Thank you all for your opinions.

Alicia, can you give me some tips on how are you thinking of doing that?

Best regards,

On Mon, May 2, 2011 at 12:36 PM, Alicia Oshlack <oshlack@wehi.edu.au> wrote:

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> Hi,
>
> Just to get back to the original question I tend to agree with Wolfgang.
> If you are looking for correlations between genes the correlations will
> be length biased with longer gene pairs getting higher correlation
> values. This is a different test to differential expression. I believe
> we can correct the correlation estimation itself rather than correcting
> expression values using something like RPKM. I believe that using RPKM
> will not remove length bias in correlations between genes like it does
> not remove length bias in DE testing. We are currently working on a way
> to correct correlations.
>
> Cheers,
> Alicia
>
>
> > Date: Sun, 1 May 2011 21:34:43 -0400 (EDT)
> > From: ywchen@jimmy.harvard.edu
> > To: "Wei Shi" <shi@wehi.EDU.AU>
> > Cc: "bioconductor@r-project.org list" <bioconductor@r-project.org>
> > Subject: Re: [BioC] RNASeq: normalization issues
> > Message-ID:
> >       <51046.155.52.45.41.1304300083.squirrel@roaming.dfci.harvard.edu>
> > Content-Type: text/plain;charset=iso-8859-1
> >
> > Thanks.
> >> Hi Yiwen:
> >>
> >>      It is a single factor experiment with six libraries. There were
> four
> >> cell
> >> types in this experiment, one of which had three replicates and others
> >> did not have replicates.
> >>
> >> Cheers,
> >> Wei
> >>
> >> On May 2, 2011, at 11:21 AM, ywchen@jimmy.harvard.edu wrote:
> >>
> >>> Hi Wei and Davis,
> >>>
> >>> Thank you so much for such detailed explanations! Now it is very
> >>> clear.
> >>> In your case you found the benefit of using quantile
> >>> normalization+GLM+LRT,
> >>> is it single factor with many libraries or multiple factor data?
> >>>
> >>> Yiwen
> >>>
> >>>
> >>>> Hi Yiwen:
> >>>>
> >>>>    As Davis said, the "length+quantile" method I mentioned in the
> >>>> previous
> >>>> correspondences is not the "quantile normalization" option in
> >>>> calcNormFactors function in edgeR. That's the reason why you didn't
> >>>> see
> >>>> gene length adjustment with that function.
> >>>>
> >>>>    Adjusting read counts using gene length (total exon length) will
> >>>> put
> >>>> all
> >>>> genes on the same baseline within the sample (longer transcripts
> >>>> produce
> >>>> more reads), and quantile between-sample normalization will make all
> >>>> samples have the same read count distribution (and library size will
> >>>> become the same as well). This is what I mean by "length+quantile"
> >>>> normalization. The quantile normalization here is the same quantile
> >>>> normalization applied to microarray data, however it is applied to
> >>>> sequencing data in a different way (used as offsets in the general
> >>>> linear
> >>>> model).
> >>>>
> >>>>    Now I elaborate how to do this normalization. Suppose you have a
> >>>> read
> >>>> count matrix of "x" with rows being genes and columns being samples
> >>>> .
> >>>> Also suppose you have a numeric vector "gene.length" which includes
> >>>> total
> >>>> exon length for each gene and gene order in "gene.length" is the
> >>>> same
> >>>> with that in "x". The following line of code yields the number of
> >>>> reads
> >>>> per 1000 bases for each gene:
> >>>>
> >>>> x1 <- x*1000/gene.length
> >>>>
> >>>> Now perform quantile normalization for gene length adjusted data:
> >>>>
> >>>> library(limma)
> >>>> x2 <- normalizeBetweenArrays(x1,method="quantile")
> >>>>
> >>>> Suppose x has two columns named "wt" and "ko". Create a design
> >>>> matrix:
> >>>>
> >>>> snames <- factor(c("wt","ko"))
> >>>> design <- model.matrix(~snames)
> >>>>
> >>>> Now get the offsets for each gene in each sample. The offsets are
> >>>> the
> >>>> intensity differences between raw data and normalized data.
> >>>>
> >>>> library(edgeR)
> >>>> y <- DGEList(counts=x,group=colnames(x))
> >>>> lowcounts <- rowSums(x)<5
> >>>> offset <- log(x[!lowcounts,]+0.1)-log(x2[!lowcounts,]+0.1)
> >>>> yf <- y[!lowcounts,]
> >>>>
> >>>> Fit general linear models to read count data with offsets included:
> >>>>
> >>>> y.glm <- estimateCRDisp(y=yf,design=design,offset=offset,trend=TRUE,
> >>>> tagwise=TRUE)
> >>>> fit <-
> >>>>
> glmFit(y=y.glm,design=design,dispersion=y.glm$CR.tagwise.dispersion,offset=offset)
> >>>>
> >>>> Perform likelihood ratio tests to find differentially expressed
> >>>> genes:
> >>>>
> >>>> DE <- glmLRT(y.glm,fit)
> >>>> dt <- decideTestsDGE(DE)
> >>>> summary(dt)
> >>>>
> >>>> Hope this will work for you!
