SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets
The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multip...
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| Format: | Article |
| Language: | en_US |
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Royal Society of Chemistry
2014
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| Online Access: | http://hdl.handle.net/1721.1/88961 https://orcid.org/0000-0002-2744-8994 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-6534-4774 |
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| author | Spencer, Sarah J. Ursu, Oana Fraenkel, Ernest Gosline, Sara Jane Calafell |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Spencer, Sarah J. Ursu, Oana Fraenkel, Ernest Gosline, Sara Jane Calafell |
| author_sort | Spencer, Sarah J. |
| collection | MIT |
| description | The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein–protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT. |
| first_indexed | 2024-09-23T10:02:10Z |
| format | Article |
| id | mit-1721.1/88961 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:02:10Z |
| publishDate | 2014 |
| publisher | Royal Society of Chemistry |
| record_format | dspace |
| spelling | mit-1721.1/889612022-09-26T15:18:42Z SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets Spencer, Sarah J. Ursu, Oana Fraenkel, Ernest Gosline, Sara Jane Calafell Massachusetts Institute of Technology. Computational and Systems Biology Program Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Gosline, Sara Jane Calafell Spencer, Sarah J. Ursu, Oana Fraenkel, Ernest The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein–protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT. National Institutes of Health (U.S.) (Grant U54CA112967) National Institutes of Health (U.S.) (Grant R01GN089903) National Science Foundation (U.S.) (Award DB1-0821391) Massachusetts Institute of Technology. Undergraduate Research Opportunities Program 2014-08-21T18:18:15Z 2014-08-21T18:18:15Z 2012-09 2012-03 Article http://purl.org/eprint/type/JournalArticle 1757-9694 1757-9708 http://hdl.handle.net/1721.1/88961 Gosline, Sara J. C., Sarah J. Spencer, Oana Ursu, and Ernest Fraenkel. “SAMNet: a Network-Based Approach to Integrate Multi-Dimensional High Throughput Datasets.” Integr. Biol. 4, no. 11 (2012): 1415. https://orcid.org/0000-0002-2744-8994 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-6534-4774 en_US http://dx.doi.org/10.1039/c2ib20072d Integrative Biology Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Royal Society of Chemistry PMC |
| spellingShingle | Spencer, Sarah J. Ursu, Oana Fraenkel, Ernest Gosline, Sara Jane Calafell SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets |
| title | SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets |
| title_full | SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets |
| title_fullStr | SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets |
| title_full_unstemmed | SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets |
| title_short | SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets |
| title_sort | samnet a network based approach to integrate multi dimensional high throughput datasets |
| url | http://hdl.handle.net/1721.1/88961 https://orcid.org/0000-0002-2744-8994 https://orcid.org/0000-0001-9249-8181 https://orcid.org/0000-0002-6534-4774 |
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