WMAXC: a weighted maximum clique method for identifying condition-specific sub-network

PLoS One. 2014 Aug 22;9(8):e104993. doi: 10.1371/journal.pone.0104993. eCollection 2014.

Abstract

Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Datasets as Topic
  • Gene Expression Regulation*
  • Gene Regulatory Networks*
  • Humans
  • Protein Interaction Mapping / methods
  • Protein Interaction Maps
  • Reproducibility of Results
  • Signal Transduction

Grant support

This work was supported by the Systems biology infrastructure establishment grant provided by Gwangju Institute of Science & Technology in 2014, and by the MSIP (The Ministry of Science, ICT and Future Planning), Korea and Microsoft Research, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2013-H0503-13-1019). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.