jchen1981 / microbiomedda

Diffferential distribution analysis of microbiome sequencing data

MicrobiomeDDA

1. mbzinb_0.2.tar.gz - a package implements differential distribution analysis between two groups based on zeroinflated negative binomial model. To install the package, download the package, open a terminal, change path to the directory that contains the downloaded .gz file, and then type 'R CMD INSTALL mbzinb_0.2.tar.gz'. The usage of the package is documented as a standard R package.
2. zeroinfl.plus.github.R - implements a fully generalized regression framework allowing the prevalance, abundance, and dispersion to depend on covariates. Existing packages do not allow covariate-dependent dispersion, which could lead to either reduced power or inflated type I error if the heterogenety is not taken into account. It has similar syntax as 'zeroinfl' from 'pscl' package.
3. zeroinfl.plus.daa.R - a wrapper function (ZISeq) to perform differential distribution analysis based on OTU table and meta dat.
4. zeroinfl.plus.example.R - Artificial simulations to illustrate the proposed method.
5. zeroinfl.plus.realdata.R - Using a real microbiome data to illustrate the proposed method (GMPR + winsorization + omnibus test).

microbiomedda's Issues

Results only return NAs

Hi all,

I tried your package and received all NA's for everything. However, other tests like DeSeq and ANCOM give me significant results (albeit small effect sizes). Is this test even more conservative than the other ones? That would likely explain my results.
My dataset consists of an experiment with two treatments and both alpha and beta diversity change significantly but with small effect sizes. Also there are lots of 0s in the otu table.
Thanks,
Sam

Not entirely clear what the recommended use is

Hi,

Thanks for creating this package. I'm currently seeking to apply it to my microbiome project. I'm a bit confused about the recommended use as there are functions within the separate R scripts as well as within the mbzinb package that seem to do the same/similar things (run the differential analysis test).

The R scripts I'm referring to are:

• https://github.com/jchen1981/MicrobiomeDDA/blob/master/zeroinfl.plus.github.R
• https://github.com/jchen1981/MicrobiomeDDA/blob/master/zeroinfl.plus.daa.R

And the package:

• https://github.com/jchen1981/MicrobiomeDDA/blob/master/mbzinb_0.2.tar.gz

I've installed the mbzinb package to run the differential abundance analysis for two group comparison using mbzinb.dataset() to set up the data, then mbzinb.test() to run the actual test. I'm interested in GMPR normalization and noticed that the ZISeq() method can also run a statistical test while GMPR normalizing the data beforehand. This function is available in an R script outside of the mbzinb package (https://github.com/jchen1981/MicrobiomeDDA/blob/master/zeroinfl.plus.daa.R).

So in summary, which workflow should I use? What are the differences ZISeq() and mbzinb.test()?

Thanks.

DA with IBD dataset using mbzinb.test

Hello Jun,
I used the example of mbznb.test to do differential analysis on the IBD dataset and only 5 OTUs were found to be significant (Padj<0.05), which was much less than the number described in the paper (48 significant OTUs were found with omnibus test). Could you please tell me is there any parameter should be tuned to get the same output as the paper? Below is the code:

library(mbzinb)
library(dplyr)
data(IBDdat)
mbzinb.test(IBDdat, group="ULCERATIVE_COLIT_OR_CROHNS_DIS") %>% mbzinb.results() %>% arrange(Padj) %>% View()

Thanks a lot for your time and help.
Best,
Min