Real-time gene expression: Statistical challenges in design and inference

David Gold, Bani Mallick, Kevin Coombes

Research output: Contribution to journalArticlepeer-review


Advances in microtechnologies are making it possible for high-throughput control and reporting of gene expression in live cells, in real-time. We explore relevant statistical challenges to modeling and inference in real-time gene expression data from single-shock experiments, with special attention on potential confounding between treatment and cell cycle variation. We propose a semi-wavelet non-linear dynamic regression model to infer modulation in gene expression due to treatment shocks in the presence of cell cycle variation. A case study is performed with public data. Results are compared ignoring cell cycle. Estimation and inference are performed by a Bayesian approach.

Original languageEnglish (US)
Pages (from-to)611-623
Number of pages13
JournalJournal of Computational Biology
Issue number6
StatePublished - Jul 1 2008
Externally publishedYes


  • Cancer genomics
  • DNA arrays
  • Gene expression
  • Statistics

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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