Node sampling for protein complex estimation in bait-prey graphs

Denise M. Scholtens*, Bruce D. Spencer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)391-411
Number of pages21
JournalStatistical Applications in Genetics and Molecular Biology
Volume14
Issue number4
DOIs
StatePublished - Aug 1 2015

Keywords

  • CoIP
  • bait-prey
  • networks
  • protein complexes
  • sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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