Entering edit mode
Alvaro J. González
▴
80
@alvaro-j-gonzalez-5813
Last seen 10.3 years ago
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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