centralityPathPlot: Structural measures of vertices view in metabolic pathways
In BioNetStat: Biological Network Analysis

Description Usage Arguments Details Value References Examples

Vertices centralities or clustering coefficient view in KEGG metabolic pathways. centralityPathPlot and pathplot are functions based on pathview function of Pathview package. Pathview is a tool set for pathway based data integration and visualization. It maps and renders user data on relevant pathway graphs. All users need is to supply their gene or compound data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. Pathview generates both native KEGG view and Graphviz views for pathways. keggview.native and keggview.graph are the two viewer functions, and pathview is the main function providing a unified interface to downloader, parser, mapper and viewer functions.

centralityPathPlot(
  gene.data = NULL,
  cpd.data = NULL,
  threshold = NULL,
  thr.value = 0.05,
  species,
  pathway.id,
  kegg.native = TRUE,
  file.name = "path",
  limit = list(gene = NULL, cdp = NULL),
  bins = list(gene = 15, cpd = 15),
  both.dirs = list(gene = FALSE, cpd = FALSE),
  mid = list(gene = "white", cpd = "white"),
  high = list(gene = "red", cpd = "red")
)

`gene.data`	an output dataframe from diffNetAnalysis function. Data frame structure has genes as rows and statistical test, Nominal p-value, Q-value (p-value FDR adjust for multiple tests)and networks measures, for each network, as columns. Row names should be gene IDs. Here gene ID is a generic concepts, including multiple types of gene, transcript and protein uniquely mappable to KEGG gene IDs. KEGG ortholog IDs are also treated as gene IDs as to handle metagenomic data. Check details for mappable ID types. Default gene.data=NULL. numeric, character, continuous
`cpd.data`	the same as gene.data, excpet named with IDs mappable to KEGG compound IDs. Over 20 types of IDs included in CHEMBL database can be used here. Check details for mappable ID types. Default cpd.data=NULL. Note that gene.data and cpd.data can't be NULL simultaneously.
`threshold`	a character indicating which column has to be used to filter which genes or coumponds will be drawn in metabolic map. The options are "pvalue" or "qvalue" to filter by Nominal p-value or Q-value (p-value FDR adjust for multiple tests), respectively. The default threshold=NULL, do not filter any row of data frame.
`thr.value`	a numeric value indicating the upper threshold value to filter data frame rows.
`species`	character, either the kegg code, scientific name or the common name of the target species. This applies to both pathway and gene.data or cpd.data. When KEGG ortholog pathway is considered, species="ko". Default species="hsa", it is equivalent to use either "Homo sapiens" (scientific name) or "human" (common name).
`pathway.id`	character vector, the KEGG pathway ID(s), usually 5 digit, may also include the 3 letter KEGG species code.
`kegg.native`	logical, whether to render pathway graph as native KEGG graph (.png) or using graphviz layout engine (.pdf). Default kegg.native=TRUE.
`file.name`	character, the suffix to be added after the pathway name as part of the output graph file. Sample names or column names of the gene.data or cpd.data are also added when there are multiple samples. Default out.suffix="pathview".
`limit`	a list of two numeric elements with "gene" and "cpd" as the names. This argument specifies the limit values for gene.data and cpd.data when converting them to pseudo colors. Each element of the list could be of length 1 or 2. Length 1 suggests discrete data or 1 directional (positive-valued) data, or the absolute limit for 2 directional data. Length 2 suggests 2 directional data. Default limit=list(gene=1, cpd=1).
`bins`	a list of two integer elements with "gene" and "cpd" as the names. This argument specifies the number of levels or bins for gene.data and cpd.data when converting them to pseudo colors. Default limit=list(gene=10, cpd=10).
`both.dirs`	a list of two logical elements with "gene" and "cpd" as the names. This argument specifies whether gene.data and cpd.data are 1 directional or 2 directional data when converting them to pseudo colors. Default limit=list(gene=TRUE, cpd=TRUE).
`mid, high`	each is a list of two colors with "gene" and "cpd" as the names. This argument specifies the color spectra to code gene.data and cpd.data. When data are 1 directional (TRUE value in both.dirs), only mid and high are used to specify the color spectra. Default spectra (low-mid-high) "green"-"gray"-"red" and "blue"-"gray"-"yellow" are used for gene.data and cpd.data respectively. The values for 'low, mid, high' can be given as color names ('red'), plot color index (2=red), and HTML-style RGB, ("\#FF0000"=red).

