[BioC] edgeR: Analyze mini-time-series MeDIP data of pooled DNAs without replicates

Gordon K Smyth smyth at wehi.EDU.AU
Fri Aug 8 05:26:11 CEST 2014

Dear Vang Quy Le,

I don't have any experience analysing MeDIP data myself, so I will assume 
that you know how to generate counts from MeDIP that are suitable for 
edgeR, and I will just answer the linear modelling question.

I also assume that you have read the section of the edgeR User's Guide 
called "What to do if you have no replicates."

In terms of an edgeR analysis, you have two main choices.  First, you 
could simply omit the dispersion estimation steps of the edgeR pipeline 
and run glmFit() and glmLRT() with a manually set value for the 
dispersion.  You have already mentioned this possibility.  There is no 
problems with this except that the number of DE genes will be highly 
dependent on the dispersion value you choice.  Nevertheless, it is much 
better than assuming Poisson variability.  We took this approach for our 
paper in Cell Reports:


Second, you could manufacture residual degrees of freedom by fitting a 
smooth curve to the time course trends.  This approach is explained the 
section called "Many time points" in the limma User's Guide.  We took this 
approach to analyse the development stages of Drosophila melanogaster in 
the voom paper:


Personally, I would take the second approach.  I would fit an orthogonal 
quadratic polynomial to the time trend for the Control and Treated groups 
separately.  This will allow you to use the complete edgeR pipeline 
including dispersion estimation.  This will allow you to test for time 
trends for each gene in each of the groups.  It will also allow you 
examine at which time point each gene has its peak expression.  See the 
analysis in the voom paper.

Best wishes

> Vang Quy Le / Region Nordjylland vql at rn.dk
> Wed Aug 6 10:19:58 CEST 2014

> Hello edgeR users,
> I have been checking around to find out how to best analyze our data as well 
> as remedy our experiment design. It's hard to decide so I will try my best to 
> explain what we have, what we want in a rather long message. Please bear with 
> me.
> We did experiments with two groups of test animals namely treated "T" and 
> control "C" (without treatment) groups. Bot T and C have 20 subjects each. We 
> took samples 4 times with 2-week intervals. For example, at week 2, we 
> extracted DNA samples from 5 subjects in the C group and 5 subjects in T 
> group. At week 4, we extracted DNA for each group from each 5 other subjects. 
> And so on. However, before MeDIP-seq we pooled all 5 DNA samples of the same 
> group and same week together to save time and money. This is like making 
> direct biological everage. So the sequencing samples look like this:
>> targets
>   Week Control Treated
> 1 week2      C2      T2
> 2 week4      C4      T4
> 3 week6      C6      T6
> 4 week8      C8      T8
> Where: C2 is the DNA pool of 5 control subjects at Week2; T2 is the DNA pool 
> of  5 treated subjects at Week 2 and so on. We then subject the pools through 
> MeDIP-Seq protocols and sequence them on NGS platform (color-space).
> 1. Which genes are hypo/hyper-methylated in response to our treatment?
> 2. How does methylation rate/status change from one time point to another 
> (i.e. which one responded early, which one responded later)?
> I have looked through R's package, MEDIPS. But it is not intended for our 
> case because it does not have functions to handle time-series data. It also 
> relies very much on edgeR to do its job. So I think it would be more flexible 
> to use edgeR directly myself. Reading edgeRUsersGuide.pdf, I found that our 
> case is similar to the example in Section "3.3 Treatment effects over all 
> times". However, we don't have replicates the way edgeR expects.
> 1. Can we find the answers for our research questions based on the current 
> data by using edgeR? And how, in high-level view?
> 2. I am thinking since we have the pools (biological mean) we can somehow 
> skip some statistics treatment (i.e. relax p.value, set dispersion value to 
> something reasonable) and get on with the workflow on Section 3.3. Will this 
> be alright in terms of data analysis practice and edgeR expectations?
> 3. If we must do something from sequencing step, what would be the most 
> economical and time-saveing things to do?
> 4. Would you suggest anything else to make the best out of this case?
> Thank you for your time.
> Kind regards,
> Vang Quy Le
> Bioinformatician, Molecular Biologist, PhD
> +45 97 66 56 29
> vql at rn.dk
> Section for Molecular Diagnostics,
> Clinical Biochemistry
> Reberbansgade
> DK 9000 Aalborg
> www.aalborguh.rn.dk

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