PhD Dissertation Learning from SAGE Data
نویسنده
چکیده
Serial Analysis of Gene Expression (SAGE) is a technology for measuring quantitatively the expression levels of all genes in a cell population simultaneously. In this thesis, I study what we could not learn, what we could learn, and how to learn what we could learn, from SAGE data. The first chapter of this thesis is a short introduction of the SAGE technology and another popular technology for measuring gene expression levels — the microarray technology. In the second chapter I show that, because all the current technologies can only measure the summed expression levels of genes from an aggregate of cells, in principle, we cannot learn the conditional independence relations among the expression levels of the genes in a single cell. Furthermore, the number of experiments required to estimate the correlation matrix of the gene expression levels is also too large to be feasible. The only feature of the expression levels of the genes we can learn reliably in practice is their expectations. Hence no algorithm of learning the gene regulatory network based on the conditional independence relations or the partial correlations among the expression levels of the genes could work with the data generated by the current technologies. iii
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