Re: Tissue heterogeneity and TMM normalization
Ni Feng <[email protected]> Tue, 9 Sep 2014 13:38:39 -0400
| Newsgroups | gmane.science.biology.informatics.conductor |
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| Message-ID | <CAFsq0YXwKA_VkGsA4BeLCsSSwCjSd3Pe=VSuGKZXr4hFYbDF2w@mail.gmail.com> |
--089e0163508ceedcd40502a567f0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Thank you Davide. I'll definitely give it a try and let you know if I bump into any questions. In addition, as a follow up to Wolfgang Huber's suggestion, I've attached a graph showing the tissue comparisons after normalizing based on CEG derived normalization factors. I will try these other normalization methods people have suggested until I feel confident about the skew. Best, Jenny On Tue, Sep 9, 2014 at 12:48 PM, davide risso <[email protected]> wrote: > 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 <[email protected]> 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 anoth= er > > 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 t= he > > normalization factors from them and see how well it works. Thanks for t= he > > suggestion! > > If this doesn't work, I'll try the quantile normalization. > > > > Thanks again for your help! > > Jenny > > > > ---------- Forwarded message ---------- > > From: Wolfgang Huber <[email protected]> > > Date: Tue, Sep 9, 2014 at 3:58 AM > > Subject: Re: [BioC] Tissue heterogeneity and TMM normalization > > To: Ni Feng <[email protected]> > > Cc: [email protected] > > > > > > Hi Ni > > > > the =E2=80=98most genes are not differentially expressed=E2=80=99 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 necessar= y > > 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) =E2=80= =94 > > 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=E2=80=99s 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=3D0, where beta is = the > > fold change and theta a positive number. This is, e.g., described in th= e > > DESeq2 vignette. > > > > Best wishes > > Wolfgang > > > > > > Il giorno 08 Sep 2014, alle ore 18:15, Ni Feng <[email protected]> ha > > scritto: > > > >> Dear all, > >> I have a general question about whether TMM normalization is appropria= te > >> for my data. I apologize for this long winded email. I am not a traine= d > >> bioinformatician and therefore have been struggling with some data > >> analysis. > >> > >> A colleague and I did an RNA seq experiment with 6 samples (each had R= NA > >> 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 yea= rs > >> ago. > >> > >> We used the Trinity package to do most of the transcriptome assembly a= nd > >> downstream analyses, such as leveraging EdgeR for differential > expression. > >> Naively I went on with all downstream analyses without verifying wheth= er > > 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 T= MM > >> 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 bla= ck > > on > >> top of all transcripts are CEG (Core Eukaryotic Genes) expression, whi= ch > > 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 towar= ds > >> 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 tha= n > > the > >> other. Although TMM normalization is designed to deal with this proble= m, > >> 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_pl= ot_0903.png>_______________________________________________ > >> Bioconductor mailing list > >> [email protected] > >> https://stat.ethz.ch/mailman/listinfo/bioconductor > >> Search the archives: > > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > > > > > > > -- > > Ni (Jenny) Ye Feng > > Ph.D. Candidate > > Bass Laboratory > > Cornell University > > Dept of Neurobiology and Behavior > > Ithaca, NY 14853 > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > [email protected] > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > 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-TVLZxgkOlNX2fBVCVOL8/[email protected] > --=20 Ni (Jenny) Ye Feng Ph.D. Candidate Bass Laboratory Cornell University Dept of Neurobiology and Behavior Ithaca, NY 14853 --089e0163508ceedcd40502a567f0 Content-Type: image/png; name="CEG_normalized_allseqs_090914.png" Content-Disposition: attachment; filename="CEG_normalized_allseqs_090914.png" Content-Transfer-Encoding: base64 X-Attachment-Id: f_hzviuwmt0 iVBORw0KGgoAAAANSUhEUgAAAlgAAAJYCAYAAAC+ZpjcAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF Vd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8 AUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWa 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