# How to define the transformation / the normalization to use in Maaslin2

**URL:** <https://forum.biobakery.org/t/how-to-define-the-transformation-the-normalization-to-use-in-maaslin2/2122>\
**Category:** MaAsLin\
**Created:** [May 21, 2021, 3:54pm UTC](https://forum.biobakery.org/t/how-to-define-the-transformation-the-normalization-to-use-in-maaslin2/2122 "2021-05-21T15:54:35Z")\
**Posts on this page:** 1\
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**Author:** ![himel.mallick](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/himel.mallick/32/1757_2.png) [@himel.mallick](https://forum.biobakery.org/u/himel.mallick)\
**Post date:** [May 21, 2021, 5:46pm UTC](https://forum.biobakery.org/t/how-to-define-the-transformation-the-normalization-to-use-in-maaslin2/2122/2 "2021-05-21T17:46:37Z")

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Hi @NicolasB - although we did not include rarefaction in our own [evaluation](https://www.biorxiv.org/content/10.1101/2021.01.20.427420v1), a recent [preprint](https://www.biorxiv.org/content/10.1101/2021.05.10.443486v1) concluded that `MaAsLin 2 (particularly with rarefied data) could also be a reasonable choice for users looking for increased statistical power at the potential cost of more false positives`.

Coming back to your question, you are right that rarefied data can be considered `normalized` data and likewise, you don’t need additional normalization before statistical modeling.

As for alternative models/transformations, we usually do not recommend a particular combination over another as the choice is usually problem- and data-specific. Apart from trying out various transformations with the LM models, you can also consider other non-LM models without normalization/transformation and see if that supports your hypothesis.

Check out the following discussions for some more insights:

1. [Metaphlan3 analysis with Maaslin2](https://forum.biobakery.org/t/metaphlan3-analysis-with-maaslin2/1674)
2. [Choosing analysis method for maaslin2](https://forum.biobakery.org/t/choosing-analysis-method-for-maaslin2/2107)

Best,  
Himel

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