A good question, but I think you should keep the paired normal in. I
think ignoring the pairing will make the test conservative rather than
invalid. Removing the cancerous sample's normal pair, while valid,
be less powerful again, so would not offer an advantage.
Professor Gordon K Smyth,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Tel: (03) 9345 2326, Fax (03) 9347 0852,
smyth at wehi.edu.au
On Wed, 25 May 2011, Timothy Hughes wrote:
> Dear Gordon,
> Thanks you very much for your detailed answer, it has really helped
me get a
> better understanding of DESeq and edgeR.
> I see that I cannot perform a paired analysis with DEseq but can do
> When it comes to getting a grip on the specifics of each cancer
> say that one can compare each cancer to the all normal samples
> pairing between the cancer sample and one of the normals) and you
> suggest a more careful analysis. But what about comparing each
> sample to the group of normals excluding the cancerous sample's
> Would this be a valid approach, superior to including the normal
> less difficult to explain than the "careful" analysis?
> On 25 May 2011 03:09, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote:
>> Dear Tim,
>> From: Timothy Hughes <timothy.hughes at="" medisin.uio.no="">
>>> To: bioconductor at r-project.org
>>> Subject: [BioC] DESeq and paired samples - pairing vs pooling
>>> We are performing a study of 8 individuals with cancer. We have 8
>>> samples. Each pair consists of two samples from the same
>>> from cancerous tissue and one from normal tissue.
>>> We have used DESeq to perform a pooled comparison between the
>>> cancerous samples and find a number of genes that are
>>> We would also like to perform a paired analysis (simple comparison
>>> the two tissue samples from the same individual). Our logic is
>>> pooled analysis will tend to identify genes as differentially
>>> if they are fairly consistently up or down-regulated across
>> Not quite sure what you mean by a pooled analysis in this context.
>> you mean treating the cancer and normal tissue samples as
>> groups. Basically you should perform a paired analysis here,
>> data is naturally paired, and otherwise you will be ignoring the
>> differences between individuals. The DE genes you have found are
>> not wrong, but you have probably missed many others.
>> But, the etiology of the same cancer type may be heterogeneous and
>>> to investigate this by performing the paired analysis.
>> Unfortunately, a paired analysis doesn't give you a way to handle
>> heterogeneity of cancers. A paired analysis will still look for
>> differential expression that is consistent across the patients. It
>> for genes that have more or less consistent relative changes
>> and cancer for each patient. It will find genes that are common to
>> majority of the cancers.
>> In connection with this,
>>> I have two questions:
>>> 1. we read in the DESeq paper that this can be done, but are we
>>> believing that we can interpret the results as I describe above?
>> I wonder where you have read this? I don't think the DESeq authors
>> it handles paired tests.
>> See above for comments on interpretation.
>> 2. Does it make sense to do a paired analysis as described above
>>> make more sense to pool the normal tissues and then compare each
>>> tissue to the pool?
>> If you want to find genes that are specific to one cancer, and not
>> other, nor to the normal tissues, then comparing each individual
>> the group of normals is probably your best route, at least the
>> You could do a standard two-group analysis with n=1 in one of the
>> This does ignore the pairing of the cancer tissue to one of the
>> but, with 8 individuals, the penalty probably isn't too high.
>> I can think of ways to a more careful analysis, but they'd be
>> explain in a publication. Using the edgeR package, you could (i)
>> paired samples model, in order to extract the biological
>> variation (BCV) from all the individuals, then (ii) compare each
>> cancer to its own paired normal tissue, using the BCV previously
>> from all the patients.
>> Of course, plotting the data to see how different the cancer
>> to be should be the first step. I personally use plotMDS.dge() in
>> package for this purpose.
>> Best wishes
>> Thanks for your help.
>>> Tim Hughes PhD (http://digitised.info
>>> Medical Genetics Department
>>> Oslo University Hospital (Ullev?l)
>>> Kirkeveien 166
>>> 0407 Oslo
>>> Tel: (+47) 23 02 72 55
> Tim Hughes PhD (http://digitised.info
> Medical Genetics Department
> Oslo University Hospital (Ullev?l)
> Kirkeveien 166
> 0407 Oslo
> Tel: (+47) 23 02 72 55