Different outputs of RTSI scores and metabolite abundances using the same input file

Microbial community profiling MelonnPan
1

Hello!! I am running melonnpan predict using the melonnpan.test.data.txt file provided in the tutorial. However, I am getting different outputs of RTSI and relative abundances of the metabolites when I repeat the process using the same input file.
What might be the reason for this?
I have attached the RTSI scores obtained from different melonnpan predict runs using the same input file.
2nd output.txt (1.3 KB)
1st output.txt (1.3 KB)

2

Hi @paramartha_banerjee - this has now been resolved. Can you try again after re-installing the latest version of the software from GitHub?

Many thanks,
Himel

3

Thanks!! @himel.mallick I am now getting the same output when I repeat the melonnpan workflow using the melonnpan.test.data.txt file provided in the tutorial.
However, it is different from the output provided in the tutorial.
RTSI scores provided in tutorial

RTSI scores obtained by me
RTSI scores obtained by me326×682 12.6 KB

I have attached a screenshot of the RTSI scores provided in the tutorial and the one obtained by me.

4

Hi @paramartha_banerjee - great catch. The tutorial is based on the previous version of the software. I have just updated it to reflect the current version of the software.

Also, since I did not explain the changes in this version before, I am adding a bit more detail for any future references on this topic.

  • The original version of the software back in 2019 used rank-based inverse normal (RIN) transformation of the metagenomic gene data and used the R package GenABEL to perform this operation.

  • In the subsequent versions, GenABEL was removed from CRAN but we forced users to install an older version of the software using devtools.

  • Later in 2022, we found a different implementation of the RIN transformation as a standalone function but it used a different mechanism to break the ties than the original GenABEL function.

  • In particular, the RIN transformation must decide on how to handle ties when there are zeroes in the data which is almost always the case with metagenomic gene abundances.

  • We initially randomly assigned unique ranks which explains the run-to-run variations in the RTSI scores but in the latest version I turned the random assignment off to alleviate this issue.

In summary, with the current RIN transformation, the RTSI scores will still be a bit different than the original MelonnPan algorithm but it should overall be very similar with all other MelonnPan steps remaining the same.

I hope this clarifies the discrepancy between different versions,
Himel

5

Thank you so much!! @himel.mallick