> >>>>
> >>>> Cheers,
> >>>> wei
> >>>>
> >>>>
> >>>>
> >>>> On May 2, 2011, at 8:52 AM, Davis McCarthy wrote:
> >>>>
> >>>>> Hi Yiwen
> >>>>>
> >>>>> The "quantile normalization" option in calcNormFactors in edgeR
> >>>>> does
> >>>>> something very different from the quantile normalization
> >>>>> (microarray-style) that Wei has been discussing.
> >>>>>
> >>>>> The quantile normalization in calcNormFactors computes an offset
> >>>>> for
> >>>>> sequencing library depth after Bullard et al (2010) [1]. This is an
> >>>>> approach in the same vein as TMM normalization [2] or scaled median
> >>>>> [3].
> >>>>>
> >>>>> I believe that the approach that Wei is suggesting is more similar
> >>>>> to
> >>>>> the quantile normalization approach that has been taken with
> >>>>> microarray
> >>>>> data, adjusting the data so that the response follows the same
> >>>>> distribution across (in this context) sequenced libraries. This
> >>>>> will
> >>>>> typically result in non-integer data from adjusting counts, but
> >>>>> count-based methods could still be used if this quantile
> >>>>> normalization
> >>>>> were treated as an offset for each observation in (e.g.) a
> >>>>> generalized
> >>>>> linear model.
> >>>>>
> >>>>> Cheers
> >>>>> Davis
> >>>>>
> >>>>>
> >>>>> [1] http://www.biomedcentral.com/1471-2105/11/94
> >>>>> [2]
> >>>>> http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2864565/?tool=pubmed
> >>>>> [3] http://genomebiology.com/2010/11/10/R106#B13
> >>>>>
> >>>>>
> >>>>>> Hi Wei,
> >>>>>>
> >>>>>> Could you elaborate on how to appropriately do
> >>>>>> gene-length-adjusted
> >>>>>> quantile normalization in edgeR? The "quantile normalization"
> >>>>>> option
> >>>>>> in
> >>>>>> calcNormFactors function does not seem to take into account the
> >>>>>> gene
> >>>>>> length.
> >>>>>>
> >>>>>> Thanks.
> >>>>>> Yiwen
> >>>>>>> Hi Jo?o:
> >>>>>>>
> >>>>>>>         Maybe you can try different normalization methods for your
> data
> >>>>>>> to
> >>>>>>> see
> >>>>>>> which one looks better. How to best normalize RNA-seq data is
> >>>>>>> still
> >>>>>>> of
> >>>>>>> much debate at this stage.
> >>>>>>>
> >>>>>>>         You can try scaling methods like TMM, RPKM, or 75th
> percentile,
> >>>>>>> which
> >>>>>>> as
> >>>>>>> you said normalize data within samples. Or you can try quantile
> >>>>>>> between-sample normalization (read counts should be adjusted by
> >>>>>>> gene
> >>>>>>> length first), which performs normalization across samples. You
> >>>>>>> can
> >>>>>>> try
> >>>>>>> all these in edgeR package.
> >>>>>>>
> >>>>>>>         From my experience, I actually found the quantile method
> >>>>>>> performed
> >>>>>>> better
> >>>>>>> for my RNA-seq data. I used general linear model and likelihood
> >>>>>>> ratio
> >>>>>>> test in edgeR in my analysis.
> >>>>>>>
> >>>>>>>         Hope this helps.
> >>>>>>>
> >>>>>>> Cheers,
> >>>>>>> Wei
> >>>>>>>
> >>>>>>> On Apr 28, 2011, at 7:36 PM, Jo?o Moura wrote:
> >>>>>>>
> >>>>>>>> Dear all,
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> Until now I was doing RNAseq DE analysis and to do that I
> >>>>>>>> understand
> >>>>>>>> that
> >>>>>>>> normalization issues only matter inside samples, because one can
> >>>>>>>> assume
> >>>>>>>> the
> >>>>>>>> length/content biases will cancel out when comparing same genes
> >>>>>>>> in
> >>>>>>>> different
> >>>>>>>> samples.
> >>>>>>>> Although, I'm now trying to compare correlation of different
> >>>>>>>> genes
> >>>>>>>> and
> >>>>>>>> so,
> >>>>>>>> this biases should be taken into account - for this is there any
> >>>>>>>> better
> >>>>>>>> method than RPKM?
> >>>>>>>>
> >>>>>>>> My main doubt is if I should also take into acount the biases
> >>>>>>>> inside
> >>>>>>>> samples
> >>>>>>>> and to do that is there any better approach then TMM by Robinson
> >>>>>>>> and
> >>>>>>>> Oshlack
> >>>>>>>> [2010]?
> >>>>>>>>
> >>>>>>>> Thank you all,
> >>>>>>>> --
> >>>>>>>> Jo?o Moura
> >>>>>>>>
> >>>>>>>>        [[alternative HTML version deleted]]
> >>>>>>>>
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> >>>>>>>
> ______________________________________________________________________
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> >>>>>
> >>>>>
> >>>>> --------------------------------------------------
> >>>>> Davis J McCarthy
> >>>>> Research Technician
> >>>>> Bioinformatics Division
> >>>>> Walter and Eliza Hall Institute of Medical Research
> >>>>> 1G Royal Parade, Parkville, Vic 3052, Australia.
> >>>>> dmccarthy@wehi.edu.au
> >>>>> http://www.wehi.edu.au
> >>>>
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