOAJ |
| description | We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed-having distinct regulator connectivity in the inferred gene-regulator dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic data from the ENCODE project. |
| first_indexed | 2024-04-14T05:12:58Z |
| format | Article |
| id | doaj.art-cb43c7c70f4c41fe8db0c3157da9debf |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-14T05:12:58Z |
| publishDate | 2016-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-cb43c7c70f4c41fe8db0c3157da9debf2022-12-22T02:10:29ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-05-01125e100488810.1371/journal.pcbi.1004888Identifying Network Perturbation in Cancer.Maxim GrechkinBenjamin A LogsdonAndrew J GentlesSu-In LeeWe present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed-having distinct regulator connectivity in the inferred gene-regulator dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic data from the ENCODE project.http://europepmc.org/articles/PMC4856318?pdf=render |
| spellingShingle | Maxim Grechkin Benjamin A Logsdon Andrew J Gentles Su-In Lee Identifying Network Perturbation in Cancer. PLoS Computational Biology |
| title | Identifying Network Perturbation in Cancer. |
| title_full | Identifying Network Perturbation in Cancer. |
| title_fullStr | Identifying Network Perturbation in Cancer. |
| title_full_unstemmed | Identifying Network Perturbation in Cancer. |
| title_short | Identifying Network Perturbation in Cancer. |
| title_sort | identifying network perturbation in cancer |
| url | http://europepmc.org/articles/PMC4856318?pdf=render |
| work_keys_str_mv | AT maximgrechkin identifyingnetworkperturbationincancer AT benjaminalogsdon identifyingnetworkperturbationincancer AT andrewjgentles identifyingnetworkperturbationincancer AT suinlee identifyingnetworkperturbationincancer |