RNAseq: Z score, Intensity, and Resources I'm very new to bioinformatics in general, and I'm trying to understand some basic concepts.

I have RNAseq data, and bioinformatics people tell me that intensities cannot be compared across patients. So there are all of these pipelines to compare intensities to Z scores--are those as simple as just plugging data into a bioconductor package?

It would be great to get some overview description of why Z values are important/why you can't compare intensities across patients, and/or some pointers towards any resources that I can read to learn more.

Thanks much for your time!
It depends on what test or analysis you want to do, whether you need intensities (expression values) or z-scores.

If you want to do statistical analysis, such as finding differentially expressed genes between groups of patients (e.g., with limma), you don't want to use z-scores. But you use normalized intensities (for microarrays) or start with raw counts for RNAseq. Then follow the user guide (limma has a great user guide, also for starters).

For visualization in heatmaps or for other clustering (e.g., k-means, fuzzy) it is useful to use z-scores. Z-scores are a form of transformation (scaling), where every genes is sort of "reset" to the mean of all samples, using also the standard deviation. If you want to know exactly what a z-score is, a simple google search can tell you the details. 

In R you can use the scale function for z-score transformation. Be aware that the function works on columns though. Which means you have to transpose your matrix first if genes are in the rows, and then transpose them back after transformation.