PhONA | R Documentation |
This function takes in phyloseq object, association matix, p value matrix and create a combined OTU-OTU and OTU-Phenotype network. User can select model to define OTU-Phontype assocaition.
PhONA( physeqobj = physeq, cordata = sparcc.cor, pdata = sparcc.pval, model = c("lm", "lasso"), iters = 1, OTU_OTU_pvalue = 0.05, OTU_OTU_rvalue = 0.6, OTU_Phenotype_pvalue = 0.5, definePhenotype = "Marketable", defineTreatment = "Maxifort", PhenoNodecolor = "yellow", PhenoNodesize = 20, PhenoNodelabel = "Yield", nodesize = 10, Pheno2OTUedgecolor = "black", netlayout = layout.fruchterman.reingold )
physeqobj |
A phyloseq object which combined OTU count, taxonomy and metadata |
cordata |
A pairwise square matrix defining OTU-OTU association |
pdata |
A pairwise square matrix defining p-value for OTU-OTU association |
model |
Model to define association between OTUs and Phenotype. Option available are "lm", "lasso".
In lasso option, we are using lasso model to reduce the number of features/OTUs. OTUs important to phenotype prediction
were ranked using |
OTU_OTU_pvalue |
Pvalue for OTU-OTU association |
OTU_OTU_rvalue |
Level of OTU-OTU association |
OTU_Phenotype_pvalue |
Pvalue for OTU-Phenotype association |
definePhenotype |
Phenotype to be used. It is a column header from phenotype data |
defineTreatment |
Select the treatment. It is same as the treatment name that the phyloseq object represents |
PhenoNodecolor |
Select color for phenotype node |
PhenoNodesize |
Select node size for phenotype node |
nodesize |
Select size for nodes other than phenotype node |
Pheno2OTUedgecolor |
Select color of edge from OTU to phenotype node |
netlayout |
Select layout for the network graph. All the layout options from igraph can be used |
coloredby |
Select taxonomic group to be used for coloring node. Options: Kingdom,Phylum, Class, Order, Family, Genus, Species |
A PhONA representing OTU-OTU association as well as OTU-Phenotype association
Running linear model based PhONA,where OTU-Phenotype association is defined using linear model PhONA( physeqobj = phyobj, cordata = sparcc.cor, pdata = sparcc.pval, model = "lm", iters = 1, OTU_OTU_pvalue = 0.001, OTU_OTU_rvalue = 0.6, OTU_Phenotype_pvalue = 0.6, definePhenotype = "Marketable", defineTreatment = "Maxifort", coloredby = "Phylum", PhenoNodecolor = "yellow", PhenoNodesize = 20, PhenoNodelabel = "Yield", nodesize = 10, Pheno2OTUedgecolor = "black", netlayout = layout.fruchterman.reingold) Running lasso model based PhONA,where OTU-Phenotypeassociation is defined using lasso model and GLM PhONA( physeqobj = phyobj, cordata = sparcc.cor, pdata = sparcc.pval, model = "lasso", iters = 1, OTU_OTU_pvalue = 0.001, OTU_OTU_rvalue = 0.6, OTU_Phenotype_pvalue = 0.6, definePhenotype = "Marketable", defineTreatment = "Maxifort", coloredby = "Phylum", PhenoNodecolor = "yellow", PhenoNodesize = 20, PhenoNodelabel = "Yield", nodesize = 10, Pheno2OTUedgecolor = "black", netlayout = layout.fruchterman.reingold)
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