[BioC] Tissue heterogeneity and TMM normalization
whuber at embl.de
Tue Sep 9 09:58:33 CEST 2014
the ‘most genes are not differentially expressed’ is a sufficient assumption 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 genes 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 estimation for RNA-sequencing data. Biostatistics, 13:523 (2012) — www.biostat.washington.edu/~dwitten/Papers/LiWittenJohnstoneTibs.pdf
They determine the normalisation factor so as to minimize the amount of differential expression.
(This is one instance of this idea I am aware of, it’s been put out for microarrays 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=0, where beta is the fold change and theta a positive number. This is, e.g., described in the DESeq2 vignette.
Il giorno 08 Sep 2014, alle ore 18:15, Ni Feng <fengni99 at gmail.com> ha scritto:
> 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
> 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
> 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 the
> other. Although TMM normalization is designed to deal with this problem,
> our data seems to need further normalization. Our within tissue comparisons
> 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
> Bioconductor mailing list
> Bioconductor at r-project.org
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