[BioC] detecting oscillating genes from RNA-seq data
Gordon K Smyth
smyth at wehi.EDU.AU
Thu Apr 3 05:16:03 CEST 2014
Dear Mali,
It is easy to do such an analysis directly in edgeR.
Suppose for example that you are looking for a daily cycle, and that you
have at least half a dozen time points spanning more than a full day. The
method is to create basis vectors for a periodic expression pattern.
Suppose that 'time' is time in hours:
s1 <- sin(2*pi*time/24)
c1 <- cos(2*pi*time/24)
design <- model.matrix(~s1+c1)
This will give a design matrix with three columns: one for the intercept
and two for the periodic signal. To test for a periodic cycle:
fit <- glmFit(y,design)
lrt <- glmLRT(fit,coef=2:3)
If you have lots of time points and you want to make a more complex curve
you can add a harmonic:
s2 <- sin(4*pi*time/24)
c2 <- cos(4*pi*time/24)
design <- model.matrix(~s1+c1+s2+c2)
There are now four coefficients parametrizing the periodic curve. (These
can be viewed as representing amplitudes and phase shifts for the two
harmonics.) I think you would seldom want more than two harmonics for
RNA-seq data.
This method can easily be adapted to find cell-cycle genes and so on.
Best wishes
Gordon
> Date: Tue, 1 Apr 2014 14:33:27 +0200
> From: mali salmon <shalmom1 at gmail.com>
> To: "bioconductor at r-project.org" <bioconductor at r-project.org>
> Subject: [BioC] detecting oscillating genes from RNA-seq data
>
> Hello List
> I have RNA-seq data from different time points and I would like to find
> oscillating genes.
> I thought of using the "cycle" package (which is based on Fourier score) ,
> but I'm not sure what values to use: FPKM or DESeq/edgeR normalized values.
> Any suggestion what would be more appropriate?
> Thanks
> Mali
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