staticConsensusNet: Static analysis.

In pwOmics: Pathway-based data integration of omics data

Description

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. By default the same time points of both data sets are considered.

Usage

staticConsensusNet(data_omics, run_times = 3, updown = FALSE,
  tp_prot = NULL, tp_gene = NULL, phospho = TRUE)

Arguments

data_omics	OmicsData object.
run_times	integer specifying number of times to run SP Steiner tree algorithm to find minimal graph, default is 3.
updown	boolean value; TRUE in case up- and downregulation should be checked individually for intersection. Type of checking is defined with parameter 'phospho'.
tp_prot	integer specifying the time point that should be included into the static consensus net for the phosphoprotein data
tp_gene	integer specifying the time point that should be included into the static consensus net for the transcriptome data
phospho	boolean value; TRUE in case up- and downregulation should be checked based on provided downstream phosphoprotein influence from identifyPR function; FALSE in case up- and downregulation should be checked for without phosphoprotein database knowledge. Default is TRUE.

Value

list of igraph objects; length corresponds to number of overlapping time points from upstream and downstream analysis.

References

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

Examples

## Not run: 
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("userspec"))
data_omics = readPhosphodata(data_omics, 
phosphoreg = system.file("extdata", "phospho_reg_table.txt", 
package = "pwOmics.newupdown")) 
data_omics = readTFdata(data_omics, 
TF_target_path = system.file("extdata", "TF_targets.txt", 
package = "pwOmics.newupdown"))
data_omics_plus = readPWdata(data_omics,  
loadgenelists = system.file("extdata/Genelists", package = "pwOmics.newupdown")) 

## End(Not run)
## Not run: 
data_omics_plus = identifyPR(data_omics_plus)    
setwd(system.file("extdata/Genelists", package = "pwOmics.newupdown"))
data_omics = identifyPWs(data_omics_plus)
data_omics = identifyTFs(data_omics)
data_omics = identifyRsofTFs(data_omics, 
noTFs_inPW = 1, order_neighbors = 10)
data_omics = identifyPWTFTGs(data_omics)
statConsNet = staticConsensusNet(data_omics)

## End(Not run)

pwOmics documentation built on Nov. 8, 2020, 4:55 p.m.