LncPriCNet (version 1.0)

getTopDiseaseLncRNAs: Get the Disease Risk LncRNAs

Description

prioritize the disease candidate lncRNAs by integrated multi-omics information.

Usage

getTopDiseaseLncRNAs(pheSeed = NULL, genSeed = NULL, lncSeed = NULL, 
			candidates = NULL, showTop = 30, gamma = 0.7, x = 1/3, y = 1/3, 
			a = 1/3, b = 1/3, GNet, PNet, LNet, GLNet, PGNet, PLNet)

Arguments

pheSeed

A character vector, represents the disease users want to study. Users should input diseaseName.

genSeed

A character vector, The gene seeds are the known disease genes of corresponding phenotype. Users should input a gene seed vector.

lncSeed

A character vector, The lncRNA seeds are the known disease lncRNAs of corresponding phenotype. Users should input a lncRNA seed vector.

candidates

A character vector. Users should input lncRNA candidates, a lncRNA seed vector.

showTop

An integer. The number of top ranked candidate lncRNAs users want to show.

gamma

Restart probability in RWR method. A value ranges from 0 to 1. The default value is 0.7.

x

Jumping probability between gene network and phenotype network. A value ranges from 0 to 1. The default value is 1/3.

y

Jumping probability between gene network and lncRNA network. A value ranges from 0 to 1. The default value is 1/3.

a

a denote the importance of the gene network. A value ranges from 0 to 1. The default value is 1/3.

b

b denote the importance of the phenotype network. A value ranges from 0 to 1. The default value is 1/3.

GNet

An adjacency matrix of the gene network with colnames and rownames representing genes. The value in it ranges from 0 to 1.

PNet

An adjacency matrix of the phenotype network with colnames and rownames representing phenotypes. The value in it ranges from 0 to 1.

LNet

An adjacency matrix of the lncRNA network with colnames and rownames representing lncRNAs. The value in it ranges from 0 to 1.

GLNet

An adjacency matrix of the gene-lncRNA network with colnames representing lncRNAs and rownames representing genes. The value in it ranges from 0 to 1.

PGNet

An adjacency matrix of the phenotype-gene network with colnames representing genes and rownames representing phenotypes. The value in it ranges from 0 to 1.

PLNet

An adjacency matrix of the phenotype-lncRNA network with colnames representing lncRNAs and rownames representing phenotypes. The value in it ranges from 0 to 1.

Value

A dataframe.

Details

The function getTopDiseaseLncRNAs could get the top ranked candidate lncRNAs by prioritization of the disease candidate lncRNAs using known disease seed nodes to perform random walk on multi-omics network. Note: If users want to use seeds we provided, pheSeed, genSeed and lncSeed can be obtained by getDiseaseInf.

See Also

DiseaseInfList, getCoExpGeneofLncs, getDiseaseInf.

Examples

## Not run: 
# #############    Prioritize candidate lncRNAs   ################ 
# 
# ## Here we use six fake matrix(networks).  
# data("GNetExample")
# data("GLNetExample")
# data("PNetExample")
# data("LNetExample")
# data("PGNetExample")
# data("PLNetExample")
# 
# ## Get three types seeds from DiseaseInfList we provided. 
# diseaseName<-"BREAST CANCER" ;
# data("DiseaseInfList")
# loci<-match(diseaseName,DiseaseInfList[["OMIMName"]]);
# pheSeedExample<-DiseaseInfList[loci,"OMIMId"];
# genSeedExample<-unlist(strsplit(as.character(DiseaseInfList[loci,"DGenes"]),";"));
# lncSeedExample<-unlist(strsplit(as.character(DiseaseInfList[loci,"DLncs"]),";"));
# PNodes<-colnames(PNetExample) ;
# GNodes<-colnames(GNetExample);
# LNodes<-colnames(LNetExample);
# pheSeedResult<-pheSeedExample;
# genSeedResult<-genSeedExample;
# lncSeedResult<-lncSeedExample;
# 
# ## Get candidates.
# candidateResult<-setdiff(LNodes,lncSeedResult);
# ##Run getTopDiseaseLncRNAs
# example<-getTopDiseaseLncRNAs(pheSeed=pheSeedResult,genSeed=genSeedResult,lncSeed=lncSeedResult,
#        candidates=candidateResult,showTop=30,GNet=GNetExample,PNet=PNetExample,LNet=LNetExample,
# 	   GLNet=GLNetExample,PGNet=PGNetExample,PLNet=PLNetExample);
# head(example)
# 
# 
# ## End(Not run)