multiOutput: Analysis results for multiple pairs: visualization outputs
In matianzhou/CAMO: Perform congruent analysis among model organisms

Description Usage Arguments Value Examples

Analysis results for multiple pairs: visualization outputs including overall pathway clustering and output for each pathway The multiOutput is function to generate visualization outputs for multiple pairs: including overall pathway clustering outputs, model MDS plot, model clustering output, heatmap of gene posterior mean, kegg pathway topology for each pathway

multiOutput(mcmc.merge.list, dataset.names, select.pathway.list, ARS_pathway,
  output = c("clustPathway", "mdsModel", "clustModel", "genePM", "keggView"),
  hashtb = NULL, pathways = NULL, keggViewSelect = c(1, 2), optK = NULL,
  kegg_pathname = NULL, hs_gene_id = NULL)

`mcmc.merge.list:`	a list of merged MCMC output matrices.
`dataset.names:`	a vector of dataset names.
`select.pathway.list:`	a list of selected pathways (containing gene components).
`ARS_pathway:`	a list of two data frames: pathway specific ARS values and their permuted p-value (pathway on rows, column being ARS value or the p-values).
`output:`	five options: "clustPathway" (pathway clustering),"mdsModel"(model MDS plot),"clustModel" (model clustering output), "genePM" (generating heatmap of gene posterior mean),"keggView" (generating kegg pathway topology, human KEGG only). For details, please refer to manuscript. cannot be empty.
`hashtb:`	hash table for text mining.
`pathways:`	complete pathway names for text mining.
`keggViewSelect:`	which two datasets to view in KEGG topology.
`optK:`	Optimal number of clusters based on clustering diagnostic results. For "clustPathway" output only.
`kegg_pathname:`	KEGG pathway name list. For "keggView" only.
`hs_gene_id:`	Human sapiens gene id. For "keggView" only.

stored output in created folders.

## Not run: 
#mcmc.merge.list from the merge step
#select.pathway.list from the pathSelect step
#ARS_pathway from the multiARS step
data(hashtb) #include hashtb & pathways
dataset.names <- c("hb","hs","ht","mb","ms","mt")
library(KEGG.db)
kegg_pathname <- unlist(as.list(KEGGPATHID2NAME))
library("org.Hs.eg.db")
hs_gene_id <- unlist(mget(x=rownames(mcmc.merge.list[[1]]),
envir=org.Hs.egALIAS2EG))
multiOutput(mcmc.merge.list,dataset.names,select.pathway.list,
ARS_pathway, output=c("clustPathway","mdsModel","clustModel","genePM","keggView"),
hashtb=hashtb,pathways=pathways,keggViewSelect = c(1,4),optK=7)

## End(Not run)