Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR

  • Authors:

  • Sangeeta B. English;Shou-Ching Shih;Marco F. Ramoni;Lois E. Smith;Atul J. Butte

  • Affiliations:

  • Stanford Center for Biomedical Informatics Research (BMIR), Stanford University School of Medicine, 251 Campus Drive, Stanford CA, 94305, USA;Pathology Department, Beth Israel Deaconess Medical Center Research North, 99 Brookline Avenue, Boston, MA 02215, USA;Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology and Harvard-Partners Center for Genetics and Genomics, Harvard Medical School, 77 Avenue Louis Paste ...;Department of Ophthalmology, Children's Hospital Boston, and Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA;Stanford Center for Biomedical Informatics Research (BMIR), Stanford University School of Medicine, 251 Campus Drive, Stanford CA, 94305, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2009

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Abstract

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.