title: Probabilities of spurious connections in gene networks: application to expression time series.
abstract: BACKGROUND
  The reconstruction of gene networks from gene-expression microarrays is gaining popularity as methods improve and as more data become available. The reliability of such networks could be judged by the probability that a connection between genes is spurious, resulting from chance fluctuations rather than from a true biological relationship.
  
  
  RESULTS
  Unlike the false discovery rate and positive false discovery rate, the decisive false discovery rate (dFDR) is exactly equal to a conditional probability without assuming independence or the randomness of hypothesis truth values. This property is useful not only in the common application to the detection of differential gene expression, but also in determining the probability of a spurious connection in a reconstructed gene network. Estimators of the dFDR can estimate each of three probabilities: (1) The probability that two genes that appear to be associated with each other lack such association. (2) The probability that a time ordering observed for two associated genes is misleading. (3) The probability that a time ordering observed for two genes is misleading, either because they are not associated or because they are associated without a lag in time. The first probability applies to both static and dynamic gene networks, and the other two only apply to dynamic gene networks.
authors.affiliation: Office of Biostatistics and Bioinformatics, Medical College of Georgia, Augusta, GA 30912-4900, USA. bickel@prueba.info
authors.forename: David R
authors.initials: DR
authors.lastname: Bickel
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D013997:Time Factors; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D014157:Transcription Factors