Dear Peter,
So if I get your question right you want to know whether there is a difference in the log ratio estimated from the RNAseq and the proteomics data, right? The problem I think that in this case the error structure for the data sets will be completely different. Perhaps you could do an arcsine square root transform on the proportion of RNAseq counts mapping to a particular gene to more or less get normally distributed residuals, and provided that that would work you might be able to test for a method x treatment interaction effect using a mixed model for a given gene/protein, though you would probably have to use nlme then and specify a custom error structure to allow for the variances to be different depending on the method you used (RNAseq or diff proteomics). Alternatively, you could estimate the 95% confidence intervals on the treatment contrasts in your proteomics and RNAseq datasets using separated mixed models and lsmeans, and then check whether or not they overlap. But probably all this is more hassle than it's worth, given that such datasets usually show very little overlap...
Cheers,
Tom
From: Wang Peter [mailto:wng.peter@gmail.com]
Sent: 30 June 2013 05:53
To: Gordon K Smyth
Cc: Tom Wenseleers; Bioconductor mailing list
Subject: Re: [BioC] Statistical power of limma vs mixed model approach to analyze microarray and differentialproteomics/peptidomics experiments and Poisson/binomial mixed models for RNAseq data
that is a good question.
i have a similar question.
if i have differential transcriptome and proteomics data under two conditions, like
(a1,a2,a3) vs. (b1,b2,b3) and (A1,A2,A3) vs. (B1,B2,B3).
a and b means transcriptome RNA DATA.
A and B means proteomics DATA.
so can i use a mixed linear model to find differential genes between
(a1,a2,a3,A1,A2,A3) vs. (b1,b2,b3, B1,B2,B3).
On Sat, Jun 29, 2013 at 11:42 PM, Gordon K Smyth > wrote:
Dear Tom,
I must say that this is first time I have heard anyone suggest that a mixed linear model is easier or simpler than an ordinary linear model. Having used the lme4 package myself, the opposite seems true to me by a very long way.
Do you know that mixed models have been proposed and implemented several times for microarray data? For example the maanova package or the lmscFit() separate channel approach of the limma package. A recent paper by Naomi Altman and myself gives a comparison of some of the approaches:
http://www.biomedcentral.com/1471-2105/14/165
For some designs, a mixed model or separate approach can indeed recover more information than a classic log-ratio analysis. I don't fully follow the approach that you describe, but it sounds to have some similarities with the limma separate channel approach as well as with maanova.
limma can be generalized to RNA-seq, see
http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
limma does allow for a treatment of randomized block designs but, apart from this exception, it is certainly true that RNA-seq analysis methods do not allow random effect models. So there is a gap in the market if you are keen to fill it.
There isn't any data object class called RGLimma that I know of. Do you mean RGList?
Best wishes
Gordon
Date: Sat, 29 Jun 2013 02:49:53 +0000
From: Tom Wenseleers >
To: "bioconductor@r-project.org" >
Subject: [BioC] Statistical power of limma vs mixed model approach to
analyze microarray and differential proteomics/peptidomics experiments
and Poisson/binomial mixed models for RNAseq data
Dear all,
I was just analyzing some data from a differential (dual label LC/MS) single factor peptidomics experiment using both a per-feature mixed model approach (using lme4's lmer function, using a model of the form intensity/abundance ~ (1|run)+label+treatment, and testing significance and calculating least square means of the differential expression ratios for the different treatments using lmerTest and lsmeans, using a Satterthwaite df approximation) and an empirical Bayes standard deviation shrinkage method as implemented in limma (by putting my data in an RGLimma object). To my surprise, I am finding more statistically significantly differentially expressed features using the mixed model approach than using limma (independent of the types of normalizations I use in limma, and I did check that the sd vs abundance was flat). Before, I have also observed the same phenomenon in some microarray datasets.
I am wondering if this would point towards a mixed model approach having superior statistical power than limma, even for this very simple design. Could this be the case? I only did 8 replicate runs in this case (corresponding to arrays in 2-color microarray exps), so not terribly many... Could it be that limma has superior statistical power for the analysis of relatively small designs with few experimental replicates (where the SD shrinkage could be beneficial), but that for larger number of replicates and also for complex designs (in particular when there are multiple sources of dependencies in the data) a mixed model approach would work better?
Also, would a mixed model approach in general not be much easier to specify (just requiring the model formula to be specified, as opposed to all the contrasts, which becomes pretty tedious for complex designs) and statistically be more flexible and powerful for multifactorial experiments (e.g. easily allowing for treatment interaction effects to be tested, and also allowing for multiple crossed random factors, e.g. to take into account several dependencies in the data, e.g. owing to spatial correlations in the microarray slides/print tip effects, other random factors, e.g. due to the use of samples with multiple genetic backgrounds or the presence of repeated measures factors, etc). For repeated measures designs I would also think that mixed model fits obtained using nlme could be even more flexible, e.g. allowing for various custom error covariance structures (e.g. to take into account temporal autocorrelations, etc); in fact, even lmer supports unstructured covariance mode! ls (which would allow variances to be different in your different groups, which I think could be quite common). Model selection, on the other hand, would appear a little more tricky, but also feasible, e.g. by minimizing the AIC of a model that is fit over all features/genes combined (as in http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_hpmixed_sect035.htm#statug.hpmixed.hpmeg4b).
Finally, an important advantage that I would see of using this approach is that it would readily generalize to RNAseq data if one would use generalized mixed models (glmer's) with a Poisson error structure (correcting for the total nr of mapped reads, etc and other important covariates) (or, alternatively, using a binomial error structure, to analyze the proportion of reads mapping to a particular gene). Overdispersion in the data in this approach could readily be taken into account by including an observation-level random factor in the model, cf. https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015867.html. The advantage here again would be that complex designs, e.g. with multiple crossed random factors, could readily be taken into account (to my understanding, edgeR or DEseq do not allow for this, right?). Would anyone perhaps have any thoughts why not to go for such a generic approach?
Would there perhaps be any market for a package to analyze differential expression in limma RGLimma and ESet data (or CountDataSet or DGEList objects for RNAseq data) using the approach I outline above? If there is enough interest, I would be keen to develop it, as it would seem pretty straightforward to do...
Cheers,
Tom Wenseleers
_______________________________________________________________________________________
Prof. Tom Wenseleers
* Lab. of Socioecology and Social Evolution
Dept. of Biology
Zoological Institute
K.U.Leuven
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* tom.wenseleers@bio.kuleuven.be
http://bio.kuleuven.be/ento/wenseleers/twenseleers.htm
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