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**170**• written 5.4 years ago by Gordon Smyth ♦

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Question: EdgeR LogCpm and LogFC values calculation

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5.4 years ago by

Gordon Smyth ♦ **37k**

Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

Gordon Smyth ♦ **37k** wrote:

Dear Koen,
> Date: Fri, 31 Jan 2014 21:09:12 +0100
> From: Koen Van den Berge <koen.vdberge at="" gmail.com="">
> To: "P.D. Moerland" <p.d.moerland at="" amc.uva.nl="">
> Cc: "bioconductor at r-project.org" <bioconductor at="" r-project.org="">
> Subject: Re: [BioC] EdgeR LogCpm and LogFC values calculation
>
> Dear Perry,
>
> Thank you for this clarification. I required this information as I
> wanted to plot the change in Log2 Cpm for my various DE genes.
I am having some trouble understanding what you are trying to do. Why
would you need to recompute a value (the average logCPM) that is
already
provided as a column of the topTags output table? The logFC (which is
the
change in log2 cpm) is already provided as well.
Note that the logCPM column of the topTags output represents average
logCPM over all the libraries. This is the quantity used as the
x-axis
for plotBCV() and plotSmear() and other similar functions in edgeR.
> Would you then consider my prior way of Log2 Cpm calculation (first
> calculating cpm through cpm() function, then taking the log2)
If you want log2 CPM for each individual sample, please use
cpm(y, log=TRUE)
rather than
log2(cpm(y))
> as wrong for plotting this certain result as it might not be a very
> correct way in dealing with the count data, or are it still log 2
cpm
> values, but simply calculated in an other fashion and maybe with a
> somewhat different interpretation?
Yes, it makes sense to plot the logCPM values that you get from cpm(),
but
you should view this as being a descriptive plot of the data rather
than
an exact representation of the fitted model. You cannot reproduce the
logFC values from topTags() from them. The edgeR logFC is more
complicated and better.
> Likewise, starting from the code you have provided me with, would
you
> consider
>
> abundance2 <- t(t(y)+prior.count.scaled)
>
> to be more correct information for plotting the log2 cpm values for
DE
> genes; or are these values only relevant when combined with the
> mglmOneGroup() function for calculating the context specific
average?
I really feel that you should not need to hack the edgeR code in this
way.
If the above hasn't already answered your questions, can you describe
more
precisely the plot you are tring to create? Are you trying to plot
the
logCPM for each sample, or for each experimental group, or just the
logFC?
What are you trying to learn?
Best wishes
Gordon
> Thank you in advance,
> Koen Van den Berge
> On 31 Jan 2014, at 14:13, P.D. Moerland <p.d.moerland at="" amc.uva.nl="">
wrote:
>
>> Dear Koen,
>>
>> The source code of the function aveLogCPM (included in the package
>> source of the current release version 3.4.2) shows that the
function
>> mglmOneGroup needs some additional parameters and other default
values
>> than the ones you used:
>>
>> prior.count <- 2
>> dispersion <- 0.05
>> # y is the matrix of the raw counts
>> y <- as.matrix(y)
>> if(is.null(lib.size)) lib.size <- colSums(y)
>> prior.count.scaled <- lib.size/mean(lib.size) * prior.count
>> offset <- log(lib.size+2*prior.count.scaled)
>> abundance <- mglmOneGroup(t(t(y)+prior.count.scaled),dispersion=dis
persion,offset=offset)
>> (abundance+log(1e6)) / log(2)
>>
>> On a small test dataset this gives me the same values as the
AveLogCPM component of a DGELRT object. Could you give this a try?
>>
>> best,
>> Perry
>>
>> ---
>> Perry Moerland, PhD
>> Room J1B-215
>> Bioinformatics Laboratory, Department of Clinical Epidemiology,
Biostatistics and Bioinformatics
>> Academic Medical Center, University of Amsterdam
>> Postbus 22660, 1100 DD Amsterdam, The Netherlands
