fetchGrinnModuNetwork: Combine a grinn network queried from Grinn internal database...
In kwanjeeraw/grinn: graph database and R package for omics data integration

Description Usage Arguments Details Value Author(s) References See Also Examples

from the list of keywords, input omics data e.g. normalized expression data or metabolomics data, and phenotypic data it is a one step function to:

1. Build an integrated network (grinn network) by connecting these keywords to a specified node type, see fetchGrinnNetwork. The keywords can be any of these node types: metabolite, protein, gene and pathway. The Grinn internal database contains the networks of the following types that can be quried: metabolite-protein, metabolite-protein-gene, metabolite-pathway, protein-gene, protein-pathway and gene-pathway.

2. Identify correlation between the input omics data e.g. normalized gene expression data, and phenotypic data e.g. weight. The function wraps around important aspects of WGCNA including blockwiseModules, cor, corPvalueStudent, labeledHeatmap. These aspects automatically perform correlation network construction, module detection, and display module-phenotype correlations. A module or the combination of modules can be selected from the heatmap of module-phenotype correlations for including in the network output, see more details below.

3. Combine the grinn network to the network module.

1	fetchGrinnModuNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX, datPheno, sfPower, minModuleSize, threshold)

`txtInput`	list of keywords containing keyword ids e.g. txtInput = list('id1', 'id2'). The keyword ids are from the specified database, see `dbXref`. Default is grinn id e.g. G371.
`from`	string of start node. It can be one of "metabolite","protein","gene","pathway".
`to`	string of end node. It can be one of "metabolite","protein","gene","pathway".
`filterSource`	string or list of pathway databases. The argument is required, if `from` or `to = "pathway"`, see `from` and `to`. The argument value can be any of "SMPDB","KEGG","REACTOME" or combination of them e.g. list("KEGG","REACTOME").
`returnAs`	string of output type. Specify the type of the returned network. It can be one of "tab","json","cytoscape", default is "tab". "cytoscape" is the format used in Cytoscape.js
`dbXref`	string of database name. Specify the database name used for the txtInput ids, see `txtInput`. It can be one of "grinn","chebi","kegg","pubchem","inchi","hmdb","smpdb","reactome","uniprot","ensembl","entrezgene". Default is "grinn". If pubchem is used, it has to be pubchem SID (substance ID).
`datX`	data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities. Columns correspond to entities e.g. genes, metabolites, and rows to samples e.g. normals, tumors. Require 'nodetype' at the first row to indicate the type of entities in each column.
`datPheno`	data frame containing phenotypic data e.g. weight, age, insulin sensitivity. Columns correspond to phenotypes and rows to samples e.g. normals, tumors.
`sfPower`	numerical value of soft-thresholding power for correlation network construction. It is automatically estimated using `pickSoftThreshold`, or it can be defined by users.
`minModuleSize`	numerical value of minimum module size for module detection.
`threshold`	numerical value to define the minimum value of similarity threshold, from 0 to 1, to include edges in the output.

The function encapsulates several methods from WGCNA so that module-phenoty correlation analysis can be fasten. These methods include:

- pickSoftThreshold estimates soft-thresholding powers from scale free topology to build the correlation network.

- blockwiseModules automatically calculates a correlation network and detects modules. Modules are the areas of the network where nodes are densely connected based on their topological overlap measure, see WGCNA for more details. Each module is labeled by color. By using the color, a module or the combination of modules can be selected ("enter color to the terminal"), for including in the network output.

- Module-phenotype correlations and significances are calculated using WGCNA functions cor and corPvalueStudent. The correlation coefficients are continuous values between -1 (negative correlation) and 1 (positive correlation), with numbers close to 1 or -1, meaning very closely correlated.

- labeledHeatmap plots a heatmap of module-phenotype correlations. A row is a module and a column is a phynotype. Each cell presents the corresponding correlation and the pvalue inside parenthesis. Each cell is colored by correlation, red means positive and blue means negative correlation.

- exportNetworkToCytoscape exports a network for using in Cytoscape (http://cytoscape.org/). The selected module is exported as the network output in which an edge will be included if it similarity threshold above the cutoff, see threshold.

list of nodes and edges. The list is with the following componens: edges and nodes. Return empty list if found nothing

Kwanjeera W kwanich@ucdavis.edu

Langfelder P. and Horvath S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9:559

Dudoit S., Yang YH., Callow MJ. and Speed TP. (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, STATISTICA SINICA, 12:111

Langfelder P. and Horvath S. Tutorials for the WGCNA package http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html

cor, corPvalueStudent, pickSoftThreshold, blockwiseModules, labeledHeatmap, exportNetworkToCytoscape, fetchGrinnNetwork, http://js.cytoscape.org/

# Create metabolite-gene network from the list of metabolites using grinn ids and combine the grinn network to a correlation of metabolite module to phenotypic data
kw <- c('G160','G300','G371','G16414','G17191')
library(grinn)
data(dummy)
data(dummyPheno)
result <- fetchGrinnModuNetwork(txtInput=kw, from="metabolite", to="gene", datX=dummy, datPheno=dummyPheno, minModuleSize=5, threshold=0.2)
# enter module color(s) seperate by space:yellow brown purple
library(igraph)
plot(graph.data.frame(result$edges[,1:2], directed=FALSE))