ggloor / frontiers_2017 Goto Github PK

Dear Professor Gloor,

Thank you for your review and clarifying the statistical dimension of Amplicon analysis. Although I'm following through with most concepts, when it comes to the adaptation of the R code, I'm having some problems.

Simply running the R code with the sample data does not result in reproducible graphs. There are many errors that I've encountered throughout. I'm presuming that this is due to the updated arrangements of R and the affiliated packages.

I would love to implement the novel approaches mentioned in your review paper in my new research paper, however, I'm finding it quite challenging to implement since the code will not even work for your provided dataset, let alone my input that might be slightly different.

The ALDEx2 graphs seem to be working just fine and are reproducible when it comes to the Review Paper. However, problems arise when

Running f.t <- aldex.ttest(f.x, conds)
Doing PCA Plots.
Would very much appreciate if an updated R script could be implemented for the newest versions of R.

I've attached my RStudio console errors, objects, and graphs(https://drive.google.com/drive/folders/1Ugl3v9XP0p5AhdTgoSprcXIMbsOPDFzu?usp=sharing)
My versions are:

R 4.1.1
RStudio 1.4.1717
ALDEx2 1.24.0
car 3.0-11 carData 3.0-4
CoDaSeq 0.99.6
zCompositions 1.3.4
igraph 1.2.6
grDevices 4.1.1
propr 4.2.6
vegan 2.5-7
Best,
Erfan

In R_block_11, why the variable used in y axis is perb.rare@matrix, I would think if the expected rho value should be plotted rho.rare@matrix should be used instead.

Also, I was a bit concern about the great difference between the point estimator of rho and the expected value of rho, when inquiring correlation of the same OTUs used in R_block_10.. Please see attachment, while point estimators of rho reached proportionalities over 0.95 in many instances, the estimated values reached at maximum ~0.8. I was expecting differences but not so notable ones.

I also tied to investigate the range of correlations obtaining by every different approximation (see attached). As it is also observed on Figure 8, correlation coefficients from E(rho) and pearson tend to be smaller than those obtained by the point estimate (see attached the hist of the sd for the whole dataset), I also saw similar behavior when using in-house datasets. I cannot really formulate a reason for this observation, or even understand which should be consider the "gold standard".
dist_rho.pdf
correlations_prop.txt