Simultaneous clustering of multiple gene expression and physical interaction datasets.
Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2010-04-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC2855327?pdf=render |
| _version_ | 1811338100062814208 |
|---|---|
| author | Manikandan Narayanan Adrian Vetta Eric E Schadt Jun Zhu |
| author_facet | Manikandan Narayanan Adrian Vetta Eric E Schadt Jun Zhu |
| author_sort | Manikandan Narayanan |
| collection | DOAJ |
| description | Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes. |
| first_indexed | 2024-04-13T18:05:40Z |
| format | Article |
| id | doaj.art-ae51e78859cf462ca0371214812b25fc |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T18:05:40Z |
| publishDate | 2010-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ae51e78859cf462ca0371214812b25fc2022-12-22T02:36:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-04-0164e100074210.1371/journal.pcbi.1000742Simultaneous clustering of multiple gene expression and physical interaction datasets.Manikandan NarayananAdrian VettaEric E SchadtJun ZhuMany genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes.http://europepmc.org/articles/PMC2855327?pdf=render |
| spellingShingle | Manikandan Narayanan Adrian Vetta Eric E Schadt Jun Zhu Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Computational Biology |
| title | Simultaneous clustering of multiple gene expression and physical interaction datasets. |
| title_full | Simultaneous clustering of multiple gene expression and physical interaction datasets. |
| title_fullStr | Simultaneous clustering of multiple gene expression and physical interaction datasets. |
| title_full_unstemmed | Simultaneous clustering of multiple gene expression and physical interaction datasets. |
| title_short | Simultaneous clustering of multiple gene expression and physical interaction datasets. |
| title_sort | simultaneous clustering of multiple gene expression and physical interaction datasets |
| url | http://europepmc.org/articles/PMC2855327?pdf=render |
| work_keys_str_mv | AT manikandannarayanan simultaneousclusteringofmultiplegeneexpressionandphysicalinteractiondatasets AT adrianvetta simultaneousclusteringofmultiplegeneexpressionandphysicalinteractiondatasets AT ericeschadt simultaneousclusteringofmultiplegeneexpressionandphysicalinteractiondatasets AT junzhu simultaneousclusteringofmultiplegeneexpressionandphysicalinteractiondatasets |