fetchGrinnDiffCorrNetwork: Combine a grinn network queried from grinn internal database...

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

View source: R/fetchGrinnDiffCorrNetwork.R

Description

from the list of keywords and input omics data e.g. normalized expression data or metabolomics 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. 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. Compute a differential correlation network of input omics data from two conditions, see datX1, datX2, datY1, datY2. Correlation coefficients, pvalues and relation directions among entities in each condition 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. Then correlation coefficients are test for differential correlations using Fisher's z-test based on DiffCorr. The differential correlation network is created by function fetchDiffCorrNetwork.

3. Combine the grinn network to the correlation network.

Usage

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fetchGrinnDiffCorrNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX1, datX2, datY1, datY2, pDiff, method)

Arguments

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).

datX1

data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of one condition. Columns correspond to entities e.g. genes, metabolites, and rows to samples. Require 'nodetype' at the first row to indicate the type of entities in each column. See below for details.

datX2

data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of another condition. Use the same format as datX1.

datY1

data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of one condition. Use the same format as datX1. If there is only one type of dataset, it can be NULL. See below for details.

datY2

data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of another condition. Use the same format as datX1. If there is only one type of dataset, it can be NULL. See below for details.

pDiff

numerical value to define the maximum value of pvalues (pvalDiff), to include edges in the output.

method

string to define which correlation is to be used. It can be one of "pearson","kendall","spearman", see cor.

Details

To calculate the differential correlation network, require the input data from two conditions; 1 and 2. The input data are matrices in which rows are samples and columns are entities. For each condition, if datNormY is given, then the correlations between the columns of datNormX and the columns of datNormY are computed. Otherwise if datNormY is not given, the correlations of the columns of datNormX are computed. Then correlation coefficients are test for significant correlation pairs. The column names of the input data are required to use grinn ids. convertToGrinnID is provided for id conversion, see convertToGrinnID.

Value

list of nodes and edges. The list is with the following componens: edges and nodes. Output includes correlation coefficients, pvalues and relation directions of each conditions, and the pvalues (pvalDiff) after testing. Return empty list if found nothing

Author(s)

Kwanjeera W kwanich@ucdavis.edu

References

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

Fukushima A. (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene, 10;518(1):209-14.

See Also

cor, corPvalueStudent, link{fetchDiffCorrNetwork}, fetchGrinnNetwork, DiffCorr, http://js.cytoscape.org/

Examples

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# Create metabolite-gene network from the list of metabolites using grinn ids and combine the grinn network to a differential correlation network of metabolites
kw <- c('G160','G300','G371')
dummyX1 <- rbind(nodetype=rep("metabolite"),mtcars[1:16,])
colnames(dummyX1) <- c('G1.1','G27967','G371','G4.1',letters[1:7])
rownames(dummyX1)[-1] <- paste0(rep("normal_"),1:16)
dummyX2 <- rbind(nodetype=rep("metabolite"),mtcars[17:32,])
colnames(dummyX2) <- c('G1.1','G27967','G371','G4.1',letters[1:7])
rownames(dummyX2)[-1] <- paste0(rep("cancer_"),1:16)
result <- fetchGrinnDiffCorrNetwork(txtInput=kw, from="metabolite", to="gene", datX1=dummyX1, datX2=dummyX2, pDiff=0.05)
library(igraph)
plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
# Create metabolite-pathway network from the list of metabolites using grinn ids and combine the grinn network to a differential correlation network of metabolites and proteins
dummyX1 <- rbind(nodetype=rep("metabolite"),mtcars[1:16,1:5])
colnames(dummyX1) <- c('G1.1','G27967','G371','G4.1','G16962')
rownames(dummyX1)[-1] <- paste0(rep("normal_"),1:16)
dummyX2 <- rbind(nodetype=rep("metabolite"),mtcars[17:32,1:5])
colnames(dummyX2) <- c('G1.1','G27967','G371','G4.1','G16962')
rownames(dummyX2)[-1] <- paste0(rep("cancer_"),1:16)
dummyY1 <- rbind(nodetype=rep("protein"),mtcars[1:16,6:10])
colnames(dummyY1) <- c('P28845','P08235','Q08AG9','P80365','P15538')
rownames(dummyY1)[-1] <- paste0(rep("normal_"),1:16)
dummyY2 <- rbind(nodetype=rep("protein"),mtcars[17:32,6:10])
colnames(dummyY2) <- c('P28845','P08235','Q08AG9','P80365','P15538')
rownames(dummyY2)[-1] <- paste0(rep("cancer_"),1:16)
result <- fetchGrinnDiffCorrNetwork(txtInput=kw, from="metabolite", to="pathway", datX1=dummyX1, datX2=dummyX2, datY1=dummyY1, datY2=dummyY2, pDiff=0.05)

kwanjeeraw/grinn documentation built on May 20, 2019, 7:07 p.m.