[BioC] Tissue heterogeneity and TMM normalization

Tue Sep 9 18:48:47 CEST 2014

Hi Jenny,

you may also want to have a look at our new RUVSeq package. In
particular, you can use the RUVg function to estimate factors of
"unwanted variation" (UV) using the CEG genes as "negative controls."

This is not equivalent to estimate the TMM normalization factors on a
subset of genes (which doesn't work too well in our experience),
because our UV factors are included in the model with some parameters
(coefficients) that are then re-estimated for all the genes. Have a
look at the vignette of RUVSeq package for details and let me know if
you have questions.

Best,
davide

On Tue, Sep 9, 2014 at 7:30 AM, Ni Feng <fengni99 at gmail.com> wrote:
> Thank you Wolfgang!
> We are using fold change >4 and FDR corrected P value of  <0.001 as
> thresholds for calling differential expression, do you think this is
> stringent enough given our skew?
>
> It was hard for me to gauge just how bad the skew is and that was another
> thing I wanted to get an opinion on.
>
> Yesterday I took out lowly expressed transcripts (<0.1 FPKM in any sample),
> which gave me a small dispersion value akin to what Trinity uses as default
> (0.1), but using the normalization factors from this dataset did not
> improve the skew. Given what Ryan Thompson said earlier I guess this makes
> sense.
>
> I had only used CEGs to calculate the dispersion, but will try to get the
> normalization factors from them and see how well it works. Thanks for the
> suggestion!
> If this doesn't work, I'll try the quantile normalization.
>
> Thanks again for your help!
> Jenny
>
> ---------- Forwarded message ----------
> From: Wolfgang Huber <whuber at embl.de>
> Date: Tue, Sep 9, 2014 at 3:58 AM
> Subject: Re: [BioC] Tissue heterogeneity and TMM normalization
> To: Ni Feng <fengni99 at gmail.com>
> Cc: bioconductor at r-project.org
>
>
> Hi Ni
>
> 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.
>
> Best wishes
>         Wolfgang
>
>
> 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
>> 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
> 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
>>
> <CEG_FPKM_over_all_090814.png><SMV_SMH_density_log2(M).pdf><SMH_SMV_MA_plot_0903.png>_______________________________________________
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>
>
>
> --
> Ni (Jenny) Ye Feng
> Ph.D. Candidate
> Bass Laboratory
> Cornell University
> Dept of Neurobiology and Behavior
> Ithaca, NY 14853
>
>         [[alternative HTML version deleted]]
>
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-- 
Davide Risso, PhD
Post Doctoral Scholar
Department of Statistics
University of California, Berkeley
344 Li Ka Shing Center, #3370
Berkeley, CA 94720-3370
E-mail: davide.risso at berkeley.edu