Hi Alvaro/Gordon,
Please accept my sincere apologies for late reply. I did not see this email
until midnight today, until I searched as I was suspicious about not
getting a reply from this very active mailing list. Not that I take that
for granted, but, sounded bit unusual. I am still not sure how It escaped
my eyes.

Thanks very very much for the suggestions.

Gordon: Yes, we expect same RNA gets translated with different efficiencies
at different life cycles. Of course, the effect may not be as big as
differential gene expression itself (we hope).

I was trying something along the lines of what Gordon suggested, but with
out taking logs. For each of the three replicates in each group, i
calculate ratios (efficiencies). Then just do a T-Test to see if ratios are
significantly different between each group. I was simply using rowttest()
from genefilter package. I will try limma. (with log of ratios rather than
just ratios).

Alvaro: I am still trying to understand the plots you are suggesting. I
will get back to you after a giving a bit of thought to it.

I really really appreciate your efforts.

Thanks,
Gowthaman




On Wed, May 1, 2013 at 6:47 AM, Alvaro J. González <
alvaro.gonzalez4@gmail.com> wrote:

> Dear Gowthaman,
>
> My recommendation in a previous message (below) was directed at you. Sorry
> that my message reads as if it were being directed at Gordon.
>
> Your experimental scenario is an interesting one and it would be nice if
> you keep us posted on how you end up tackling it. I think the scatter plot
> that I suggested and the interaction test that Gordon suggested are worth
> pursuing.
>
> Best wishes,
>
> Alvaro J. Gonzalez, PhD
> Computational Biology
> Memorial Sloan-Kettering Cancer Center
> New York, NY
>
> *#### original message ####*
> *[BioC] edgeR: Using ratios (translational efficiencies) as input*
> *Gordon K Smyth smyth at wehi.EDU.AU *
> *Wed May 1 02:36:02 CEST 2013*
> *Dear Alvaro,*
> *
> *
> *Well, honestly I don't know.  My naive and literal reading of the original
> *
> *email was that the interest was in "differential efficiencies between *
> *groups".  In your example, the ratio of ribosomal-bound RNA to normal mRNA
> *
> *is identical for the two groups, hence my naive interpretation is that *
> *there no evidence for "differential efficiencies" between life stages 1 *
> *and 2.*
> *
> *
> *Now that I see your interpretation of the data, I see that one could test
> *
> *for "differential efficiency" simply by using an interaction test in *
> *edgeR.*
> *
> *
> *However I have no experience of this type of analysis, and I don't know *
> *what is scientifically sensible.  Making good plots is always a good way *
> *to go, but send suggestions to the original poster.  It's his problem, not
> *
> *mine!*
> *
> *
> *Best wishes*
> *Gordon*
> *
> *
> *--------------- original message --------------*
> *[BioC] edgeR: Using ratios (translational efficiencies) as input*
> *Alvaro J. Gonzlez alvaro.gonzalez4 at gmail.com*
> *Mon Apr 29 15:34:47 CEST 2013*
> *
> *
> *But Gordon,*
> *
> *
> *Isn't it the case that if you feed logs of ratios into limma you're *
> *automatically losing the statistical significance of those ratios, as well
> *
> *as the absolute expression in each condition, which can be relevant?*
> *
> *
> *For instance, define "t" as one of Gowthaman's transcripts. As far as I *
> *understand, he has four RNAseq libraries measuring the activity of this *
> *transcript:*
> *
> *
> *1) Transcripts from normal mRNA:*
> *     1.1) Life stage 1, his transcript gets t_1.1 = 4 reads.*
> *     1.2) Life stage 2, his transcript gets t_1.2 = 2 reads.*
> *2) Transcripts from ribosome-bound RNAs:*
> *     2.1) Life stage 1, his transcript gets t_2.1 = 100 reads.*
> *     2.2) Life stage 2, his transcript gets t_2.2 = 50 reads.*
> *
> *
> *Let's say edgeR being applied to the two 1) conditions produces:*
> *
> *
> *log2(t_1.1/t_1.2) = log2(4/2) = 1 with adjP = 0.5, meaning, it seems like*
> *the transcript was differentially overexpressed in life stage 1, but with*
> *no statistical significance, so we're not really sure.*
> *
> *
> *Then you do the same with the two 2) conditions:*
> *
> *
> *log2(t_2.1/t_2.2) = log2(100/50) = 1 with adjP < 0.01, so you really*
> *believe the transcript was overexpressed in life stage 1.*
> *
> *
> *Now you feed those two logFCs into limma (1 and 1), and of course, you get
> *
> *nothing out, in terms of differential behavior. But the reality is that *
> *there was a huge change between normal and ribosomal RNAs which was *
> *diluted by the use of the ratios.*
> *
> *
> *What do you think?*
> *
> *
> *My suggestion, just to start, would be to produce a scatter plot of*
> *logFC(normal RNA) vs logFC(ribosomal RNA), and to encode adjP values in*
> *both axes: say for instance by using colors in the x-axis (red is*
> *significant, green is not), and using dot shapes in the y-axis (star is*
> *significant, dot is not).*
> *
> *
> *This plot should show you those transcripts in which interesting stuff is*
> *going on.*
> *
> *
> *Regards,*
> *
> *
> *- Alvaro.*
> *
> *
> *> Dear Gowthaman,*
> *>*
> *> I'm not quite sure what translational efficiencies are.  Do you have a *
> *> different efficiency value for each gene and each RNA sample?  If you *
> *> do, why not take logs of the ratios (offsetting counts by 1/2 or 1 to *
> *> avoid zeros) and feed them into limma?*
> *>*
> *> Best wishes*
> *> Gordon*
> *>*
> *>>*
> *>> Hi Everyone,*
> *>> I have been using edgeR for the last couple years with great success.*
> *>> Thanks very much. Now I have slightly unconventional dataset to try.
> We*
> *>> have two groups to compare (life stages) each with three replicates. *
> *>> But,*
> *>> for each sample in each group, we made two different RNAseq libraries.*
> *>> 1)  one from fragmented mRNA (classical RNAseq) and*
> *>> 2)  another from Ribosome-bound RNA fragments. This library would*
> *>> indicate how much of the RNA is actively being translated.*
> *>>*
> *>> I have used edgeR to analyse data from each of this separately (data *
> *>> from classical RNAseq or Ribosome-bound). So this let us study the *
> *>> differentially transcribed genes or differentially translated genes. *
> *>> And got really nice results.*
> *>>*
> *>> The next step is to compare the translational efficiencies between *
> *>> them. In each sample the ratio between read counts of Ribosome bound *
> *>> mRNAs and fragmented mRNA would give us the translational efficience of
> *
> *>> that gene. We can generate these efficiences (ratios) for each of the *
> *>> three replicates in each group. Can I feed this data to edgeR to find *
> *>> out which genes have 'differential efficiencies' between groups?*
> *>>*
> *>> I understand, edgeR insists on NOT normalizing the read counts and all
> *
> *>> the further statistics depends on the total library size count. By, *
> *>> using ratios, i completely throw edgeR off. But, i am not sure what is
> *
> *>> the best alternate to this?*
> *>>*
> *>> Any ideas?*
> *>>*
> *>> Much thanks in advance,*
> *>> Gowthaman*
>
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>
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-- 
Gowthaman

Bioinformatics Systems Programmer.
SBRI, 307 West lake Ave N Suite 500
Seattle, WA. 98109-5219
Phone : LAB 206-256-7188 (direct).

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