I am working with RNA-seq data from the Cancer Genome Atlas TCGA and I have been reading about how people have compared gene expression levels measured by RNA-seq. Many of the papers I have read talk about "differential expression" for comparing each gene's expression levels in the Experimental and Control conditions.

In TCGA data, I typically have a patient cohort who have had the mRNA in their tumors sequenced just once so there is no Experimental-vs-Control dynamic. I am interested in finding which patients' tumors show gene-expression that is significantly higher than the rest of the cohort but I have not had any luck finding literature describing this kind of comparison. I'm thinking maybe I can apply existing differential expression techniques to my situation but that seems cumbersome and not-necessarily appropriate so thought I'd ask the community here if there's a better way of finding which members of a cohort are outliers for specific genes.

Also: all of my RNA-seq data has already been RPKM normalized for me. I have been advised that using RSEM instead would be better for comparing gene expression across multiple samples but, for logistic reasons, I'm probably stuck with my RPKM-normalized expression levels.

Fundamentally, I'm looking for the best way to compare gene expression across samples to determine which samples have outlying high/low gene expression. Intuitively, I figure I could just compute median z-scores for each gene's expression levels within my cohort and consider anyone with a |z-score| greater than 2 to be an "outlier" but I haven't found any literature to support this kind of approach either.

Any suggestions, papers, or advice will be greatly appreciated.


2 Answers 2


When you say RPKM do you mean crude RPKM or the estimates that you get using expectation maximization methods like cufflinks and eXpress?

It is better if you get your RPKM or FPKM values from one of these programs because you can differentiate between transcript variants.

I have mostly used cufflinks and eXpress. Cufflinks package is better for multiple datasets. You can use cuffquant (which takes SAM/BAM) files to compute FPKM. Cuffquant will also need a reference GTF file. Cuffquant gives a binary .cxb file which you cannot read directly. Once you have generated the .cxb files for all your cohort samples, then pass all these files to cuffnorm. It will normalize the data and give you FPKM values for each gene in each sample in the form of a huge table.

Next point is which genes do you wish to compare. Do you want to compare known oncogenes which show consistent upregulation in all cancers? Actually there is a paper in which they have done this (I'll let you know the reference when I find it. Not able to recollect now).

You can then see how many of these genes show consistent expression in your cohort. Basically you need to identify a set of genes before you go on to study which patient shows anomalous expression.

  • $\begingroup$ I've been supplied with the kind of RPKM values you'd get from the Tuxedo suite but I have no insight into the specific pipeline used. I would love to see a paper that compares RNA-seq data between oncogenes across cancers; please post a link when you get a chance. Also: my collaborators have already identified a set of genes to focus on. $\endgroup$
    – Slavatron
    Aug 14, 2014 at 14:40
  • $\begingroup$ I just don't remember which journal it was but I am sure I read one.. Perhaps it is about miRNAs and oncogene interactions in different cancers. $\endgroup$
    Aug 14, 2014 at 15:42
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    $\begingroup$ Nonetheless if you want to compare differentially expressed from different cancers then you can get it by doing a principal component analysis on the RNAseq data for different cancers from TCGA $\endgroup$
    Aug 14, 2014 at 15:43
  • $\begingroup$ I expect to use PCA later on to see which expression patterns have the greatest impact on clinical data. For the time being though, my focus is comparing a small set of genes across patients who all have the same cancer (I guess I should have included that in my original question). Don't worry about the paper, I never thought to look for papers comparing expression across different cancer types and I've found some useful literature that way. $\endgroup$
    – Slavatron
    Aug 14, 2014 at 15:59

Seems like you want to have a general approach for comparing gene expression signatures.

A recent paper,Clark et al, takes a geometric approach which is elegant. The idea is to do dimensionality reduction (singular value decomposition) of the expression data, and then calculate the cosine distance between the gene expression signatures in the reduced space.

If you apply this methodology you will be able to group together patients with very similar signatures (small distances) and identify outliers (larger distances). Moreover, based on the loadings from the singular value decomposition, you will be able to identify which genes are driving the differences in the measured distances, and thus identify 'relative differentially expressed genes'.


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