Susan-

Thank you for weighing in. I actually learned a good deal from your recently submitted paper, Waste Not, Want Not:Why rarefying microbiome data is inadmissible, available on Joey McMurdie’s website (for anyone else interested). Reading that manuscript is actually what turned me on to using DESeq2 for variance stabilization in the first place. I appreciate your group’s work in this area as often the techniques used in RNA-seq are not easily transferred to metagenomic studies.

I agree that my particular study design makes statistical analyses much more difficult, which we hope to rectify by sequencing further samples across multiple years.

Thank you for your insights.

Kristina
------------------------------------------------------------------
Kristina Fontanez, Postdoctoral Fellow
fontanez@mit.edu<mailto:fontanez@mit.edu>
Massachusetts Institute of Technology
Department of Civil and Environmental Engineering
48-120E
15 Vassar Street
Cambridge, MA 02139



On Jan 24, 2014, at 11:36 AM, Susan Holmes <susanatstat@gmail.com<mailto:susanatstat@gmail.com>> wrote:

Dear Kristina,
I don't quite know whether the BioConductor users group is the right forum for this interesting scientific
discussion, the problem with your data is that you do not have replicates and the varying library
sizes give a heteroscedasticity that reduces the power with which you could try to compare
the different abundances. All the BioC people can do is help you fix your code, not your experiment.

It's not because you fix the radio that the news will be good.

With Joey McMurdie, we are putting alot of work in making this passage to standard normalization
procedures more understandable for biologists and your input is valuable, however your experiment
seems to be have a design flaw for what you want to do.
I still believe that normalization the data will give a much more reasonable scale of comparison
than taking the raw data. Data transformations are always arbitrary, but the original measurements
scales are also. I think reading more thoroughly a chapter on the Delta method and variance stabilization
in a book like John Rice's Introduction to Mathematical Statistics and Data Analysis will enable to get a better handle on the transformation and variance stabilization issues which are often encountered in these settings.

Good Luck
Susan




Susan Holmes
Professor, Statistics and BioX
John Henry Samter University Fellow
in Undergraduate Education
Director, Mathematical and Computational Sciences
Stanford
http://www-stat.stanford.edu/~susan/



On Fri, Jan 24, 2014 at 5:12 PM, Kristina M Fontanez <fontanez@mit.edu<mailto:fontanez@mit.edu>> wrote:
Dear Bioconductors-

After previous discussions on this list, I had settled on using regularized log transformed counts in order to present relative abundance heatmaps/barcharts of my metagenomic data. As this transformation accounts for both differing library sizes and stabilizes the variance among counts, it seemed an appropriate choice.

However, now I am running into the issue that the transformed data does not make biological sense. For example, below is a small table with 12 samples and the OTU counts for one taxa which have been either rlog transformed (using the blind option to ignore experimental design) or represent the raw counts. This particular taxa is one of the most abundant in my dataset. As you can see, the rlog transformed counts are much more stable across samples than the raw counts. This is exactly the expected behavior of the rlog transformation in that the variance of counts among samples has been reduced and the library sizes are now much more similar.

The problem is that the results do not make biological sense. This particular taxa is a copiotroph and so grows to high abundance in the presence of short bursts of nutrients. That kind of burst of growth is expected in samples 1,3,5, and 7, however samples 9-12 represent seawater samples and so they are nutrient-poor (relatively). This taxa ought to be in low abundance in those samples, which is the case in the raw counts. However, the rlog transformation inflates their counts so that they are now similar to those found in all other samples. This result obscures what is likely the true abundance of this taxa in the water column by inflating it beyond what is biologically reasonable given the environment in which these samples were collected.

        sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8 sample9 sample10        sample11        sample12
OTU1 rlog trans  136,280         93,429          131,635         93,351          120,957         93,334          120,358         93,317          93,304          93,310          93,308          93,322
OTU1 raw         468,459         1,190   168,280         330     237,248         94      232,483         195     249     1,647   331     501

With expression data, it seems appropriate to assume that in a treatment vs control scenario, all genes are likely to be expressed at some level (if even a low level) in all samples. However, I do not think that holds true for the abundance of taxa, especially in a case like mine where the samples are not so much different treatments as different ways of collecting microbes. It very well may be that the abundance of a particular taxa drops to a few hundred cells in any given sample because it is simply outcompeted by other microbes - that is a distinctly plausible biological event. The variance stabilization appears to mute that effect thereby making it appear that all taxa appear in all samples at relatively similar abundances.

Given these results, is rlog transformation really an appropriate choice when comparing metagenomic data? The variance stabilization seems to drastically change the biological patterns in the dataset.

Thanks,
Kristina

------------------------------------------------------------------
Kristina Fontanez, Postdoctoral Fellow
fontanez@mit.edu<mailto:fontanez@mit.edu><mailto:fontanez@mit.edu<mailto:fontanez@mit.edu>>
Massachusetts Institute of Technology
Department of Civil and Environmental Engineering
48-120E
15 Vassar Street
Cambridge, MA 02139




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