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>
Massachusetts Institute of Technology
Department of Civil and Environmental Engineering
48-120E
15 Vassar Street
Cambridge, MA 02139




	[[alternative HTML version deleted]]

