[BioC] scholarly reference for "don't draw PCA/heatmap dendrograms on DEGs"
kevin.r.coombes at gmail.com
Mon Dec 9 15:05:22 CET 2013
I don't have a good reference either.
But you can easily simulate matrices full of IID standard normal data,
pick the "most differentially expressed" and show that this
noise/nonsense perfectly separates any two "groups" that you want to
pretend is present in the data.
On 12/9/2013 8:55 AM, Lorena Pantano wrote:
> I don't have any reference to give you.
> But my experience says that you don't get necessary a good heatmap
> separated by two conditions although you use only DE genes. Probably
> because many time,s results from DE genes are not so strong to separate the
> two groups, or because there is a systematically outlier in your comparison
> and get DE genes that are not true, or any other reason.
> I can say that I have done more than 50 DE analysis, and only once, I got a
> clear heatmap showing two groups. So, I guess there is something there.
> very interesting your initiative.
> On Mon, Dec 9, 2013 at 2:19 PM, Aaron Mackey <ajmackey at gmail.com> wrote:
>> A colleague of mine is skeptical of my assertion that drawing sample-level
>> PCA plots and/or clustered heatmaps based only on differentially expressed
>> genes (DEGs) is a circular, self-fulfilling prophecy -- they assert that
>> there's no guarantee samples will cluster by condition (despite the fact
>> that the condition is exactly what drives selection of DEGs), and so hopes
>> to use the observed clustering as further "evidence" of the condition
>> effects. Rather than spend more time trying to explain statistical
>> concepts, I was hoping to checkmate the argument with a nice Nature Methods
>> review or somesuch. Any pointers?
>> Thanks in advance,
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