Hi everyone,

I am using the TCGA portal to get mRNA expression data for various cancer studies (e.g. lung, liver, thyroid etc). I have two questions about the data:

1. Some cancer studies on TCGA have "mRNA expression RNASeq V2 RSEM" values & corresponding "z-scores". I am confused as to what the "mRNA expression z-Scores (RNA Seq V2 RSEM)" data constitutes of. How are the z-scores calculated and what do they represent?
2. We have been on a lookout for control dataset for the cancer studies on TCGA. Does anyone know of a good place where you can find control dataset for tissues like Lung, Liver, Thyroid etc. (basically all the fore-gut tissues)? We are working with control data from [GTEx][1] but they have RPKM values & TCGA has RSEM/RSEM z-scored values, so we have to do a lot of scaling/normalization/transformation to compare these disparate datasets. We would like to know if there is any mRNA expression data (obtained via RNASeq V2 RSEM) for controls.

Thanks!

UPDATE: I have posted the second part as a separate question [here][2].

 [1]: http://www.gtexportal.org/home/
 [2]: https://www.biostars.org/p/107594/
I will attempt to answer part 1 of your question:

A z-score for a sample indicates the number of standard deviations away from the mean of expression in the reference. The formula is:

z = (expression in tumor sample - mean expression in reference sample) / standard deviation of expression in reference sample

TCGA states:

> For mRNA and microRNA expression data, we typically compute the relative expression of an individual gene and tumor to the gene's expression distribution in a reference population. **That reference population is either all tumors that are diploid for the gene in question, or, when available, normal adjacent tissue.** 

(It is not always clear what the cell of origin of a tumor is, so the mRNA expression in normal adjacent tissue can sometimes be misleading, which is why expression is sometimes compared within the set of tumors only).

As for part 2, CPM (count per million) data for each gene and sample would be ideal for the cross-sample comparisons, but I am not sure where you could get such data. Maybe you should post this as a separate question?