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J Comput Biol. 2009 Feb;16(2):201-12. doi: 10.1089/cmb.2008.07TT.

Learning signaling network structures with sparsely distributed data.

Author information

1
Department of Microbiology and Immunology, Baxter Laboratory in Genetic Pharmacology, Stanford University School of Medicine, Stanford, CA, USA.

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.

PMID:
19193145
PMCID:
PMC3198894
DOI:
10.1089/cmb.2008.07TT
[Indexed for MEDLINE]
Free PMC Article

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