extractGenes {BGcom} | R Documentation |
Function that returns the lists of genes in common using the two suggested rules and additional ones defined by the user
extractGenes(output.ratio,output.bay,gene.names,data,q=NULL)
output.ratio |
The output object from the Frequentist model (ratio function) |
output.bay |
The output object from the Bayesian model (baymod function) |
gene.names |
ID of the genes (e.g Affy ID) |
data |
Matrix of pvalues used for the analysis |
q |
additional thresholds in the form of a vector to select a list of genes in common. If it is NULL only the Rmax and rule2 are used to select the lists of genes in common |
To select a list of interesting features from the Bayesian model we suggest two decision rules in the paper: 1. the maximum of Median(R(q)) only for the subset of credibility intervals which do not include 1 2. the largest threshold q for which the ratio R(q) il bigger than 2
The first one is pointing out the strongest deviation from independence, whilst the second is the largest threshold where the number of genes called in common at least doubles the number of genes in common under independence. The user can always define an additional threshold q which he finds of interest and obtain the list of genes associated with it
The function returns an object of the class list. Each element is a matrix where the first column has the names of the genes while the second/third columns have the Pvalues from the two experiments:
max |
The list of genes in common selected on the basis of the threshold associated to Rmax |
rule2 |
The list of genes in common selected on the basis of the threshold associated to R2 |
table.q |
The list of genes in common selected on the basis of the additional thresholds selected by the user |
Marta Blangiardo
1. M.Blangiardo and S.Richardson Statistical tools for synthesizing lists of differentially expressed features in related experiments, Genome Biology, 8, R54
data = simulation(n=500,GammaA=1,GammaB=1,r1=0.5,r2=0.8,DEfirst=300,DEsecond=200,DEcommon=100) T<- ratio(data$Pval,interval=0.01,dir="D:/",name="CompData1Data2",pvalue=TRUE) BayesianModel<- baymod(data$Pval,repl=100,output.ratio=T,dir="D:/") gene.names = data$names gene.lists <- extractGenes(output.ratio=T,output.bay=BayesianModel,gene.names,data$Pval,q=NULL) ## The function is currently defined as function(output.ratio,output.bay,gene.names,data,q=NULL){ if(output.ratio$pvalue==FALSE){ data = 1 - data } lists = dim(data)[2] #Decision rules: #1) Maximum for CI not including 1 max.R = max(output.bay[round(round(output.bay[,1],2),1)>1,2]) threshold.max = output.ratio$q[output.bay[,2]==max.R] #Table temp = matrix(0,dim(data)[1],lists) for(i in 1:dim(data)[1]){ for(j in 1:lists){ if(data[i,j]<= threshold.max){temp[i,j]<-1} } } table.max <- data[apply(temp,1,sum)==lists,] names.max <- gene.names[apply(temp,1,sum)==lists] if(output.ratio$pvalue==FALSE){ table.max <- data.frame(Names=names.max,RankingStat = 1- table.max) } if(output.ratio$pvalue==TRUE){ table.max <- data.frame(Names=names.max,RankingStat = table.max) } if(length(output.ratio$q[output.bay[round(output.bay[,1],2)>1,2]>=2])>0){ #2) Rule 2 threshold.2 = max(output.ratio$q[round(round(output.bay[,2],2),3)>=2 & round(round(output.bay[,1],2),1)>1]) #Table temp = matrix(0,dim(data)[1],lists) for(i in 1:dim(data)[1]){ for(j in 1:lists){ if(data[i,j]<= threshold.2){temp[i,j]<-1} } } table.2 <- data[apply(temp,1,sum)==lists,] names.2 <- gene.names[apply(temp,1,sum)==lists] if(output.ratio$pvalue==FALSE){ table.2 <- data.frame(Names=names.2,RankingStat = 1-table.2) } if(output.ratio$pvalue==TRUE){ table.2 <- data.frame(Names=names.2,RankingStat = table.2) } if(is.null(q)) {return(list(max = table.max,rule2 = table.2))} if(!is.null(q)){ l = length(q) table.q = list() for(r in 1:l){ temp = matrix(0,dim(data)[1],lists) for(i in 1:dim(data)[1]){ for(j in 1:lists){ if(data[i,j]<= q[r]){temp[i,j]<-1} } } table.q[[r]] <- data[apply(temp,1,sum)==lists,] names.q <- gene.names[apply(temp,1,sum)==lists] if(output.ratio$pvalue==FALSE){ table.q[[r]] <- data.frame(Names=names.q,RankingStat = 1-table.q[[r]]) } if(output.ratio$pvalue==TRUE){ table.q[[r]] <- data.frame(Names=names.q,RankingStat = table.q[[r]]) } names(table.q)[[r]] <- paste("q=",q[r]) } } return(list(max = table.max,rule2 = table.2, User = table.q)) } if(length(output.ratio$q[output.bay[round(output.bay[,1],2)>1,2]>=2])==0){ if(is.null(q)) {return(list(max = table.max))} if(!is.null(q)){ l = length(q) table.q = list() for(r in 1:l){ temp = matrix(0,dim(data)[1],lists) for(i in 1:dim(data)[1]){ for(j in 1:lists){ if(data[i,j]<= q[r]){temp[i,j]<-1} } } table.q[[r]] <- data[apply(temp,1,sum)==lists,] names.q <- gene.names[apply(temp,1,sum)==lists] if(output.ratio$pvalue==FALSE){ table.q[[r]] <- data.frame(Names=names.q,RankingStat = 1-table.q[[r]]) } if(output.ratio$pvalue==TRUE){ table.q[[r]] <- data.frame(Names=names.q,RankingStat = table.q[[r]]) } names(table.q)[[r]] <- paste("q=",q[r]) } } return(list(max = table.max,User = table.q)) } }