Comments (3)
You can generate the association matrix using SpiecEasi and use NetCoMi for network analysis and plotting as described here.
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Thank you for replying to my question. I'm working with 2 phyloseq objects that contain ITS data and Metabolite data, and I merged them into one object named "fun.bac.merge.renamed". I would like to ask you if I pass association matrix creation using SpiecEasi and use "measure = measure = "spieceasi" " instead. What is the different result that will be compared to the above tutorial?
net_genus <- netConstruct(fun.bac.merge.renamed,
measure = "spieceasi", #Association measures
measurePar = list(nlambda=10, lambda.min.ratio = 1e-2),
normMethod = "clr",
zeroMethod = "none",
sparsMethod = "none",
thresh = 0.3, #Defult
verbose = 2
)
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Passing the association matrix to netConstruct()
and setting measure to "spieceasi" won't work, because that would mean you are estimating associations from an association matrix, which doesn't make sense.
You have two options:
- Estimate the association matrix in advance with
SpiecEasi
and pass the matrix tonetConstruct()
as explained in the tutorial.
Here you have to setdataType
to "condDependence" (or just "correlation"; there's currently no difference). - Pass your phyloseq object to
netConstruct()
and set measure to "spieceasi" so that associations are estimated withinnetConstruct()
. In this case, thedataType
must be "counts".
The latter won't work if you want to do a cross-domain analysis, so I recommend following my tutorial, which covers exactly your use-case.
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