This function uses pathview to visualize the vertex structural measures in metabolic maps. Pathview maps and renders user data on relevant pathway graphs. Pathview is a stand alone program for pathway based data integration and visualization. It also seamlessly integrates with pathway and functional analysis tools for large-scale and fully automated analysis. Pathview provides strong support for data Integration. It works with: 1) essentially all types of biological data mappable to pathways, 2) over 10 types of gene or protein IDs, and 20 types of compound or metabolite IDs, 3) pathways for over 2000 species as well as KEGG orthology, 4) varoius data attributes and formats, i.e. continuous/discrete data, matrices/vectors, single/multiple samples etc. To see mappable external gene/protein IDs do: data(gene.idtype.list), to see mappable external compound related IDs do: data(rn.list); names(rn.list). Pathview generates both native KEGG view and Graphviz views for pathways. Currently only KEGG pathways are implemented. Hopefully, pathways from Reactome, NCI and other databases will be supported in the future.

From viersion 1.9.3, pathview can accept either a single pathway or multiple pathway ids. The result returned by pathview function is a named list corresponding to the input pathway ids. Each element (for each pathway itself is a named list, with 2 elements ("plot.data.gene", "plot.data.cpd"). Both elements are data.frame or NULL depends on the corresponding input data gene.data and cpd.data. These data.frames record the plot data for mapped gene or compound nodes: rows are mapped genes/compounds, columns are: kegg.names standard KEGG IDs/Names for mapped nodes. It's Entrez Gene ID or KEGG Compound Accessions. labels Node labels to be used when needed. all.mapped All molecule (gene or compound) IDs mapped to this node. type node type, currently 4 types are supported: "gene","enzyme", "compound" and "ortholog". x x coordinate in the original KEGG pathway graph. y y coordinate in the original KEGG pathway graph. width node width in the original KEGG pathway graph. height node height in the original KEGG pathway graph. other columns columns of the mapped gene/compound data and corresponding pseudo-color codes for individual vertex measures The results returned by keggview.native and codekeggview.graph are both a list of graph plotting parameters. These are not intended to be used externally.

This function is an adaptation of Luo, W. and Brouwer, C., Pathview: an R/Bioconductor package for pathway based data integration and visualization. Bioinformatics, 2013, 29(14): 1830-1831, doi: 10.1093/bioinformatics/btt285

set.seed(5)
expr <- as.data.frame(matrix(rnorm(120),40,30))
labels <- rep(0:3,10)
adjacencyMatrix1 <- adjacencyMatrix(method="spearman", association="pvalue", threshold="fdr",
 thr.value=0.05, weighted=FALSE)
vertexCentrality <- degreeCentralityVertexTest(expr, labels, adjacencyMatrix1,numPermutations=10)
vertexCentrality2<-cbind(c(4790, 4791, 4792, 4793, 84807, 4794, 4795, 64332, 595, 898, 23552,
 1017, 8099, 10263, 4609, 23077, 26292, 84073, 4610, 4613, 10408,  80177, 114897, 114898, 114899,
  114900, 114904, 114905, 390664, 338872),vertexCentrality)
centralityPathPlot(gene.data=vertexCentrality2, cpd.data=NULL, threshold="pvalue", thr.value=1,
 species="hsa" , pathway.id="05200", kegg.native=TRUE, file.name="path_example",
limit = list(gene = NULL, cdp = NULL), bins = list(gene = 15,cpd = 15), 
both.dirs= list(gene = FALSE,cpd = FALSE), mid =list(gene = "white", cpd = "white"),
high = list(gene = "red",cpd = "red"))