I am using qiime2 extensively for my project and its amazing,
Now i need some clarification regarding supervised machine learning classifier and identifying core ASVs
i have 450 samples belongs to five different species, based on the alpha, beat diversity and taxonomy I wouldn’t able to predict the core ASVs of particular species because it is highly varied within species. Is their any method to identify core ASVs
Where the supervised machine learning classifier will solve this issue or i have to use another method.
Please suggest me.
Thank you for your valuable time

1 Like

Sure, you could give it a try — but finding important features with random forests would not necessarily indicate “core” ASVs… rather (if it has good accuracy) it would identify groups of ASVs that are particular to one species or another but might not be present in every single member of a single species. Still, it would give you a list of important species that you could then focus on to try and answer this question, e.g., build a heatmap of important features to see which groups they are most prevalent in and how prevalent.

Good luck!

[email protected] for the quick reply

This topic was automatically closed 31 days after the last reply. New replies are no longer allowed.