the number of trials, is assumed to be Poisson distributed and represents the total number of mRNA’s of a given type in the cells. Here, we extend the model to take into account the multinomial nature of the original mRNA population. The second strategy is presented in Subsection 4.2. This approach proposes that a count T$_{i}$ ∼ Binomial ( g$_{i}$, p ) and that the vector g ∼ Multinomial ( N, m ). We believe this strategy represents the data generating process of SAGE slightly more realistically than the first strategy. Finally, in Subsection 4.3 we consider a missing data strategy. This approach applies a modified version of the conjugate Dirichlet-Multinomial model and is more computationally expedient than the first two strategies. Overall, each of the three strategies offers its own unique strengths and weaknesses, which we discuss below. The algorithms discussed were all applied to a published SAGE data set which analyzed the yeast Saccharomyces cerevisiae (Velculescu et al., 1997). In particular, data collected from the log-phase were analyzed. There were 6096 genes included in the data. The maxi- mum tag count was 392 and the minimum was zero. 3560 of the genes included had observed counts of 0. Gene dependent sampling probabilities ranges from ≈ . 003 to 1. Tag assign- ments to unique genes and assignment of non-unique tags for this analysis were described in Gilchrist et al. (2007). All simulations described below were computed using R (Version 2.3.1) on dual core Intel desktop computer running Linux Fedora 5. In what follows, the symbol T will represent the vector of observed tag counts, φ the known vector of gene dependent sampling probabilities, g a latent vector representing the actual number of mRNA’s from each gene, m the vector of mRNA proportions and N the total mRNA copy number in the cell. Throughout this analysis, we assume that gene dependent tag formation probabilities φ$_{i}$ are known constants. 13