Hi @spraguedawley. Z scores are defined as the number of standard deviations a value is away from the mean expression level, which makes it a good measure of differential expression. So if a gene has higher expression in hippocampus (for example) as compared to the rest of the brain, then the z score will be high regardless of what the average expression is in brain. I would not recommend comparing absolute expression differences between genes at all using microarray data. That said, the log2 expression values would be better to use than z scores for comparing such average expression levels between genes. Differential expression values (e.g., how much higher is gene x in hippocampus than in frontal cortex) are more reliable and can be compared between genes. This thread on data normalization and z scores goes into a bit more detail about some components of this response, but feel free to reply if you still have questions.
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