Bias correction and Bayesian analysis of aggregate counts in SAGE libraries

Background: Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism’s transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3’ most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results: Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context. Conclusions: Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression. Background Tag-based transcriptome sequencing libraries consist of a collection of short sequences of DNA called tags along with tabulated counts of the number of times each tag is observed in a sample. These observed tag counts represent a sample from a much larger pool of mRNA tags in a tissue or organism. In the past, Serial Analysis of Gene Expression (SAGE) was the most commonly used technology for generating libraries of tag counts. SAGE libraries have been used to address a number of biological questions including: estimating transcriptome size, and estimating the density of relative expression levels. Most frequently, SAGE was used to assess differential expression across cells from different tissues or strains, or cells grown under different experimental conditions. Next generation methods such as Digital Gene Expression (DGE) tag profiling [1] now provide a more efficient method to generate tag libraries and are growing in popularity. Libraries based on DGE are used to address the same questions and provide much larger numbers of tags leading to increased statistical power. However, the close similarities between DGE and SAGE, particularly the use of restriction enzymes, lead both techniques to share the same inherent biases. As has been repeatedly noted in both SAGE and DGE studies e.g. [1-4], the mRNA from a single gene can lead to the production of more than one type of tag within the same library. Observing multiple tags from a single gene complicates data analysis and has a number of potential causes including alternative mRNA splicing and incomplete cDNA digestion. Incomplete digestion typically