Node sampling for protein complex estimation in bait-prey graphs

Scholtens Denise M. () and Spencer Bruce D.
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Scholtens Denise M.: Northwestern University Feinberg School of Medicine, Division of Biostatistics, Department of Preventive Medicine, 680 N. Lake Shore Drive Suite 1400, Chicago, IL 60611, USA
Spencer Bruce D.: Institute for Policy Research, Department of Statistics, Northwestern University, 2006 Sheridan Road, Evanston, IL 60208, USA

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 4, 391-411

Abstract: In cellular biology, node-and-edge graph or “network” data collection often uses bait-prey technologies such as co-immunoprecipitation (CoIP). Bait-prey technologies assay relationships or “interactions” between protein pairs, with CoIP specifically measuring protein complex co-membership. Analyses of CoIP data frequently focus on estimating protein complex membership. Due to budgetary and other constraints, exhaustive assay of the entire network using CoIP is not always possible. We describe a stratified sampling scheme to select baits for CoIP experiments when protein complex estimation is the main goal. Expanding upon the classic framework in which nodes represent proteins and edges represent pairwise interactions, we define generalized nodes as sets of adjacent nodes with identical adjacency outside the set and use these as strata from which to select the next set of baits. Strata are redefined at each round of sampling to incorporate accumulating data. This scheme maintains user-specified quality thresholds for protein complex estimates and, relative to simple random sampling, leads to a marked increase in the number of correctly estimated complexes at each round of sampling. The R package seqSample contains all source code and is available at http://vault.northwestern.edu/~dms877/Rpacks/.

Keywords: bait-prey; CoIP; networks; protein complexes; sampling (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2015-0007

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