Hi @Lena_Lapidot - apologies that we have not documented this part of the functionality well in our current MaAsLin 2 tutorial. I hope the following is helpful when choosing the right combination of statistical model, normalization, and transformation.
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For statistical models, if your input is count, then you can use
NEGBINandZINB, whereas, for non-count input, you can useLMandCPLM. -
Apart from the statistical models, you need to pay close attention to whether the selected normalization and transformation options are valid with respect to the input requirement above.
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Among the normalization approaches implemented in MaAsLin 2,
TMMandCSSonly work on counts and they also return normalized counts unlikeTSSandCLR. Therefore, if your input is count, you can use the above two normalizations (i.e.,TMM,CSS,orNONE(in case the data is already normalized)) without a further transformation (i.e.transform = 'NONE'). -
Among the non-count models,
CPLMrequires the data to be positive. Therefore, any transformation that produces negative values will typically NOT work forCPLM. -
All the non-
LMmodels use an intrinsic log link transformation due to their close connection to GLMs and they are recommended to be run withtransform = 'NONE'. -
Apart from that,
LMis the only model that works on both positive and negative values (following normalization/transformation) and you have more wiggle room to vary the corresponding parameters which are typically limited for non-LMmodels.
I know it’s a lot of information but I hope this helps. Please let us know if you have any follow-up questions or if you encounter any issues with the alternative non-default models.
All the best,
Himel