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

BMC Bioinformatics. 2004 Aug 31;5:119. doi: 10.1186/1471-2105-5-119.


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 significant if one accounts for it.

Conclusion: Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Astrocytoma / genetics
  • Bayes Theorem
  • Brain Chemistry / genetics
  • Brain Neoplasms / genetics
  • Computational Biology / statistics & numerical data
  • Databases, Genetic
  • Gene Expression Profiling / statistics & numerical data*
  • Gene Expression Regulation, Neoplastic / genetics
  • Genetic Variation / genetics*
  • Humans
  • Models, Genetic*
  • Oligonucleotide Array Sequence Analysis / statistics & numerical data*
  • Research Design