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\nMultivariate statistical analysis of transcriptional data: an application to yeast metabolic networks.\nQuantitative transcriptome analysis is a powerful approach to determine the metabolic state and physiological response of living cells. A central challenge of this quantitative transcriptomics approach, which allows the determination of expression of all or almost all of the genes of an organism, is the statistical and computational analysis of the large volume of data it generates. Current transcriptome analysis requires sophisticated, custom-written software to analyze the large data sets produced by high-throughput technologies. Here, we describe a general procedure for the statistical analysis of transcriptome data that does not rely on expensive custom-written software and is independent of a specific organism or technology. Statistical analysis of the transcriptome data sets requires two steps: The first step is normalization of the data using in-house scripts and the second is statistical analysis of the normalized data using Principal Component Analysis (PCA), Hierarchical Clustering, Generalized Linear Models and others. We discuss how PCA is applied to groups of genes, as well as to the data from different experiments. The PCA-based approach that we describe is applicable to many different systems (microorganisms, plants and animals). We demonstrate the use of this PCA-based method to analyze the transcriptome data set from the yeast Saccharomyces cerevisiae grown in different conditions and for different times and compare the results with those obtained from the analysis of the transcriptome data of two Escherichia coli strains grown in different media.These mice live to be between 26 and 42 months old, which is about nine to 13 years in human years, and are not available commercially. I’m fond of big, old mice and often use “sentinel” strains, meaning they live at least 10 years with a continuous supply of aged food. (In the old days when I was doing genetics testing, I used C57BL/6, but these are expensive.)
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