6. Conclusions
In this chapter we aimed to give a guide to the state-of-art in statistical methods for SAGE analysis. We just scratch some issues for the sake of being focused in differential expression detection problems, but we hope that main ideas could be useful to track the original literature. We saw that estimation of a tag abundance could not be simpler than observed counts divided by sequenced total, but rather can receive sophisticated treatments such as multinomial estimation, correction of potential sequencing errors, a priori knowledge incorporation, and so on. Given an (assumed) error-corrected data set, one could search for differentially expressed tags among conditions. Several methods for this were mentioned, but we stress the importance of using biological replication designs to capture general information. Finally, we want to point out that only accumulation of experimental data in public databases, with biological replication, and use of good statistics could improve usefulness of SAGE, MPSS or EST counting data in general terms, helping to elucidate basic/applied gene expression questions.
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Vêncio, R.Z.N., Brentani, H. (2006). Statistical Methods in Serial Analysis of Gene Expression (Sage). In: Zhang, W., Shmulevich, I. (eds) Computational and Statistical Approaches to Genomics. Springer, Boston, MA. https://doi.org/10.1007/0-387-26288-1_11
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DOI: https://doi.org/10.1007/0-387-26288-1_11
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