Re: Tissue heterogeneity and TMM normalization

Hi Ni

the =91most genes are not differentially expressed=92 is a sufficient assum=
ption that one can use to prove that the estimated normalisation factor is =
close to the true one, under some model. It is not a necessary assumption, =
TMM or similar normalisations can still be useful beyond (e.g. if many gene=
s are d.e. but up and down are about balanced; etc.)

Did you try compouting the normalisation parameters from the CEG genes only=
 and then applying to all data?

An interesting idea was put forward by J. Li, D. M. Witten, I. M. Johnstone=
 and R. Tibshirani: Normalization, testing, and false discovery rate estima=
tion for RNA-sequencing data. Biostatistics, 13:523 (2012) =97 www.biostat.=
washington.edu/~dwitten/Papers/LiWittenJohnstoneTibs.pdf =

They determine the normalisation factor so as to minimize the amount of dif=
ferential expression.
(This is one instance of this idea I am aware of, it=92s been put out for m=
icroarrays before, apologies to anyone else who proposed this.)

Also, if I understood your plots correctly, the biases are relatively small=
 in amplitude. So you could leave them there, but apply a banded hypothesis=
 test (i.e. H0: |beta| < theta) rather than H0: beta=3D0, where beta is the=
 fold change and theta a positive number. This is, e.g., described in the D=
ESeq2 vignette. =


Best wishes
	Wolfgang


Il giorno 08 Sep 2014, alle ore 18:15, Ni Feng <[email protected]> ha scri=
tto:

> Dear all,
> I have a general question about whether TMM normalization is appropriate
> for my data. I apologize for this long winded email. I am not a trained
> bioinformatician and therefore have been struggling with some data
> analysis.
> =

> A colleague and I did an RNA seq experiment with 6 samples (each had RNA
> pooled from 6 individuals) and no biological replicates. The 6 samples
> included 2 tissue types collected at 3 different time points. I know that
> this is not an ideal experimental set-up, we did this experiment 3 years
> ago.
> =

> We used the Trinity package to do most of the transcriptome assembly and
> downstream analyses, such as leveraging EdgeR for differential expression.
> Naively I went on with all downstream analyses without verifying whether =
my
> data violated underlying assumptions of TMM normalization.
> =

> For example, we found ~30% of our transcripts showed differential
> expression between any 2 pairwise comparisons. Does this violate the TMM
> assumption that most genes are NOT differentially expressed?
> =

> Furthermore, we noticed that there is still a tissue bias after
> normalization. Attached is a scatterplot of TMM normalized values for each
> tissue (summed across 3 sample groups for each tissue). Plotted in black =
on
> top of all transcripts are CEG (Core Eukaryotic Genes) expression, which =
we
> believe should be good candidates for "house keeping" genes. Both CEGs and
> all genes show that at higher expression levels, there is a skew towards
> one tissue ("VMN"), whereas in the middle values, there is a skew towards
> the other tissue ("H").
> =

> I have also attached a density plot of the M values, and a MA plot to
> visualize the skew. These plots were generated from 1 pair of tissue
> comparisons ("SMH" vs "SMV).
> =

> These plots reflect the fact that one tissue is more heterogeneous than t=
he
> other. Although TMM normalization is designed to deal with this problem,
> our data seems to need further normalization. Our within tissue compariso=
ns
> are great and do not show this kind of skew. My questions are:
> =

> 1) does our data violate TMM normalization assumptions
> 2) do you have another normalization method to suggest for our data
> 3) should we just forget about tissue-comparisons
> =

> I have also played around with the suggestions about estimating a
> dispersion value based on the EdgeR user guide. Can discuss this further.
> =

> Thank you for your time and patience, and any advice is much appreciated.
> =

> -- =

> Ni (Jenny) Ye Feng
> Ph.D. Candidate
> Bass Laboratory
> Cornell University
> Dept of Neurobiology and Behavior
> Ithaca, NY 14853
> <CEG_FPKM_over_all_090814.png><SMV_SMH_density_log2(M).pdf><SMH_SMV_MA_pl=
ot_0903.png>_______________________________________________
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