Jump to main content
Jump to site search
PLANNED MAINTENANCE Close the message box

Scheduled maintenance work on Wednesday 27th March 2019 from 11:00 AM to 1:00 PM (GMT).

During this time our website performance may be temporarily affected. We apologise for any inconvenience this might cause and thank you for your patience.

Issue 11, 2012
Previous Article Next Article

SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets

Author affiliations


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 proteinprotein 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.

Graphical abstract: SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets

Back to tab navigation

Supplementary files

Publication details

The article was received on 28 Mar 2012, accepted on 26 Aug 2012 and first published on 21 Sep 2012

Article type: Paper
DOI: 10.1039/C2IB20072D
Citation: Integr. Biol., 2012,4, 1415-1427
  • Open access:

Search articles by author