Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Background: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results: We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so sig...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Ricardo Z. N. Vêncio, Helena Brentani, Diogo F. C. Patrão, Carlos A. de B. Pereira
• Keywords: BMCBI 2004, Gene Expression, Important Challenge, Within-class Variability