>> tel: +31 20 5666945
>> p.d.moerland at amc.uva.nl, http://www.bioinformaticslaboratory.nl/
>>
>>
>> -----Original Message-----
>> From: bioconductor-bounces at r-project.org [mailto:bioconductor-
bounces at r-project.org] On Behalf Of Koen Van den Berge
>> Sent: Friday, January 31, 2014 1:08 PM
>> To: bioconductor at r-project.org
>> Subject: [BioC] EdgeR LogCpm and LogFC values calculation
>>
>> Dear Bioconductor mailing list,
>>
>> I am currently researching the differences in RNA-Seq data
analysis,
>> comparing the two well known EdgeR and Voom methods. However, there
is
>> one thing I can not manage to reproduce, namely the logCPM value in
the
>> output of the LRT table of EdgeR, after analyzing a certain
contrast or
>> coefficient. I understand from the manual and helpfile, that this
>> logCPM value is a log2 counts per million, normalized for library
>> sizes. I also understand that it is not a simple mean that is being
>> used, but rather the mglmOneGroup() function. However, when I try
to
>> recalculate this myself, I can not obtain the same value. My
workflow
>> in doing so looks like this:
>>
>> #Calculate through the table
>> counts <- read.delim("file.txt")
>> Treat <- factor(rep(c(rep(c("Cont","DPN","OHT"),4)),each=2))[-19]
>> #Delete removed sample 19 by authors
>>
>> y.s <- DGEList(counts=counts.filtered.smart,group=Treat)
>> y.s <- calcNormFactors(y.s)
>> y.common <- estimateGLMCommonDisp(y, design, verbose=TRUE)
y.trended <- estimateGLMTrendedDisp(y.common, design) y.tagwise <-
estimateGLMTagwiseDisp(y.trended, design,prior.df=22) lrt <-
glmLRT(fit,coef= c(5,6,9,8)) lrt$table$logCPM
>>
>> #Calculate manually
>> cpmstest <- cpm(y, normalized.lib.sizes = TRUE) LogCpmstest <-
log(cpmstest + 0.5, base = 2)
>> mglmOneGroup(LogCpmstest) #not identical
>>
>> mean(LogCpmstest) #not identical
>>
>>
>> mglmOneGroup(y.tagwise) #also non-identical
>>
>> Have you got any recommendations in what should be changed in this
manual calculation workflow? What am I doing wrong?
>>
>> Any help would be greatly appreciated.
>>
>> Sincerely, Koen.
______________________________________________________________________
The information in this email is confidential and
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Koen Van den Berge • **170**
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5.4 years ago by
Gordon Smyth ♦ **37k**

Answer: EdgeR LogCpm and LogFC values calculation

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Koen Van den Berge • **170** wrote:

Dear Gordon,

Thank you for your reply.

Since the LRT table only gives a LogCPM for the average of each gene, my desire was to calculate this for each sample separately, for all genes. In this way, I wanted to create a dot plot - as added in the appendix - which could visually show the changes in expression values for the genes we have found to be significantly differentially expressed (note I had three samples in the control group and four in the treatment group). This is my motivation in reaching out to the individual LogCPM values and since these did not coincide with the ones provided by the table, I was worried. Since the fitted model reproduces a single coefficient for each treatment group, I guess it will not be possible to calculate sample- wise log2 cpms that would be similar to the ones provided by the table, and maybe I should stick to my descriptive log2 cpm plots as provided in the appendix?

Thank you in advance,

Koen

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Since the fitted model reproduces a single coefficient for each treatment group, I guess it will not be possible to calculate sample-wise log2 cpms that would be similar to the ones provided by the table, and maybe I should stick to my descriptive log2 cpm plots as provided in the appendix?

Yes. As you say, the fitted model doesn't estimate sample-wise values. You need to plot the data, not the model.

Gordon

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