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

ABSTRACT:

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 compariso

SUBMITTER: Vencio RZ 

PROVIDER: S-EPMC517707 | biostudies-literature | 2004 Aug

REPOSITORIES: biostudies-literature