Hi Yifang
I have discussed this issue with Gordon and Mark R---briefly, this is not
something to worry about. We do not expect the library sizes of the
pseudocounts necessarily to be approximately equal when TMM normalization (or
another type of normalization factor) is applied. That comment in the User's
Guide dates back to the pre-TMM normalization days and is no longer relevant. I
will update the User's Guide for the next release to remove that confusing
statement. Sorry for the angst that note in the Guide has caused.
In answer to your specific questions:
1) Best way to check that the normalization has performed well is to look at
MA-plots (plotSmear in edgeR).
2) It does not matter that the normalized library size of the conditions
differs from the common.lib.size.
3) Yes, the results are still meaningful even if the library sizes of the
pseudocounts are different.
4) I suspect that the differences in library sizes of the pseudocounts are
caused by the same thing that causes differences in normalization factors: sets
of genes that are highly expressed in a given sample but not in others. For
some reassurance, you could look at:
colSums(d$pseudo.alt)*d$samples$norm.factors
These values should be quite similar (do not worry too much if they differ),
which shows the effect of the normalization factors on the effective library
size.
5) We routinely remove genes with very low expression levels in our analysis
and I would recommend it generally. In your experiment you have a minimum group
size of two, so I would recommend keeping genes that are expressed at a level
of 0.5 counts per million or greater in at least two samples. This is almost
exactly what you did in your earlier email. Removing genes with <=40 counts in
>= 6 samples is probably overdoing it.
Example code (in the next release there will be a convenience function to
calculate counts per million easily from a DGEList object):
cpm <- 1e06*t( t(d$counts) / d$samples$lib.size )
d <- d[ rowSums( cpm > 0.5 ) >= 2, ]
Mark provided me with a really nice reproducible example (thanks Mark!) that
illustrates how and why TMM normalization works and also why we do not expect
the library sizes of the pseudocounts to be equal after normalization factors
are applied. Try running the code below for yourself:
========================
library(edgeR)
f <- url("http://bioinf.wehi.edu.au/folders/tmm_rnaseq/LK_data.RData";)
load(f)
close(f)
D <- as.matrix(MA.subsetA$M)
g <- as.character(MA.subsetA$genes$EnsemblGeneID)
source("http://bioinf.wehi.edu.au/folders/tmm_rnaseq/functions.R";)
libSizes <- colSums(D)
foldDiff <- 3
pUp <- .8
pDifferential=0.1
xx <- generateDataset(commonTags=2e4, uniqueTags=c(3000,100),
foldDifference=foldDiff,
pUp=pUp, pDifferential=pDifferential, empiricalDist=D[,1],
libLimits=rep(1e6,2))
d <- DGEList(counts=xx$DATA, group=c(1,2))
d1 <- calcNormFactors(d)
par(mfrow=c(1,2))
plotSmear(d, ylim=c(-5,5))
plotSmear(d1, ylim=c(-5,5))
d <- estimateCommonDisp(d)
colSums(d$pseudo.alt)
d1 <- estimateCommonDisp(d1)
colSums(d1$pseudo.alt)
==========================
Hope that clarifies a few things for you.
Best wishes
Davis
P.S. In general it is good form not to re-post the same question again on the
list. We receive and read questions on the BioC mailing lists, but sometimes it
can be a few days before we can make time to respond.
On Jun 22, 2011, at 7:50 AM, yifang tan wrote:
>
> Hi Davis/Gordon:
> I posted my question here again hope you can see it.
> When I tried edgeR and met a problem with the number of pseudocounts
> for each library after normalization, which should come to close numbers.
> This have been addressed in edgeR
> several times that "the total counts in each libray of the pseudocounts
> agrees well with the common library size" (page 27 & 44 of the
> user's guide), but my result are quite different between treatments although
> for the replicates within treatment the pseudocounts are very similar. I
> can't get to the common.lib.size for each treatment after I tried several
> methods (TMM, RLE and quantile).
> 1) Did I miss anything during my run with edgeR? How can I assure the
> normalization went well?
>
> 2) Does the normalized library size of the conditions matter or NOT, if they
> are different from the common.lib.size?
>
>
> 3) Is the result still meaningful even the library sizes of pseudocounts are
> different?
>
>
> 4) What could probably be the reason(s) to cause the library sizes of
> pseudocounts so different?
>
>
> 5) Should I remove the smaller number reads as some other people do?
> After I removed the smaller numbers of counts (<=40 in >=6 out of
> 14 samples), the normalized library sizes become very similar.
>
>
> I can feel my lack of mathematics for the packages. I attach part of my code
> here.
>
> ---------------------------------------------------------------------
> d$samples$lib.size
> #"Zygote1", 21012147
> "Zygote2", 19924212
> "Octant1", 9660245
> "Octant2", 26002900
> "Globular1",17139388
> "Globular2", 7649319
> "Heart1", 16430105
> "Heart2", 20101956
> "Torpedo1", 12920266
> "Torpedo2", 6306742
> "Bent1", 44241095
> "Bent2", 20094409
> "Mature1", 15166090
> "Mature2", 23203758
>
> d$common.lib.size
> [1] 16554344.47
>
> colSums(d$pseudo.alt)
> # Zygote1 21523774.62
> Zygote2 21638415.63
> Octant1 14533481.82
> Octant2 12046955.46
> Globular1 18920316.62
> Globular2 18439528.30
> Heart1 11754608.30
> Heart2 12759230.11
> Torpedo1 11248245.52
> Torpedo2 11410667.92
> Bent1 16101723.65
> Bent2 17980670.24
> Mature1 26785396.02
> Mature2 27067289.80
> #
>
>> sessionInfo()
> R version 2.13.0 (2011-04-13)
> Platform: x86_64-pc-linux-gnu (64-bit)
> locale:
> [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C LC_TIME=en_CA.UTF-8
> LC_COLLATE=en_CA.UTF-8
> [5] LC_MONETARY=C LC_MESSAGES=en_CA.UTF-8
> LC_PAPER=en_CA.UTF-8 LC_NAME=C
> [9] LC_ADDRESS=C LC_TELEPHONE=C
> LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
> other attached packages:
> [1] ALL_1.4.7 Biobase_2.12.1 limma_3.8.2 edgeR_2.2.5
> loaded via a namespace (and not attached):
> [1] tools_2.13.0
> ---------------------------------------------------------------------
> [[elided Hotmail spam]]
>
>
> Yifang
>
>
> Yifang Tan
>
> [[alternative HTML version deleted]]
>
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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
[email protected]
http://www.wehi.edu.au
______________________________________________________________________
The information in this email is confidential and intend...{{dropped:8}}
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