Does QIIME is supported by machine learning tools ?

Dear all,

I’m asking if QIIME is supported by machine learning tools . Otherwise, i generated microbial data using QIIME. i WANT to perform data integration gathering biochemical, genetic and microbiome informations of all individuals.

I have three questions :
1- Is this number of subjects = 50 or 55 is sufficient to perform this task ?
2- How can i estimate the sample size (statistical power calculation) of subjects to perform this job and do you have any suggestion of tool ?
3- Does QIIME is supported by machine learning tools which are able to achieve data integration and which one is recommended for this task ?

Remark : i think that 55 samples subdivided between controls and affected patients is not sufficient for power calculation to achieve this task but i want to get your point of view.

Thank you so much

Hi @M_F,
Statistics is a large and complex discipline, and many of your questions are unanswerable without context. I’ll give you some rough concepts, below. You’ll have to spend some time considering what methods you plan to use for this study, and may want to talk with a statistician about your individual research if you’re still wrestling with broad questions after doing your own research.

That depends…

  • on your discipline
  • your methods
  • your study

Different disciplines have different standards of significance. Talk with your colleagues, and check the relevant literature and see what others are using. Some methods (e.g. those that rely on multiple testing) can reduce the statistical power of your study significantly, so keep an eye out for that. Some studies require more or less statistical rigor to be of value to the academic community.

Like the first question, this is complex and situational. I’d again recommend talking to colleagues/mentors about your study design, reading the literature, and learning more about the specific methods you want to use. This will give you a better sense of what you might be able to expect from a given sample size.

QIIME 2 plugins make many different analytical methods available, some of which use machine-learning, and some of which don’t. I’m not sure what you mean by “data integration”, but the tutorials and docs are great if you want to start learning more.

Best,
Chris :parrot:

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Hello @ChrisKeefe

Thanks for the reply. Which are the analytical methods maked by QIIME2 using machine learning approach ? When can we use machine learning tools ? I mean by data integration data mining. What are the tools used to achieve data mining prospection ?

Thanks

There are a bunch of them, and I doubt there’s a comprehensive list anywhere. Many of the methods in q2-feature-classifier and q2-sample-classifier use ML.

That depends largely on which tool you want to use, and is too broad a question for me to answer adequately. Often, ML is used for clustering/classifying observations into groups, or detecting changepoints in data streams. Try googling that question, or spending some time with the ML literature, if you’d like to know more about ML generally.

Again, not quite sure what you mean here. To the best of my knowledge, there aren’t any QIIME 2 plugins for data mining at this time. Because QIIME 2 uses a plugin model, where almost everything it can do is defined in plugins instead of a centralized code base, you’d be absolutely welcome to write a QIIME 2 plugin that, for example, mines and imports data from public data repositories.

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