title: Reconciling differential gene expression data with molecular interaction networks.
abstract: BACKGROUND
  Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease.
  
  
  RESULTS
  To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone.
  
  
  BACKGROUND
  C++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/∼murali/software/biorithm-docs.
authors: Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.
authors: Christopher L
authors: CL
authors: Poirel
authors: Ahsanur
authors: A
authors: Rahman
authors: Richard R
authors: RR
authors: Rodrigues
authors: Arjun
authors: A
authors: Krishnan
authors: Jacqueline R
authors: JR
authors: Addesa
authors: T M
authors: TM
authors: Murali
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism; D001692:Biological Transport; D001921:Brain / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D005947:Glucose / Q000378:metabolism; D006801:Humans; D006816:Huntington Disease / Q000235:genetics / Q000378:metabolism; D007328:Insulin / Q000502:physiology; D020411:Oligonucleotide Array Sequence Analysis; D025941:Protein Interaction Mapping; D012984:Software
chemical_list: D007328:Insulin; D005947:Glucose