biomarkerCategorization {AWFisher} | R Documentation

## biomarker categrorization

### Description

biomarker categrorization

### Usage

    biomarkerCategorization(studies, afunction, B = 10, DEindex = NULL,
      fdr = NULL, silence = FALSE)

### Arguments

`studies` | a list of K studies. Each element (kth study) of the list is another list consisting gene expression matrix and and label information.
`afunction` | A function for DE analysis. Options can be function_limma or function_edgeR. Default option is function_limma. However, use could define their own function. The input of afunction should be list(data, label) which is consistent with one element of the studies list/argument. The return of afunction should be list(pvalue=apvalue, effectSize=aeffectsize)
`B` | number of permutation should be used. B=1000 is suggested.
`DEindex` | If NULL, BH method will be applied to p-values and FDR 0.05 will be used. User could specify a logical vector as DEindex.
`fdr` | Default is 0.05. The co-membership matrix calculation will base on genes with this specified fdr.
`silence` | If TRUE, will print out the bootstrapping procedure.

### Details

biomarker categrorization via boostrap AW weight.

### Value

A list consisting of biomarker categrorization result.

`varibility` | Varibility index for all genes
`dissimilarity` | Dissimilarity matrix of genes of DEindex==TRUE
`DEindex` | DEindex for Dissimilarity

### Author(s)

Zhiguang Huo

### Examples

    N0 = 10
    G <- 1000
    GDEp <- 50
    GDEn <- 50
    K = 4

    studies <- NULL
    set.seed(15213)
    for(k in seq_len(K)){
        astudy <- matrix(rnorm(N0*2*G),nrow=G,ncol=N0*2)
        ControlLabel <- seq_len(N0)
        caseLabel <- (N0 + 1):(2*N0)

        astudy[1:GDEp,caseLabel] <- astudy[1:GDEp,caseLabel] + 2
        astudy[1:GDEp + GDEn,caseLabel] <- astudy[1:GDEp + GDEn,caseLabel] - 2

        alabel = c(rep(0,length(ControlLabel)),rep(1,length(caseLabel)))

        studies[[k]] <- list(data=astudy, label=alabel)
    }

    result <- biomarkerCategorization(studies,function_limma,B=100,DEindex=NULL)
    sum(result$DEindex)
    head(result$varibility)
    print(result$dissimilarity[1:4,1:4])

[Package AWFisher version 1.0.1 Index]