README.md

In wyguo/RLowPCor: Inference of co-expression gene network using relevance low order partial correlation from large scale expression data

RLowPC

R package to construct Relevance Low order Partial Correlation gene networks

Installation

To install the package, run the R codes

library(devtools)
install_github('wyguo/RLowPC')

Additional packages used

ppcor, corpcor, minet, netbenchmark, ggplot2, gridExtra, plyr, fdrtool

Examples of RLowPC R package

Example1: RLowPC function

```{r,cache=T,include=T,message='hide',warning=F,eval=F}

load library

library(RLowPC) library(corpcor)

load data

data(gnwdata) data.exp<-gnwdata$size100$ts1[1:63,] genes<-colnames(data.exp)[-c(1:3)]

load reference network

ref.edge<-gnwdata$size100$net1 ref.edge[,3]<-1 ref.adj<-edgelist2adjmatrix(edgelist = ref.edge,genes = genes,directed = F)

filter low expressed genes

data2anova<-data.frame(time=factor(paste0(data.exp$experiment,'_',data.exp$time)),data.exp[,-c(1:3)]) data.new<-anova2de(data.exp = data2anova,ncol.idx =1,model = 'expression~time',pval.cut = 0.01) data.exp<-data.new$de.ts genes<-data.new$de.gene ref.adj<-ref.adj[genes,genes]

infer correlation network

inf.cor<-abs(cor(data.exp)) diag(inf.cor)<-0

infer PC network

inf.pcor<-abs(pcor.shrink(data.exp)[1:length(genes),1:length(genes)]) diag(inf.pcor)<-0

infer LowPC

inf.LowPC.edge<-LowPC(data.exp,cutoff = 0.01,cutat='pval') if(is.null(inf.LowPC.edge$secondPC)){ inf.LowPC<-ref.adj*0 } else { inf.LowPC<-edgelist2adjmatrix(inf.LowPC.edge$secondPC[,1:3],genes = genes,directed = F) }

inf RLowPC

reduction.sapce<-na.omit(adjmatrix2edgelist(adjmatrix = inf.pcor,directed = F,order = T)[1:1000,]) inf.RLowPC.edge<-RLowPC(data.exp = data.exp,edgelist = reduction.sapce, method = 'pearson',pc.estimator = 'shrink') inf.RLowPC.edge$cor.weight<-abs(inf.RLowPC.edge$cor.weight) inf.RLowPC<-edgelist2adjmatrix(inf.RLowPC.edge[,c(1,2,4)],genes = genes,directed = T) inf.RLowPC<-abs(inf.RLowPC) inf.RLowPC<-pmax(inf.RLowPC,t(inf.RLowPC))

infer first order PC based on reduction sapce

first PC

inf.firstPC.edge<-firstPC(data.exp = data.exp,edgelist = reduction.sapce, method = 'pearson',controlist = NULL) inf.firstPC<-edgelist2adjmatrix(inf.firstPC.edge[,1:3],genes = genes,directed = F)

second PC

inf.secondPC.edge<-secondPC(data.exp = data.exp,edgelist = reduction.sapce, method = 'pearson',controlist = NULL) inf.secondPC<-edgelist2adjmatrix(inf.secondPC.edge[,1:3],genes = genes,directed = F)

Put the inferred networks into a list.

inf.list<-list(Cor=inf.cor,PC=inf.pcor,LowPC=inf.LowPC,firstPC=inf.firstPC, secondPC=inf.secondPC,RLowPC=inf.RLowPC) sapply(inf.list,dim) dim(ref.adj)

calculate confusion table

inf.table<-lapply(inf.list,function(x) table.evaluate(x,ref.adj = ref.adj))

cut the tables at top 1000 predictions

inf.table<-lapply(inf.table,function(x) table.cut(input.table = x)$table.output)

plot the figure

x11() plotAUC(table.methods = inf.table, roc.lim = c(0,1,0,1), pr.lim = c(0,1,0,0.4), top.precision.lim = c(0,700,0,0.4), fpr.precision.lim = c(0,1,0,0.4), color = c('blue','purple','green','lightblue','red','yellow'), lwd=1,fill = T)

####Example2: average.consensus function
```{r,cache=T,include=T,message='hide',warning=F}
 ##create two random networks
 library(RLowPC)
 set.seed(4)
 net1<-abs(matrix(rnorm(16),4,4))
 net1<-pmax(net1,t(net1))
 diag(net1)<-0
 set.seed(5)
 net2<-abs(matrix(rnorm(16),4,4))
 net2<-pmax(net2,t(net2))
 diag(net2)<-0
 dimnames(net1)<-dimnames(net2)<-list(letters[1:4],letters[1:4])
 ##put networks into list
 net.list<-list(net1=net1,net2=net2)
 inf.consensus<-average.consensus(adjmatrix.list = net.list,directed = F)
 adj2rankadj(net1)
 adj2rankadj(net2)
 inf.consensus

Example3: table.evaluate function

```{r,cache=T,include=T,message='hide',warning=F}

load library

library(RLowPC)

load data

data(gnwdata) data.exp<-gnwdata$size100$ts1[,-c(1:3)] genes<-colnames(data.exp) ref.edge<-gnwdata$size100$net1 ref.edge[,3]<-1 ref.adj<-edgelist2adjmatrix(ref.edge,genes) inf.cor<-abs(cor(data.exp)) diag(inf.cor)<-0 table.cor<-table.evaluate(inf.adj = inf.cor,ref.adj = ref.adj) head(table.cor)

####Example4: edgelist2adjmatrix function
```{r,cache=T,include=T,message='hide',warning=F}
##load data
library(RLowPC)
data(gnwdata)
data.exp<-gnwdata$size100$ts1[,-c(1:3)]
genes<-colnames(data.exp)
ref.edge<-gnwdata$size100$net1
ref.edge[,3]<-1
ref.adj<-edgelist2adjmatrix(ref.edge,genes,directed=F)

Example5: adjmatrix2edgelist function

```{r,cache=T,include=T,message='hide',warning=F}

load data

library(RLowPC) data(gnwdata) data.exp<-gnwdata$size100$ts1[,-c(1:3)] genes<-colnames(data.exp)

build correlation network

inf.cor<-abs(cor(data.exp)) diag(inf.cor)<-0

convert matrix to edge list

adjmatrix2edgelist(inf.cor)

```

wyguo/RLowPCor documentation built on May 4, 2019, 12:04 p.m.