Identify for each corresponding timepoint of the two datasets the consensus network. Protein intersection of the omics data and TF intersection are linked via SteinerTree algorithm applied on STRING protein-protein interaction database. The Steiner tree algorithm refers to the shortest path heuristic algorithm of [1,2]. Target genes of this consensus network are identified via the chosen TF-target gene database(s). Please note that the consensus graphs can be different as in the Steiner Tree algorithm the start terminal node is picked arbitrarily and there are always several shortest path distances.
staticConsensusNet(data_omics, run_times = 3)
integer specifying number of times to run SP Steiner tree algorithm to find minimal graph, default is 3.
list of igraph objects; length corresponds to number of overlapping time points from upstream and downstream analysis.
1. Path heuristic and Original path heuristic, Section 4.1.3 of the book "The Steiner tree Problem", Peter L. Hammer
2. "An approximate solution for the Steiner problem in graphs", H Takahashi, A Matsuyama
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#please run with whole database files (prepared according to vignette) data(OmicsExampleData) data_omics = readOmics(tp_prots = c(0.25, 1, 4, 8, 13, 18, 24), tp_genes = c(1, 4, 8, 13, 18, 24), OmicsExampleData, PWdatabase = c("biocarta", "kegg", "nci", "reactome"), TFtargetdatabase = c("chea", "pazar")) ## Not run: data_omics = readTFdata(data_omics) data_omics_plus = readPWdata(data_omics, loadgenelists = FALSE) data_omics = identifyPWs(data_omics_plus) data_omics = identifyTFs(data_omics) data_omics = enrichPWs(data_omics) data_omics = identifyRsofTFs(data_omics, only_enriched = FALSE, noTFs_inPW = 1, order_neighbors = 10) data_omics = identifyPWTFTGs(data_omics, only_enriched = FALSE) statConsNet = staticConsensusNet(data_omics) ## End(Not run)
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