Hi guys, 

we are working on an university project where we want to find discriminating genes of different cancer types. For that we are using gene expression data of the TCGA dataset.

A naive approach would be to simply run some feature selection on tumor data of each type. However, we assume that this would not identify genes relevant for the tumor but the cell type itself. For example, we want to compare thyroid and lung cancer. Using only tumor data, we would expect that we find differentially expressed genes that are specific not for the tumor but for the original cell type itself. So we want to "normalize" thyroid tumor data with healthy thyroid tissue to find discriminating genes for thyroid tumor first that can now be compared with "normalized" genes for lung cancer.

We have some ideas how to do this ourselves but we suppose that this is not an uncommon task, so **has anyone heard of this "normalization" approach and how it usually is done**? We suppose that this needs to be done when clustering cancer types to see meaningful differences but we could not find this in the literature we read.

We hope we could state our problem in a comprehensible way, if not, feel free to ask. Thanks for your help!
Hey, you could try:

 1. Obtain healthy / normal tissue specific expression data from one or more online databases ( see
    here: https://www.biostars.org/p/90411/ - also look up *FANTOM5*)
 2. Using these databases, determine which genes are specific to your
    tissues of interest (e.g. thyroid). To do this, just do something
    like converting the downloaded data (preferably it has a normal distribution) to Z-scores. Thus,
    'tissue-specific' genes will have high Z-scores in the
    tissues in which they are most expressed; whereas, ubiquitously
    expressed genes will have low Z-scores

After that, when you conduct your differential expression comparison in tumours between thyroid and lung, you can just filter the list of differentially expressed genes for the genes encountered via the above 2-step approach) - this is the simplistic approach.

There should be a way, however, to actually 'normalise' your tumour expression data based on the results that you obtain from the tissue specific databases. For example, one could supply the healthy / normal tissue-specific data as priors to an empirical Bayesian regression model and then adjust your expression data based on these priors.

Also keep in mind that 'thyroid' and 'lung' refer to many different cell- and tissue-types.

Kevin