Learning signaling network structures with sparsely distributed data
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Lauffenburger_Learning Signaling cmb.2008.07TT.pdf
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Author(s)
Sachs, Karen
Itani, Solomon
Carlisle, Jennifer
Nolan, Garry P.
Pe'er, Dana
Lauffenburger, Douglas A.
Date Issued
February 2009
Journal
Journal of Computational Biology
Publisher
Mary Ann Liebert, Inc.
Citation
Sachs, Karen et al. “Learning Signaling Network Structures with Sparsely Distributed Data.” Journal of Computational Biology 16.2 (2010): 201-212. © 2009 Mary Ann Liebert, Inc.
Version
Final published version
Abstract
Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs “Markov neighborhoods” for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1089/cmb.2008.07TT
